Apparatus and method for analyzing and monitoring high frequency electrogram and electrocardiogram under various physiological conditions

Abstract

A method of analyzing a subject's cardiac condition, the method including measuring an ECG or electrogram signal, extracting a high frequency (HF) portion from a QRS portion of the ECG or electrogram signal, generating an HFQRS signal, calculating an HF value based on an analysis of the HFQRS signal, measuring at least one more physiological value associated with the subject, and analyzing the ECG or electrogram signal based on the HF value and the physiological value. Related apparatus and methods are also described.

Description

[0001] Related Applications

[0002] This application is a PCT patent application claiming priority to U.S. Provisional Patent Application No. 63 / 152,917, filed February 24, 2021. The contents of all the foregoing applications are incorporated herein by reference as if fully set forth herein. Technical Field

[0003] In some embodiments, the present invention relates to an apparatus and method for detecting myocardial ischemia by analyzing high-frequency components of electrocardiograms and / or electrorecordination, and more specifically, but not limited to, analysis taking into account physiological conditions such as heart rate and / or respiratory rate and / or tidal volume. Background Technology

[0004] The terms “electrocardiogram” and “ECG” are used in this specification and claims to refer to the electrical activity of the heart sensed using electrodes placed on the skin of a subject.

[0005] The term "electrogram" is used in this specification and claims to refer to cardiac electrical activity sensed using electrodes placed inside a subject's body.

[0006] Electrocardiography (ECG) and electrorecording are used to measure the frequency and regularity of the heartbeat, as well as the size and position of the ventricles, the presence of any damage to the heart, and the effects of drugs or devices used to regulate the heart.

[0007] ECG measurements are typically performed using two or more electrodes, which can be combined into several pairs. The output of each pair of electrodes is called a lead.

[0008] ECG is the most common method for measuring and diagnosing abnormalities in myocardial electrical activity and cardiac rhythms, especially abnormalities caused by damage to the conductive tissues that carry electrical signals, or abnormal rhythms caused by electrolyte imbalances. In the case of myocardial infarction (MI), ECG can identify whether the myocardium has been damaged and sometimes indicate the location of the damage, although not all areas of the heart are covered.

[0009] ECG equipment detects and amplifies minute electrical changes on a patient's skin caused by the depolarization and subsequent repolarization of the myocardium during each heartbeat. At rest, each myocardial cell carries a negative charge (relative to the extracellular charge), resulting in a negative potential on the cell membrane. The activation phase of the cell begins with depolarization, triggered by the influx and outflow of positive and negative ions, reducing the absolute value of the negative potential to zero. This activation initiates the mechanical processes of the myocardial cells, leading to their contraction. During each heartbeat cycle, a healthy heart produces an ordered depolarization wave, triggered by cells in the sinoatrial node and propagating through the atria, then through the atrioventricular node, and finally throughout the ventricles via a unique conduction system, thus achieving multi-site activation. This progression is detected as a waveform in the potential difference (or voltage) recorded between two electrodes placed on either side of the heart and can be displayed as a graph on a screen or paper. The resulting signal reflects the electrical activity of the heart, with different leads more clearly representing different parts of the myocardium.

[0010] A typical ECG recording of a cardiac cycle (heartbeat) consists of the P wave, QRS complex, T wave, and U wave, and is usually visible in 50% to 75% of ECG recordings. The baseline voltage of an ECG is called the isoelectric line. Typically, the isoelectric line is measured as the portion of the recording that follows the T wave and precedes the next P wave.

[0011] Standard ECG recordings typically filter out high-frequency components. In a process called low-pass filtering, frequency components above 100 Hz are usually filtered out (e.g., regulatory requirements are typically in the range of 0.05–100 Hz). In some commercial implementations, the low-pass filtering process uses a lower threshold, such as 75 Hz or even 50 Hz. Often, the noise level in an ECG recording makes it impossible to reliably separate, identify, or measure high-frequency components above 150 Hz (typically measured in microvolts) from a single ECG recording. To measure and process these high-frequency components, signal-to-noise ratio enhancement schemes, such as filtering and averaging, are usually required.

[0012] Other background technologies include:

[0013] U.S. Patent No. 8,626,275 to Amit et al., entitled “Apparatus and method for detecting myocardial ischemia using high-frequency component analysis of electrocardiogram”;

[0014] U.S. Patent No. 8,538,510 to Toledo et al., entitled "Apparatus and Method for Identifying Myocardial Ischemia Using High-Frequency QRS Potential Analysis"; and

[0015] A paper titled "Deriving Respiratory Signals from Multilead Electrocardiograms" by George B. Moody, Roger G. Mark, Andrea Zoccola, and Sara Mantero, published in the journal *Computational Cardiology*, Vol. 12, pp. 113-116, Washington, D.C., describes a signal processing technique for extracting respiratory waveforms from a standard ECG, which allows for the detection of respiratory effort.

[0016] All disclosures of the references mentioned above and throughout this specification, as well as all disclosures of the references mentioned in those references, are incorporated herein by reference. Summary of the Invention

[0017] In some embodiments, the present invention relates to an apparatus and method for detecting myocardial ischemia by analyzing high-frequency components of electrocardiograms and / or electrorecordination, and more specifically, but not limited to, analysis taking into account physiological conditions such as heart rate and / or respiratory rate and / or tidal volume.

[0018] According to one aspect of some embodiments of the present disclosure, a method for analyzing the cardiac condition of a subject is provided, the method comprising measuring an electrocardiogram (ECG) or electrorecordionation signal, extracting a high-frequency (HF) portion from the QRS portion of the ECG or electrorecordionation signal, generating a high-frequency QRS (HFQRS) signal, calculating an HF value based on the analysis of the HFQRS signal, measuring at least one physiological value related to the subject, and analyzing the ECG or electrorecordionation signal based on the HF value and the physiological value.

[0019] According to some embodiments of this disclosure, calculating the HF value based on the analysis of the HFQRS signal includes calculating the HFQRS signal value.

[0020] According to some embodiments of this disclosure, HF values ​​under multiple different physiological states are recorded.

[0021] According to some embodiments of this disclosure, the HF value includes the root mean square (RMS) of the HF signal.

[0022] According to some embodiments of this disclosure, the physiological value is heart rate.

[0023] According to some embodiments of this disclosure, calculating the HF value includes calculating a first HF value and calculating a second HF value, and measuring at least one physiological value includes measuring a first physiological value related to the time of measuring the first HF value and measuring a second physiological value related to the time of measuring the second HF value.

[0024] According to some embodiments of this disclosure, the analysis includes generating a scatter plot of multiple points, each point being defined by a first value of HF and a second value of physiological value.

[0025] According to some embodiments of this disclosure, the analysis includes calculating the slope of a regression line passing through multiple points in a scatter plot.

[0026] According to some embodiments of this disclosure, the analysis includes determining the likelihood of ischemia based on the slope.

[0027] According to some embodiments of this disclosure, the analysis includes conclusions about the likelihood of ischemia based on non-positive slope values.

[0028] According to some embodiments of this disclosure, analysis is performed at multiple points, including points captured at multiple different heart rates.

[0029] According to some embodiments of this disclosure, the analysis includes calculating calculated values ​​based on HF values ​​and physiological values.

[0030] According to some embodiments of this disclosure, the calculated value is the ratio of the currently measured HFQRS signal RMS to the resting HFQRS signal RMS measured in a resting state over a time period, and the physiological value is the ratio of the currently measured heart rate (HR) to the resting HR measured in a resting state over that time period.

[0031] According to some embodiments of this disclosure, the calculated value is equal to:

[0032]

[0033] NHFRMS is the difference between the maximum value of the RMS of the HFQRS signal measured during the current time period and the minimum value of the RMS of the HFQRS signal measured in the resting state.

[0034] NHR is the difference between the current HR measured during this time period and the resting HR measured in the resting state.

[0035] max(HF) is the maximum HF value measured within this time period.

[0036] min(HF) is the minimum HF value measured within this time period.

[0037] current HR It is the average HR measured during this period, and

[0038] resting HR The mean HR is measured under resting conditions.

[0039] According to some embodiments of this disclosure, the physiological value is the normalized heart rate (NHR), where NHR is the ratio of the HR measured during a first time period to the resting HR measured during a second time period while at rest.

[0040] According to some embodiments of this disclosure, the calculated value includes a value obtained by dividing a first value of the measured HF value by a second value of the HF value at rest.

[0041] According to some embodiments of this disclosure, resting HR The value is retrieved from storage for the test subject.

[0042] According to some embodiments of this disclosure, resting HR The value is retrieved from storage for a class of test subjects associated with that test subject.

[0043] According to some embodiments of this disclosure, the physiological value is the measured respiratory rate.

[0044] According to some embodiments of this disclosure, physiological values ​​are values ​​related to respiratory depth.

[0045] According to some embodiments of this disclosure, the physiological value is the tidal volume of respiration.

[0046] According to some embodiments of this disclosure, the physiological value is the respiratory rate measured by analyzing ECG or electrophysiological signals.

[0047] According to some embodiments of this disclosure, the respiratory rate is calculated based on measuring the interval between R waves in a continuous QRS complex.

[0048] According to some embodiments of this disclosure, the HFQRS signal value includes a value obtained based on a measurement of the reduced amplitude zone (RAZ) in the HFQRS signal.

[0049] According to some embodiments of this disclosure, the HFQRS signal value includes the ratio of the interval length between two adjacent local maxima of the HFQRS signal envelope to the length of the QRS composite wave.

[0050] According to some embodiments of this disclosure, the HFQRS signal value includes the ratio of the basin area of ​​the RAZ to the area of ​​the HFQRS signal envelope.

[0051] According to some embodiments of this disclosure, the HFQRS signal value is the ratio of the HFQRS signal value measured in a first time period to the resting HFQRS signal value measured in a resting state in a second time period; and the physiological value is the ratio of the HR measured in the first time period to the resting HR measured in a resting state in the second time period.

[0052] According to one aspect of some embodiments of the present disclosure, a system for analyzing ECG or electrocardiogram signals is provided. The apparatus includes an input for the ECG or electrocardiogram signals, a high-frequency (HF) signal extractor that extracts the HF portion from the ECG or electrocardiogram signals, and a processor configured to calculate an HF (HF component of the ECG or electrocardiogram signal) value based on the analyzed HF portion, measure at least one physiological value associated with a subject, and analyze the ECG or electrocardiogram signals based on the HF value and the physiological value.

[0053] According to some embodiments of this disclosure, the HF value includes the HFQRS signal value obtained based on the HF component of the QRS complex in the ECG or electrogram signal.

[0054] According to some embodiments of this disclosure, the physiological value is heart rate.

[0055] According to some embodiments of this disclosure, the processor is configured to generate a scatter plot of multiple points, each point being defined by a first value of HF and a second value of heart rate.

[0056] According to some embodiments of this disclosure, the system is configured to display a scatter plot.

[0057] According to some embodiments of this disclosure, the processor is configured to calculate the slope of a regression line passing through multiple points in a scatter plot.

[0058] According to some embodiments of this disclosure, the processor is configured to generate a scatter plot based on multiple points including points captured at multiple different heart rates.

[0059] According to some embodiments of this disclosure, the system is configured to store HF values ​​and physiological values.

[0060] According to some embodiments of this disclosure, the processor is configured to calculate a value based on the HF value and the physiological value.

[0061] According to some embodiments of this disclosure, it also includes means for storing calculated values.

[0062] According to one aspect of some embodiments of the present disclosure, a method for performing a cardiac stress test is provided, the method comprising starting the cardiac stress test, measuring an ECG or electrogram signal, extracting a high-frequency (HF) portion from the ECG or electrogram signal to generate an HFQRS signal, calculating multiple HF values ​​at multiple different times based on analysis of the HFQRS signals at multiple different times, measuring multiple corresponding values ​​of at least one physiological parameter at multiple times corresponding to the multiple HF values, and stopping the cardiac stress test based on at least some of the multiple physiological parameter values ​​being different from each other.

[0063] According to some embodiments of this disclosure, stopping a cardiac stress test includes stopping the cardiac stress test based on at least some of a plurality of physiological parameter values ​​being sufficiently different from each other.

[0064] According to some embodiments of this disclosure, physiological parameters include heart rate, and stopping the cardiac stress test includes stopping the cardiac stress test before the heart rate reaches the target heart rate of the Bruce test.

[0065] According to some embodiments of this disclosure, physiological parameters include heart rate, and stopping the cardiac stress test includes stopping the cardiac stress test before the heart rate reaches the target heart rate for a six-minute test.

[0066] According to some embodiments of this disclosure, the analysis of ECG or electrophysiological signals is also included based on multiple HF values ​​and corresponding multiple physiological values.

[0067] According to one aspect of some embodiments of the present disclosure, a method for analyzing the cardiac condition of a subject is provided, the method comprising measuring an ECG or electrophysiological signal, extracting a high-frequency (HF) component from the ECG or electrophysiological signal to generate an HFQRS signal, calculating multiple HF values ​​at multiple different times based on analysis of the HFQRS signals at multiple different times, measuring multiple corresponding values ​​of at least one physiological parameter at multiple times corresponding to the multiple HF values, and analyzing the ECG or electrophysiological signal based on the multiple HF values ​​and the corresponding multiple physiological values.

[0068] According to some embodiments of this disclosure, the analysis includes analysis based on at least some of a plurality of physiological parameter values ​​being sufficiently different from each other.

[0069] According to some embodiments of this disclosure, physiological parameters include heart rate, and the analysis includes performing the analysis before the heart rate reaches the target heart rate of the Bruce test.

[0070] According to some embodiments of this disclosure, physiological parameters include heart rate, and the analysis includes performing the analysis before the heart rate reaches the target heart rate for a six-minute test.

[0071] According to some embodiments of this disclosure, the analysis of ECG or electrophysiological signals is also based on multiple HF values ​​and corresponding multiple physiological values.

[0072] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. While similar or equivalent methods and materials may be used in the practice or testing of embodiments of this disclosure, exemplary methods and / or materials are described below. In case of conflict, the patent specification (including definitions) shall prevail. Furthermore, these materials, methods, and examples are illustrative only and are not necessarily restrictive.

[0073] As those skilled in the art will understand, some embodiments of this disclosure can be embodied as systems, methods, or computer program products. Therefore, some embodiments of this disclosure can take the form of entirely hardware embodiments, entirely software embodiments (including firmware, resident software, microcode, etc.), or embodiments combining software and hardware aspects, all of which are generally referred to herein as “circuit,” “module,” or “system.” Furthermore, some embodiments of this disclosure can take the form of computer program products embodied in one or more computer-readable media having computer-readable program code embodied thereon. Implementation of the methods and / or systems of some embodiments of this disclosure can involve manually, automatically, or a combination thereof performing and / or completing selected tasks. Furthermore, according to the actual instruments and apparatus of some embodiments of the methods and / or systems of this disclosure, several selected tasks can be implemented by hardware, software, or firmware and / or a combination thereof, for example, using an operating system.

[0074] For example, according to some embodiments of this disclosure, the hardware for performing a selected task can be implemented as a chip or circuit. As software, the selected task according to some embodiments of this disclosure can be implemented as a plurality of software instructions executed by a computer using any suitable operating system. In exemplary embodiments of this disclosure, one or more tasks according to some exemplary embodiments of the methods and / or systems described herein are performed by a data processor, such as a computing platform for executing multiple instructions. Optionally, the data processor includes volatile memory for storing instructions and / or data and / or non-volatile memory for storing instructions and / or data, such as a magnetic hard disk and / or removable media. Optionally, network connectivity is also provided. A display and / or a user input device such as a keyboard or mouse are also optionally provided.

[0075] Any combination of one or more computer-readable media may be used in some embodiments of this disclosure. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) will include the following: an electrical connection having one or more wires, a portable computer floppy disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the context of this document, a computer-readable storage medium may be any tangible medium that may contain or store programs for use by or in connection with an instruction execution system, apparatus, or device.

[0076] Computer-readable signal media may include, for example, propagated data signals containing computer-readable program code in baseband or as part of a carrier wave. Such propagated signals may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium that is not a computer-readable storage medium and may communicate, propagate, or transmit programs for use by or in connection with an instruction execution system, apparatus, or device.

[0077] The program code contained on a computer-readable medium and / or the data used therefrom may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, radio frequency, or any suitable combination thereof.

[0078] Computer program code used to perform operations of some embodiments of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" programming language or similar programming languages. The program code can be executed entirely on the user's computer, as part of a standalone software package on the user's computer, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., using an internet service provider via the internet).

[0079] Some embodiments of the present disclosure are described below with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / actions specified in the flowchart illustrations and / or block diagrams.

[0080] These computer program instructions may also be stored in a computer-readable medium that can instruct a computer, other programmable data processing apparatus or other device to operate in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instructions that implement the functions / actions specified in the flowcharts and / or block diagrams.

[0081] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus or other equipment to cause a series of operational steps to be performed on the computer, other programmable apparatus or other equipment to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide a process for implementing the functions / actions specified in the flowchart and / or block diagram.

[0082] Some of the methods described in this article are typically designed for use by computers and may be infeasible or impractical for human experts to perform purely manually. Human experts who wish to perform similar tasks manually (e.g., analyzing high-frequency ECGs) might use entirely different approaches, such as leveraging expert knowledge and / or the pattern recognition capabilities of the human brain, which would be far more efficient than manually completing the steps of the methods described in this article. Attached Figure Description

[0083] This document describes some embodiments of the present disclosure by way of example only, with reference to the accompanying drawings. Referring now specifically to the drawings, it is emphasized that the details shown are exemplary and for illustrative purposes in discussing embodiments of the present disclosure. In this regard, the description taken in conjunction with the drawings will enable those skilled in the art to clearly understand how embodiments of the present disclosure are implemented.

[0084] In the attached diagram:

[0085] Figure 1A This is a simplified illustration of typical locations for connecting acquisition electrodes in the prior art, including electrodes for acquiring high-frequency components of electrograms;

[0086] Figure 1B This is a simplified illustration of a subcutaneous device used in the prior art for analyzing electrophoresis.

[0087] Figure 2 This is a simplified illustration of an existing technique for determining respiratory rate based on the analysis of electrorecords;

[0088] Figure 3A This is a simplified illustration of an apparatus for detecting myocardial ischemia by analyzing high-frequency components of electrorecordination and / or electrocardiogram according to an exemplary embodiment of the present invention;

[0089] Figure 3B This is a simplified illustration of an apparatus for detecting myocardial ischemia by analyzing high-frequency components of electrorecordination and / or electrocardiogram, according to an exemplary embodiment of the present invention.

[0090] Figure 4A This is a simplified flowchart illustrating a method for analyzing the cardiac condition of a subject according to an exemplary embodiment of the present invention;

[0091] Figure 4B This is a simplified flowchart illustrating a method for performing a cardiac stress test according to an exemplary embodiment of the present invention;

[0092] Figure 4C This is a simplified flowchart illustrating a method for analyzing the cardiac condition of a subject according to an exemplary embodiment of the present invention;

[0093] Figure 5A This illustrates the potential advantages of incorporating the HR adjustment threshold into HFQRS signal analysis according to an example embodiment;

[0094] Figure 5B Table 432 shows a comparison of various heart rate (HR) and HFQRS signal values ​​with baseline heart rate values;

[0095] Figure 6 A graph showing the results of the acute phase of the experiment according to an example embodiment is shown;

[0096] Figure 7A Two graphs showing the significant responses of the ST and iHF QRS signals according to an example embodiment are presented;

[0097] Figure 7B and 7C Two diagrams depicting the electrical recordings and the HFQRS envelope during occupancy according to an example embodiment are shown;

[0098] Figure 8 A graph comparing the sensitivity of the iHFQRS signal and the ST offset during occupancy, according to an example embodiment, is shown.

[0099] Figure 9 Two graphs comparing ST offset and iHFQRS during two blockages are shown according to an example embodiment; and

[0100] Figures 10A-10CThis is a graph showing the ST and HFQRS signal values ​​collected during an induced ischemic attack, according to an example embodiment. Detailed Implementation

[0101] In some embodiments thereof, the present invention relates to an apparatus and method for detecting myocardial ischemia by analyzing high-frequency components of electrocardiograms and / or electrorecordination, and more specifically, but not limited to, analysis taking into account physiological conditions such as heart rate and / or respiratory rate.

[0102] Overview

[0103] Various methods and apparatuses for analyzing high-frequency (HF) ECG signals have been described, such as those described above in U.S. Patent No. 8,626,275 to Amit et al. and U.S. Patent No. 8,538,510 to Toledo et al., which optionally generate various parameters describing the HF ECG signal and optionally detect potential ischemic and / or additional cardiac conditions based on the values ​​of the parameters. Non-limiting example parameters include measuring the attenuation zone (RAZ) in the HF ECG envelope profile.

[0104] One aspect of some embodiments includes a method and apparatus for analyzing HF ECG or electrogram signals measured under different heart rate conditions.

[0105] In some embodiments, variations between HF ECG electrogram signals measured at different heart rate conditions may indicate the likelihood of ischemia.

[0106] In some embodiments, a change in the HF ECG electrogram signal measured under different heart rate conditions that exceeds a threshold may indicate the possibility of ischemia.

[0107] In some embodiments, two HFQRS signal values ​​acquired at two different heart rates are compared, and if the HFQRS signal value sampled at the higher heart rate is not higher than the HFQRS signal value sampled at the lower heart rate, the possibility of ischemia may be indicated.

[0108] In some embodiments, the subject may optionally begin a stress test, optionally a standard stress test, such as the Bruce test or a 6-minute test, and even if the stress test ends earlier than expected, for whatever reason, analysis of HF ECG or electrocardiogram measurements acquired at different heart rates may produce an indication of ischemia. As a non-limiting example, the subject may not reach their target heart rate, and an indication of the likelihood of ischemia may optionally be generated based on analysis of HF ECG or electrocardiogram measurements acquired at different heart rates.

[0109] In some embodiments, the subject has a device that collects HF ECG or electrocardiogram measurements over a period of time, not necessarily during a stress test. As some non-limiting examples, the device may be an implantable cardiac device (ICD), a cardiac resynchronization therapy (CRT) device, or a Holter monitor. In some embodiments, the device may optionally record HF ECG or electrocardiogram measurements and heart rate. Analyzing HF ECG or electrocardiogram measurements at different heart rate values ​​may optionally provide an indication of ischemia. In some embodiments, the device may optionally record HF ECG or electrocardiogram measurements and respiratory rate. Analyzing HF ECG or electrocardiogram measurements at different respiratory rate values ​​may optionally provide an indication of ischemia.

[0110] In some embodiments, a scatter plot of HFQRS signal values ​​versus heart rate values ​​is generated and optionally displayed. In some embodiments, analysis of the scatter plot may optionally allow a physician to indicate the likelihood of ischemia.

[0111] In some embodiments, the same system that measures HFQRS signal values ​​and heart rate values ​​stores pairs of HFQRS signal values ​​and heart rate values ​​sampled at the same time.

[0112] In some embodiments, the system optionally generates a scatter plot as described above based on stored values. In some embodiments, the system optionally uses this pair of values ​​to calculate a regression line. In some embodiments, the system optionally generates an indication of potential ischemia based on one or more parameters of the regression line. In some embodiments, the system optionally generates an indication of potential ischemia based on the slope value of the regression line. In some embodiments, the system optionally generates an indication of potential ischemia based on a positive value of the slope of the regression line.

[0113] In some embodiments, the system may optionally record the slope of a scatter plot over a specific time period, and may optionally continue monitoring the slope over an additional time period. In some embodiments, if the slope gradually changes over time, the system may optionally generate an indication that the slope is changing. In some embodiments, if the slope gradually decreases over time, and / or the value of the slope becomes more negative over time, the system may optionally generate an indication of the likelihood of ischemia.

[0114] One aspect of some embodiments includes a method and apparatus for analyzing HF ECG or electrogram signals measured under different physiological conditions, optionally generating various parameters describing the HF ECG or electrogram signals, and optionally detecting potential ischemic and / or additional cardiac conditions based on analysis of parameter values ​​related to measurements of physiological conditions.

[0115] Non-limiting examples of HF ECG or electrorecordography parameters include the measurement of the reduction zone (RAZ) within the contour of the HF ECG or electrorecordography envelope. In some embodiments, a first value of the parameter is interpreted as indicating potential ischemia when the physiological parameter (such as heart rate or respiratory rate) is low, and a second value of the HF ECG or electrorecordography parameter is interpreted as indicating potential ischemia when the physiological parameter value is high.

[0116] In some embodiments, a table that associates the values ​​of one or more physiological parameters, the values ​​of one or more HF signal analysis parameters, with potential cardiac conditions is used to detect and / or indicate the likelihood of a patient having such a cardiac condition. In some embodiments, a look-up table (LUT) that associates the values ​​of one or more physiological conditions, the values ​​of one or more HF signal analysis parameters, with potential cardiac conditions is used to detect and / or indicate the likelihood of a patient having such a cardiac condition.

[0117] In some embodiments, machine learning techniques such as neural networks may be optionally used to detect and / or indicate the likelihood of a patient having this cardiac condition, based on training on HF signal analysis parameter values ​​and physiological parameter values.

[0118] Some non-limiting examples of HF signal parameters include:

[0119] HFRMS -- Root mean square (or second root mean square) of the high-frequency components of the ECG signal;

[0120] RAZ region—RAZ is a notch or gap pattern in the amplitude envelope of an HF ECG or electrogram signal. The RAZ region is the duration of the RAZ in the time domain; and

[0121] RAZ ratio or RAZ percentage -- The ratio or percentage of the area of ​​the basin (the depression in the upper part of the envelope) to the area below the upper part of the envelope.

[0122] Some non-limiting examples of physiological parameters include:

[0123] Heart rate;

[0124] Respiratory rate;

[0125] Normalized respiratory rate is defined as the difference between the respiratory rate measured in the current time period and the resting respiratory rate measured in a resting state over a time period.

[0126] Tidal volume – the normal volume of air expelled between a normal inhalation and exhalation without additional effort; and

[0127] blood pressure.

[0128] It should be noted that physiological parameters can be obtained using the same sensors and processors used to acquire HF signal parameters. For example, heart rate can be obtained from an ECG signal, as is known in the art. Similarly, respiratory rate can be obtained from an ECG signal, as described by Moody et al. in their aforementioned article entitled “Extracting Respiratory Signals from a Multilead Electrocardiogram” by way of non-limiting example.

[0129] To better understand some embodiments of this disclosure, first refer to... Figure 1A and 1B The measurements shown are electrocardiograms and electrorecords, and as... Figure 2 The diagram shows the measurement of respiratory rate based on ECG signals.

[0130] Now for reference Figure 1A It is a simplified illustration of the location of the attachment acquisition electrodes in the typical prior art, including electrodes for acquiring the high-frequency components of the electrogram.

[0131] Figure 1A Three line drawings 101, 102, and 103 depict human subjects, and three sets of positions 105, 106, and 107 for placing electrodes used to collect high-frequency components of electrocardiograms.

[0132] Now for reference Figure 1B It is a simplified illustration of a subcutaneous device used in the prior art for analyzing electrograms. Figure 1B A simplified illustration is depicted of a subcutaneously implanted electronic box, also referred to as canister 120, having one or more electrode lines 121, 125, optionally electrically connecting canister 120 to one or more electrodes at one or more subcutaneous electrogram signal acquisition locations 122, 126.

[0133] In some embodiments, the tank 120 is electrically connected via electrode wire 121. Figure 1B The example electrode shown is located at a subcutaneous sampling position 122, which is optionally located above the rib 124, below the skin (not shown), and at the left edge of the subject's sternum 127.

[0134] In some embodiments, the tank 120 is electrically connected via electrode wire 125. Figure 1B The electrode shown is located at an example acquisition position 126, which is optionally located above the rib 124, below the skin (not shown), and at the right edge of the subject's sternum 127.

[0135] It is important to note that implantation sites 122, 126, and even the exterior of rib 124 may benefit from signals containing noise that is significantly lower than that at the skin surface acquisition site.

[0136] Now for reference Figure 2 It is a simplified illustration of existing techniques for determining respiratory rate based on electrocardiogram analysis.

[0137] Figure 2 Figure 200 shows respiratory-induced QRS complex amplitude modulation in a conventional ECG signal. Figure 200 has an X-axis 201 representing time and a Y-axis 202 representing signal amplitude. The upper trace 202 shows the ECG trace, and the lower trace 204 shows respiration measured by a pneumatic respiration transducer (PRT) placed around the subject's chest. The upper trace 202 and lower trace 204 show measurements for a 10-second duration. Figure 200 is taken from the aforementioned article entitled "Respiratory Signal Extraction from Multilead Electrocardiograms" published by Moody et al.

[0138] Figure 2 The article describes and teaches a method for analyzing QRS complex amplitudes in conventional ECG signals and respiratory movements.

[0139] Referring now to exemplary embodiments, and by way of non-limiting example, it can be seen that the amplitude of the QRS complex wave increases and decreases relative to respiration.

[0140] In some embodiments, the phase of respiration may optionally be measured by one or more motion sensors inside or connected to the patient. In some embodiments, the phase of respiration may optionally be measured by a band wrapped around the chest.

[0141] In some embodiments, the phase of breathing may optionally be measured by an audio sensor. This detection by an audio sensor can potentially operate in both implantable and external devices.

[0142] In some embodiments, the stage of respiration may optionally be measured by a spirometry device. The spirometry device may be located in a unit external to the subject's body. In some embodiments, the spirometry unit may send signals to the implanted unit to detect the stage of respiration.

[0143] In some embodiments, detecting circulatory movement of a subject's chest using motion sensors and analytics is optionally performed as part of detecting respiratory circulation.

[0144] Before explaining at least one embodiment of this disclosure in detail, it should be understood that this disclosure is not necessarily limited in its application to the details of the construction and deployment of the components and / or methods described in the following description and / or shown in the drawings and / or embodiments. The contents of this disclosure can be implemented or performed in various ways.

[0145] One aspect of some embodiments relates to the quantification of high-frequency signals from electrocardiograms within an externally connected monitoring device setup, such as an ECG device, a Holter monitor (a type of ambulatory electrocardiogram device), a wearable ECG device (an ECG system carried by a patient during routine daily activities), an "ECG watch" (a wristband ECG device), etc., as well as the quantification of high-frequency signals from electrocardiograms within implantable devices, subcutaneous electrodes, intracardiac electrodes, or intracoronary electrode setups. In some embodiments, the device for measuring the electrocardiogram or electrocardiogram may optionally be a recording device. In some embodiments, the device for measuring the electrocardiogram or electrocardiogram may optionally be a continuous measurement and / or continuous recording device.

[0146] In some embodiments, the implantable device may be a standalone sensing and analysis device or used in conjunction with another implantable device such as a pacemaker, ICD (implantable cardiac device), or CRT (cardiac resynchronization therapy) device. The implantable device may use implantable electrodes to sense electrograms. These electrodes may be located on the device itself or placed near the heart, within the heart, and / or in the endocardium and / or coronary arteries. The electrodes may measure electrogram signals between two electrodes, between the tips of two electrodes (i.e., distal and proximal tips), or between the electrodes and the device canister (the box or packaging containing the implantable device).

[0147] In some embodiments, high-frequency (HF) analysis of the QRS segment of an electrocardiogram or electrocardiogram is used to detect myocardial ischemia. In some embodiments, the HF analysis may generate one or more parameters based on the analysis of the high-frequency electrocardiogram (ECG) or electrogram (EGM) signal of the QRS segment, both of which will be referred to below as the HFQRS signal. The term "HFQRS signal" is used in this specification and claims to characterize the high-frequency portion of the electrocardiogram (ECG) or electrogram (EGM) signal of the QRS segment.

[0148] The following are some unrestricted example parameters:

[0149] The root mean square (RMS) of the HFQRS signal (HFRMS). The RMS of the HFQRS signal typically decreases during ischemia; and

[0150] The existence and quantization of the attenuation region (RAZ) in HFQRS signals.

[0151] High-frequency QRS (HFQRS) signals comprise the high-frequency components of the QRS complex portion of an ECG or electrophysiological record. HFQRS signals are typically small signals, usually measured in microvolts. HFQRS signals provide information about the depolarization phase of the cardiac cycle and exhibit higher accuracy in detecting stress-induced ischemia compared to changes in the repolarization phase. In some embodiments, HFQRS signals are typically measured and / or analyzed in the 150–250 Hz (Hertz) band, or even the 150–500 Hz band.

[0152] HFRMS is a term used to represent the high-frequency root mean square (or second root mean square) of a QRS complex wave, i.e., the high-frequency component of the QRS complex wave.

[0153] HFQRS envelope – is a term used to represent the time-domain envelope of an HFQRS composite wave. In some embodiments, the HFQRS envelope is determined for each lead. In some embodiments, the HFQRS envelope can be calculated using a low-pass filter with a Hilbert transform, as described in U.S. Patent 8,626,275 mentioned above.

[0154] Amplitude Reduction Zone (RAZ) is a term used to refer to a notch or gap pattern in the amplitude of the envelope of an HFQRS signal (in the time domain). RAZ is defined as the interval between two adjacent local maxima on the contour of the envelope. In some embodiments, as a non-limiting example, the absolute value of the local maxima is higher than three points in the preceding envelope and three points in the following envelope.

[0155] In some embodiments, the non-limiting RAZ quantization technique includes calculating the percentage of the area of ​​a basin (a depression in the upper contour of the envelope, or a depression in a higher peak of the envelope) relative to the area below the upper contour of the envelope.

[0156] In some embodiments, the reduction of RMS and the quantization of RAZ can be adjusted to absolute heart rate (HR) or normalized heart rate (NHR).

[0157] NHR can optionally be calculated as the ratio of current HR to mean resting HR, and can optionally be measured over a period of time at rest.

[0158] In some embodiments, adjustment can be optionally made by dividing the fraction representing the decrease in the RMS or RAZ value by the HR or NHR. In some embodiments, another function can be used instead of the division function to adjust the RMS or RAZ parameters for various HR values.

[0159] HFQRS is measured in various configurations, enabling the analysis of electrical activity in multiple vectors based on the position of leads relative to the equipment tank and the position between lead pairs or combinations.

[0160] For example, high-frequency signals used to detect myocardial ischemia have relatively low amplitudes compared to noise, even for intracardiac signals. In some embodiments, it is preferable to align and average several QRS intervals, optionally within a contiguous timeframe, which can therefore be assumed to be part of the patient's relatively constant physiological state. This averaging process may increase the signal-to-noise ratio (SNR), potentially allowing for more accurate analysis. In the above context, "contiguous timeframe" may optionally mean within 10, 20, 30, or at most 180 seconds. In some embodiments, the term "contiguous timeframe" may optionally refer to a time period in which the heart rate variation does not exceed X%, for example, not exceeding 5%, 10%, or 15%.

[0161] In some embodiments, the relationship between respiratory cycle and heart rate is known in the literature and is referred to as respiratory arrhythmia.

[0162] In some embodiments, heart rate is one of the factors controlling the behavior of the HFQRS signal, leading to coupling between HFQRS parameters and the respiratory cycle. In some embodiments, it may be beneficial to track the respiratory cycle and compare HFQRS results between measurements taken at similar points in the respiratory cycle (e.g., during maximal exhalation).

[0163] In some embodiments, to potentially improve power consumption, the HFQRS signal is measured only once per respiratory cycle, optionally at the same portion of the respiratory cycle, rather than at each heartbeat.

[0164] Using state-of-the-art sensors, it may be possible to measure blood pressure at different sites, such as in the pulmonary artery. This measurement could provide a more accurate description of a patient's physiological state and may be correlated with HFQRS parameters as well as other parameters for a given physiological condition. HFQRS parameters may vary naturally under normal conditions. Gating or triggering the signal based on the values ​​of physiological parameters such as blood pressure, pulse, and respiratory cycle can make HFQRS more sensitive and accurate, potentially increasing its indicative role in ischemic states and ischemic diseases.

[0165] In some embodiments, the simultaneous acquisition, registration, and analysis of HFQRS and such additional physiological parameters (blood pressure, pulse, etc.) may have two consequences. First, HFQRS can be analyzed and evaluated for multiple parameter states while recording a person's baseline. Second, HFQRS measurements or analyses may be performed only under certain, possibly predetermined, multiple parameter states.

[0166] In some embodiments, a baseline is determined for comparison to detect ischemia by way of non-limiting examples, optionally accomplished through continuous measurement and ongoing updates. Acute ischemia may be detected by reaching a specific HFQRS value or a normalized HFQRS value, or by a specific change compared to the baseline. Gradual deterioration of the patient's condition may also be detected by tracking parameters of the baseline itself.

[0167] Now for reference Figure 3A This is a simplified illustration of an apparatus for detecting myocardial ischemia by analyzing high-frequency components of electrorecordination and / or electrocardiogram according to an exemplary embodiment of the present invention.

[0168] Figure 3A A schematic diagram is shown of an HF electrogram and / or electrocardiogram analyzer 302, and one or more electrodes 306 for collecting electrogram signals.

[0169] In some embodiments, the analyzer 302 may be included in an implantable device.

[0170] In some embodiments, one or more electrodes may be attached to the body of the device, also known as the canister of the device.

[0171] In some embodiments, the analyzer 302 may optionally be connected to one or more electrodes used in pacemakers and / or ICDs and / or CRTs.

[0172] In some embodiments, the analyzer 302 may optionally be included in the same body or may be used as a pacemaker and / or ICD and / or CRT.

[0173] In some embodiments, electrode 306 may include one or more of an electrode for placement on the skin, an endocardial electrode, an intracardiac electrode, and / or an intracoronary electrode.

[0174] In some embodiments, a unipolar electrode and / or a bipolar electrode and / or a multipolar electrode or a combination thereof are used.

[0175] Now for reference Figure 3B This is a simplified illustration of an apparatus for detecting myocardial ischemia by analyzing high-frequency components of electrorecordination and / or electrocardiogram according to an exemplary embodiment of the present invention.

[0176] Figure 3B A schematic diagram is shown of an HF electrogram and / or electrocardiogram analyzer 312, one or more ECG and / or electrocardiogram electrodes 316 for collecting electrogram and / or electrocardiogram signals, and one or more electrodes 319 for collecting physiological signals other than ECG and / or electrocardiogram signals.

[0177] In some embodiments, the analyzer 312 may be included in an implantable device.

[0178] In some embodiments, one or more electrodes 316, 319 may be attached to the body of the device, also referred to as the tank of the device.

[0179] In some embodiments, the analyzer 312 may optionally be connected to one or more of the same electrodes 316, 319 used with pacemakers and / or ICDs and / or CRTs.

[0180] In some embodiments, the analyzer 312 may optionally be included in the same body or may be used as a pacemaker and / or ICD and / or CRT.

[0181] In some embodiments, electrodes 316, 319 may include one or more of an electrode for placement on the skin, an endocardial electrode, an intracardiac electrode, and / or an intracoronary electrode.

[0182] In some embodiments, a unipolar electrode and / or a bipolar electrode and / or a multipolar electrode or a combination thereof are used.

[0183] In some embodiments, one or more electrodes 319 for collecting physiological signals other than ECG and / or electrocardiogram may optionally be used to estimate blood pressure (e.g., using data from photoplethysmography (PPG)) and / or respiratory rate (e.g., using one or more piezoelectric sensors and / or one or more accelerometers and / or intranasal pressure sensors).

[0184] In some embodiments, the detection of ischemic attacks and / or the monitoring of ischemic conditions may optionally be based on changes in one or more parameters of high-frequency signals in a single and / or multiple leads of an ECG (electrocardiogram) or electrorecordination using one or more external electrodes or one or more intracardiac electrodes.

[0185] Some non-limiting examples of high-frequency signal parameters include:

[0186] The HR-adjusted HFQRS signal RMS decreases, which can optionally be achieved by dividing the fraction representing the RMS decrease by the absolute heart rate (HR) value.

[0187] The NHR-adjusted RMS drop can optionally be achieved by dividing the fraction representing the RMS drop by the normalized heart rate (NHR). A non-limiting example for calculating NHR is to calculate the ratio of the current HR measured in a first time period (typically 10 seconds, or within the range of 5, 10, 20, 30, 40, 50, and 60 seconds) to the mean resting HR measured in a second time period (the same or different) (typically 60 seconds). This can be described mathematically as follows (where HF is a high-frequency signal):

[0188]

[0189] The HR-adjusted RAZ measurement decreases, which can optionally be achieved by dividing the RAZ measurement by the absolute heart rate (HR).

[0190] The reduction in RAZ measurement due to NHR adjustment can be achieved, optionally by dividing the RAZ measurement by the normalized heart rate (NHR). A non-limiting example for calculating NHR is to calculate the ratio of the current HR measured over a typically 5-second time period to the mean resting HR measured over a typically 60-second time period.

[0191] The HR-adjusted RMS decline can optionally be adjusted using a function of HR and RMS decline, such that the HR-adjusted RMS decline produces similar values ​​for low RMS declines during low HR periods and high RMS declines during high HR periods. A typical, unrestricted example of such a function is RMSD / exp(HR), where RMSD is the RMS decline.

[0192] The NHR-adjusted RMS decrease can optionally be adjusted using a function of NHR and RMS decrease such that the NHR-adjusted RMS decrease produces similar values ​​for low RMS decreases during low NHR periods and high RMS decreases during high NHR periods. A typical, non-limiting example of such a function is RMSD / exp(NHR), where RMSD is the RMS decrease. NHR can optionally be calculated as the ratio of the current HR measured over a typically 5-second time period to the mean resting HR measured over a typically 60-second time period.

[0193] The HR-adjusted RAZ decrease can optionally be achieved using a function of HR and RAZ decrease, such that the HR-adjusted RAZ decrease produces similar values ​​for low RAZ decreases during low HR periods and high RAZ decreases during high HR periods. A typical, non-restrictive example of such a function is RAZD / exp(HR), where RAZD is the RAZ decrease.

[0194] The NHR-adjusted RAZ measurement decrease can optionally be achieved using a function of NHR and RAZ decrease, such that the NHR-adjusted RAZ decrease produces similar values ​​for low RAZ decreases during low NHR periods and high RAZ decreases during high NHR periods. A typical, non-limiting example of such a function is RAZD / exp(NHR), where RAZD is the RAZ decrease. NHR can optionally be calculated as the ratio of the current HR measured over a typically 5-second time period to the average resting HR measured over a typically 60-second time period.

[0195] In some embodiments, the detection of ischemic attacks and / or the monitoring of ischemic conditions may optionally be based on measuring changes in one or more parameters of the high-frequency signal as described above, and also on detecting different phases of the respiratory cycle. In some embodiments, the phases of the respiratory cycle may be characterized in the following ways:

[0196] a. Amplitude of the QRS composite wave (specifically, the amplitude of the R-wave in some embodiments).

[0197] b. Cardiac cycle

[0198] Some non-limiting examples of physiological parameters include:

[0199] Increased respiratory rate:

[0200] ((current breathingrate )-(resting breathingrate ))=((restingcurrent BR )-(resting BR ))

[0201] Now for reference Figure 4A This is a simplified flowchart illustrating a method for analyzing the heart condition of a subject according to an exemplary embodiment of the present invention.

[0202] Figure 4A The methods shown include:

[0203] Measure ECG or electrophysiological recording signals (402);

[0204] The high-frequency (HF) component is extracted from the ECG or electrogram signal to generate an HFQRS signal (404);

[0205] The HF value (406) was calculated based on the analysis of the HFQRS signal;

[0206] Measure at least one physiological value (408) related to the subject; and

[0207] The ECG or electrorecording signal is analyzed based on the HF value and the physiological value, and the adjusted HFQRS signal value (410) is calculated based on the HFQRS signal value and the physiological value.

[0208] In some embodiments, a significant change in the HF signal when there is a minor or no change in physiological condition indicates ischemia. As a non-limiting example, a relative decrease of 50% in HF RMS (e.g., from 8 uV to 4 uV) and a 10% increase in heart rate (e.g., from 90 BPM to 99 BPM).

[0209] Now for reference Figure 4B This is a simplified flowchart illustrating a method for performing a cardiac stress test according to an exemplary embodiment of the present invention.

[0210] Figure 4B The methods shown include:

[0211] Measure ECG or electrophysiological recording signals (422);

[0212] The high-frequency (HF) component is extracted from the ECG or electrophysiological record signal to generate the HFQRS signal (424);

[0213] Based on the analysis of HFQRS signals at multiple different times, multiple HF values ​​at multiple different times were calculated (426);

[0214] Measure at least one or more corresponding values ​​of physiological parameters at multiple times corresponding to multiple HF values ​​(428); and

[0215] The cardiac stress test was stopped based on the fact that at least some of the values ​​of multiple physiological parameters were different from each other (430).

[0216] Now for reference Figure 4C This is a simplified flowchart illustrating a method for analyzing the heart condition of a subject according to an exemplary embodiment of the present invention.

[0217] Figure 4C The methods shown include:

[0218] Measure ECG or electrophysiological recording signals (442);

[0219] The high-frequency (HF) component is extracted from the ECG or electrophysiological record signal to generate the HFQRS signal (444);

[0220] Based on the analysis of HFQRS signals at multiple different times, multiple HF values ​​at multiple different times were calculated (446);

[0221] Measure at least one or more corresponding values ​​of physiological parameters at multiple times corresponding to multiple HF values ​​(448); and

[0222] ECG or electrophysiological signals were analyzed based on multiple HF values ​​and corresponding physiological values ​​(450).

[0223] It is anticipated that during the patent term of this application, many related devices and methods for measuring electrographs (including low-frequency and high-frequency) will be developed, and the scope of the term electrograph is intended to include all these prior art techniques.

[0224] It is anticipated that during the patent term of this application, many related devices and methods for measuring electrocardiograms (including low-frequency and high-frequency ones) will be developed, and the scope of the term electrocardiogram is intended to a priori include all these new technologies.

[0225] The terms “comprising,” “including,” “having,” and their variations mean “including but not limited to.”

[0226] The term "composed of" is intended to mean "including and limited to".

[0227] The term "consistently made up of" means that a composition, method, or structure may include additional ingredients, steps, and / or portions, provided that the additional ingredients, steps, and / or portions do not materially alter the basic and novel characteristics of the claimed composition, method, or structure.

[0228] As used herein, the singular forms “a,” “an,” and “the” include references to the plural forms unless the context explicitly specifies otherwise. For example, the terms “a unit” or “at least one unit” can include multiple units, including combinations thereof.

[0229] The terms “example” and “exemplary” are used herein to mean “served as an example, instance, or illustration.” Any embodiment described as “example” or “exemplary” is not necessarily to be construed as being more preferred or advantageous than other embodiments, and / or excludes the incorporation of features from other embodiments.

[0230] The term "optionally" is used herein to mean "provided in some embodiments but not in others". Any particular embodiment of this disclosure may include multiple "optional" features unless these features conflict.

[0231] Throughout this application, various embodiments of the present disclosure may be presented in a range format. It should be understood that the range format is merely for convenience and brevity and should not be construed as a rigid limitation on the scope of the invention. Therefore, the range description should be considered as having specifically disclosed all possible subranges and the individual values ​​within those ranges. For example, a description of a range such as 1 to 6 should be considered as having specifically disclosed subranges such as 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, etc., and individual numbers within that range, such as 1, 2, 3, 4, 5, and 6. This applies regardless of the width of the range.

[0232] Whenever a range of numbers is indicated herein (e.g., “10-15”, “10 to 15”, or any pair of numbers connected by such range indications), it means any number (fraction or integer) included within that range limit, including the range limit, unless the context explicitly states otherwise. The phrases “range / range / scope between the first and second indicators” and “from the first indicator to,” “to,” “until,” or “until” (or another such range indication term) “range / range / scope of the second indicator” are used interchangeably herein and are intended to include the first and second indicators and all decimals and integers in between.

[0233] Unless otherwise stated, the numbers used herein and any ranges of numbers based thereon are approximations within a reasonable range of measurement precision and rounding error as understood by those skilled in the art.

[0234] It should be understood that, for clarity, certain features of this disclosure described in the context of individual embodiments may also be provided in combination in a single embodiment. Conversely, for brevity, various features of this disclosure described in the context of a single embodiment may also be provided individually, or in any suitable sub-combination, or as embodiments suitable for any other description of this disclosure. Certain features described in the context of various embodiments should not be considered essential features of those embodiments unless the embodiments would not function without these elements.

[0235] The various embodiments and aspects of this disclosure as described above and claimed in the claims section are experimentally supported in the following examples.

[0236] Example

[0237] The following examples, together with the foregoing description, illustrate some embodiments of this disclosure in a non-limiting manner.

[0238] Now for reference Figure 5A The illustration shows the potential advantages of incorporating the HR-adjusted threshold into HFQRS signal analysis according to an example embodiment.

[0239] Figure 5A The correlation trend between HFQRS signal values ​​and heart rate (HR) in single-lead (V4) HF signals measured during routine 12-lead electrocardiogram stress testing in ischemic patients is shown.

[0240] Figure 5A The top graph 402 shows y-axis 404 as HR [heart rate per minute] and x-axis 406 as time [seconds], and the bottom graph 412 shows y-axis 414 as HFQRS signal value [microvolts] and x-axis 416 as time [seconds].

[0241] Figure 5A The first track 410 in the top heart rate curve 402 shows the patient's heart rate during the stress test, and the second track 411 in the bottom HFQRS signal value curve 422 shows the HFQRS signal value of the HF ECG signal during the stress test.

[0242] Different symbols are used at different time points 412, 413, 414, 415, and 416 to represent different HFQRS signal values ​​at different heart rates during the test.

[0243] The circular symbol at time 412 indicates the location of the measurement baseline or resting HFQRS signal value.

[0244] The inverted triangle symbol at time 416 indicates the location where a high HR (considered 100% HR in this load test) and low HFQRS signal value were measured.

[0245] The additional symbols at times 413, 414, and 415 indicate the different times when the patient reached a lower HR (95%, 90%, and 85% of 100% HR, respectively).

[0246] Different time points (413, 414, and 415) were used to simulate different potential test endpoints. These endpoints were selected to simulate three shorter tests in which patients achieved lower heart rates (95%, 90%, and 85% of 100% HR, respectively). The HFQRS index was calculated using the same baseline or resting heart rate for additional simulated tests, such as... Figure 5B As shown, the HFQRS signal value decreases monotonically as the heart rate percentage decreases.

[0247] Now for reference Figure 5B Table 432 shows a comparison of various heart rate (HR) and HFQRS signal values ​​with baseline heart rate values.

[0248] Figure 5A Optionally, this can be used to benefit from HR-adjusted thresholds to explain HF trends. See the non-restrictive example. Figure 5A The trend shows that if the test subjects reach 100 BPM (reference) Figure 5A If the test ends before 413 and 414, the relative decrease of HFQRS in microvolts (µV) is below the threshold of 50%.

[0249] Figure 5AIt also showed a trend of heart rate above 100 BPM (refer to 415, 416), and a corresponding relative decrease in HFQRS. We can conclude that the negative interpretation is incorrect. However, a true positive interpretation is obtained if the HR adjustment threshold is lowered by 50% of the threshold for a pattern of high decrease at low relative heart rate changes (refer to the simulation tests shown in 413-414).

[0250] Furthermore, the results in Table 432 also show that the HR-adjusted threshold can reduce the HFQRS level in positive HFQRS leads by a constant threshold of 50%, which is valuable for detecting positive leads and making early diagnoses.

[0251] This article describes a study in which changes in high-frequency intracardiac electrograms indicate myocardial ischemia.

[0252] Myocardial infarction (MI) causes electrophysiological changes, which are reflected in surface electrocardiography (ECG) and intracardiac electrogram (EGM). Recent studies have shown that EGM monitoring of ST segment deviation may be a sensitive biomarker for thrombotic coronary artery occlusion. High-frequency QRS complex (HFQRS) on surface ECG has been shown to be a reliable biomarker for MI. Few studies have been published on the high-frequency components of the EGM signal, such as those measured in implantable devices during MI.

[0253] This study recorded and analyzed intracardiac HFQRS (iHFQRS) at standard EGM electrodes in typical locations (right ventricle (RV), right atrium (RA), and left ventricle (LV)) under normal conditions and during MI to investigate the iHFQRS response to ischemia in animals.

[0254] Methods – Two different pig models were used – acute and chronic. In the acute model, ischemia in anesthetized pigs was induced by inflating a balloon in a coronary artery. In the chronic model, a copper-plated stent was placed along the left anterior descending artery (LAD), and conscious animals were monitored for several weeks as the stent gradually occluded.

[0255] Results – During balloon-induced myocardial infarction (MI), the amplitude of the iHFQRS signal was significantly reduced. This response preceded ST-segment changes and was the sole indicator of ischemia in short-term occlusions. In the chronic model, the iHFQRS signal was more sensitive to and more stable in response to ischemia following copper-plated stent placement compared to ST-segment levels. Two effects were observed in the iHFQRS of ischemic animals: RMS decreased during increased heart rate (stress testing), and the mean amplitude showed a monotonic decrease on a normal basis as the induced disease progressed. These iHFQRS changes were noticed several days prior to the onset of acute myocardial infarction (AMI).

[0256] Conclusion - The results indicate that iHFQRS has the potential to serve as an early indicator of myocardial ischemia onset and progression.

[0257] introduce

[0258] Early diagnosis and monitoring of ischemic heart disease

[0259] Ischemic heart disease (IHD) is a leading cause of death worldwide, and its diagnosis relies heavily on the interpretation of surface electrocardiograms (ECGs). However, various abnormalities in the depolarization or repolarization phases of the cardiac cycle caused by myocardial infarction (MI) are reflected as relatively minor changes on surface ECGs, or may not be apparent at all during visual examination. Given the various available clinical protocols that can reduce mortality, early detection of IHD and ischemic events can significantly improve clinical outcomes.

[0260] Acute myocardial infarction (AMI) is an acute ischemic disease requiring timely intervention to prevent further myocardial necrosis. It is a typical case where the time from onset to treatment is crucial and must be shortened as much as possible. Stable IHD, meaning the patient's symptom status shows no recent or acute changes, is another valuable condition for early detection. Patients with stable IHD often experience chronic, slowly worsening angina symptoms, which are usually treated with medication or may require emergency intervention; therefore, continuous, long-term monitoring is essential.

[0261] Unlike surface ECG analysis based on changes in the repolarization phase of the cardiac cycle (ST segment changes), HFQRS analysis is based on the HF component of the depolarization phase (QRS complex). While surface ECG signals are in the millivolt range, the HFQRS extracted from them are in the microvolt range (µV). Extraction of these low-amplitude signals can be achieved using high-resolution ECG acquisition, signal-to-noise ratio (SNR) enhancement, and advanced signal processing techniques.

[0262] The independent nature of the ST segment and HFQRS makes the latter a potentially valuable tool in the diagnostic process, and adds to the information that can be used in conjunction with ST segment-related information. Furthermore, it has been shown to provide diagnostic information about the presence and severity of acute myocardial infarction, as well as in the diagnosis of patients with acute coronary syndrome in the emergency department.

[0263] HFQRS analysis can be used in other clinical applications. One such application is embedding HFQRS analysis into implantable devices to monitor MI, improve the diagnosis of MI-related pathology, and reduce healthcare costs.

[0264] Intracardiac electrocardiogram

[0265] Intracardiac electrogram (EGM) refers to the sensing of intrinsic changes in electrical potential signals measured by electrodes from specific locations on or within the heart. In some embodiments, EGM signals are optionally recorded, analyzed, and stored by electrodes (e.g., electrodes in implantable cardiac devices) that allow for bipolar recordings (recorded from two adjacent electrodes, typically within a chamber) and unipolar recordings (recorded from a tip electrode located within the device's "canister," typically in the upper chest or shoulder). Modern implantable devices have extensive memory capabilities, enabling continuous EGM monitoring.

[0266] Compared to surface ECGs recorded from the body surface, EGM signals are measured from the myocardium. Therefore, EGM recordings avoid the insulation of the thoracic cavity and lungs, and thus typically have an amplitude 5–10 times greater. Furthermore, they do not contain noise from the electrode-skin interface and are considered more independent of electrode positioning.

[0267] Patients with implantable devices (i.e., cardiac rhythm management devices) have a high prevalence of coronary artery disease (CAD). Multiple studies have shown that myocardial ischemia can be detected via endocrine gamma (EGM), and that EGM may be more sensitive than surface ECG in detecting ischemia. Recently, the ability of implantable devices based on intracardiac ST-segment measurements to aid in the early diagnosis of acute myocardial infarction has been demonstrated in porcine models and humans. However, the ischemic HFQRS manifestations in EGM signals have not yet been investigated.

[0268] The current study reports animal experiments characterizing changes in intracardiac HFQRS electrography (iHFQRS) during myocardial infarction (MI). The investigation was conducted in a preclinical setting, including both acute and chronic models. The acute MI model was used to assess the sensitivity and timing of iHFQRS in detecting ischemia and to compare it with intracardiac ST-segment deviation. The chronic MI model was based on the implantation of a copper-coated stent into the coronary artery and was used to test the feasibility of using iHFQRS changes to identify coronary infarction formation and to evaluate its potential to provide reliable monitoring of ischemic burden.

[0269] method

[0270] Acute phase

[0271] A prospective, interventional preclinical study was conducted to evaluate the feasibility of implantable devices for monitoring and / or early detection of myocardial infarction (MI). EGM electrodes were implanted in three anesthetized and ventilated juvenile (4-6 months old) female pigs (weighing 60-70 kg) in a controlled environment to simulate implantable biventricular devices.

[0272] Data collection

[0273] EGM uses specified acquisition hardware (AQ-200, BSP Medical, Tel-Aviv, Israel), along with improved amplifiers (input range ±100mV) and wiring configurations for acquisition. This example system features a high sampling rate of 2kHz per channel, a wide frequency response up to 300Hz, and a high resolution of 16 bits, making it suitable for HFQRS analysis.

[0274] As a non-limiting example, the system is rewired from a conventional 12-lead ECG cabling configuration to a STAR configuration, similar to the cabling of an implantable pacemaker, as follows: six EGM channels are connected to channels C1-C6 (each intracardiac electrode is connected to two channels, one for its distal end and the other for its proximal end), the canister is connected to the left-leg channel (LL), and the neutral electrode is connected to the right-leg channel (RL). Using the STAR configuration, six far-field EGM voltage recordings are obtained between each electrode tip: (RVd (distal right ventricular tip), RVd / p (proximal / distal right ventricular tip), RAd / p (proximal / distal right atrial tip), LVd / p (proximal / distal left ventricular tip)) and the canister.

[0275] Test Plan

[0276] Each trial began with an invasive procedure to implant three bipolar pacemaker leads in the right ventricle (RV), right atrium (RA), and coronary sinus (CS) to pace the left ventricle (LV), similar to typical implantable intracardiac electrode placement. Each electrode had a proximal and distal tip (hereafter indicated by "p" and "d" in the suffix), thus recording a total of six EGM channels simultaneously. Next, two additional electrodes were implanted subcutaneously: a canister-style analog electrode (CAN) located in the canister position and used as a reference, and another noise-cancelled electrode (neutral). For reliable fixation to cardiac tissue, both the RV and RA electrodes were implanted with retractable screws (CapSureFix, respectively). TM5568 (Medtronic and Safios53, Biotronik), while the left ventricular (LV) electrode was inserted into the coronary sinus (CS) and positioned near the LV wall using a flexible designated electrode (Corox OTW 85-BP, Biotronik). Electrode positioning was verified by fluoroscopy and real-time visual examination of the EGM signal triggered by an external pacemaker.

[0277] Once all electrodes are correctly positioned, they are connected to the acquisition device and baseline measurements are recorded. After sufficient and satisfactory baseline recordings have been obtained, balloon catheters are inflated at different sites in one or more major coronary arteries to produce partial and complete occlusions. Partial and complete balloon occlusions of the major coronary arteries (left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA)) are used to induce AMI. Table 1 (see below) lists the occlusion locations and the number of repetitions per animal. The location, severity (complete or partial), and duration (occlusion duration between 10 and 180 seconds) of the occlusion along the coronary arteries varied. Each occlusion began with baseline recordings taken a few minutes before balloon inflation and continued after balloon deflation without any intervention (greater than 10 minutes).

[0278] Table 1 - Block List for Each Location

[0279]

[0280]

[0281] HFQRS Analysis

[0282] To extract iHFQRS content from EGM recordings, a three-stage processing approach was employed: (1) peak detection (PD), (2) alignment, and (3) low-frequency filtering to obtain high-frequency signals. Since the signal-to-noise ratio (SNR) level was sufficient during recording (greater than 10), an automatic frame-by-frame analysis method was used for analysis.

[0283] 1. Peak Detection (PD): An example procedure is based on finding the maximum amplitude above an updated threshold. The first step is to apply two median filters, one to remove the baseline (e.g., a 301 [ms] window) and the other to suppress the T-wave component by subtracting the filter output from the input signal (e.g., a 151 [ms] window). Next, another median filter (e.g., a 13 [ms] window) is used to eliminate spikes from non-physiological sources (e.g., electrode movement), and the absolute value of the signal is sent to a peak detector (some leads have negative polarity). The peak detector may find the maximum amplitude above an initially set adaptive threshold, which may optionally be manually set and iteratively updated relative to a template mean. PD is applied to each lead separately, and optionally only QRS complexes detected in at least two leads (e.g., within a 30 ms time window) are considered valid. The detected peak time may be selectively and automatically projected to all available leads and used to calculate heart rate (HR).

[0284] 2. Alignment: Accurate alignment of QRS complexes is crucial for successful HFQRS analysis. Therefore, to optimize alignment, an intermediate frequency (IF) range template matching process is applied to the detected QRS complexes to achieve better localization of the reference point on the QRS cross-correlation. For this purpose, the raw signal is optionally filtered using a causal finite impulse response (FIR) bandpass filter (BPF) with a cutoff frequency of, for example, 20–70 Hz (filter order 21), and aligned from the baseline and averaged with a set of adjacent valid QRS complexes to form the template for each lead. In EGM, the QRS complex can also be referred to as the V-wave. Template matching is based on the cross-correlation value between the updated template and a given QRS complex (e.g., both with a magnitude of 200 ms). The initial template for each lead consists of 100 valid complexes. The template is iteratively updated during recording, where each new complex is compared with the current template and used to update the template if it meets the inclusion criteria. The template is then used twice: once as a reference to align valid templates with the updated template based on the cross-correlation value, and again to exclude irregular complexes. The cross-correlation value between the template and each detected composite wave (per lead) is used as an inclusion criterion, with an optional threshold of less than 0.95 as an exclusion criterion. The result of this stage is the time of all valid QRS composite waves and their matching hysteresis for optimized alignment.

[0285] 3. High-Frequency Filtering: A specified digital filter was used to filter into the HF band. This filter was applied to all recordings and leads before quantization. Next, valid QRS complexes were extracted using a 200-millisecond time window centered on each valid QRS complex. For further optimization of alignment, a high-resolution correction based on a parabolic approximation was employed.

[0286] HFQRS Assessment

[0287] Two methods were used to evaluate iHFQRS during normal perfusion and occlusion: morphological examination and quantification. For visual examination of the iHFQRS signal, the envelope of the composite wave was calculated using the Hilbert transform, applied only to the effective composite wave. Due to the rapid oscillation of the iHFQRS signal, the envelope representation is more efficient and useful for analysis and comparison. To quantify the HFQRS content, the root mean square (RMS) of the iHFQRS signal along the QRS composite wave was calculated. This quantization was applied to the effective composite wave and used to construct iHFQRS time curves. These time curves describe the level of iHFQRS (per lead) during the experiment and were used to evaluate the time-dependent sensitivity of the iHFQRS index in identifying minor lesions (MIs).

[0288] Routine electrocardiogram analysis

[0289] To compare the time-dependent sensitivity of HFQRS with the index derived from traditional EGM, the ST-segment shift of the effective composite wave is automatically extracted, and a matching ST-segment time curve is generated. The low-frequency RMS value of the QRS composite wave is calculated, and the ST shift is defined as the difference between the ST level and the PR interval level of the composite wave.

[0290] chronic phase

[0291] Experimental protocol

[0292] Five series of experiments were conducted on conscious female pigs (weighing 60-70 kg) over a period of 1-2 months, with EGM monitored 24 hours a day. To allow the LAD to gradually occlude, a copper-plated stent was implanted into the artery after several days of baseline signal acquisition. The progression of stenosis was assessed by several angiography sessions during the experiments.

[0293] To identify occlusion formation using iHFQRS signals, two main analytical methods were investigated: (1) Elevated HR analysis – This method is based on signals measured during daily (1 to 3) stress tests. The basic principle is that in the case of partial occlusion, animals may experience demand-driven ischemia when HR increases, which may be reflected in a reduction in the iHFQRS signal relative to the baseline signal (normal HR). (2) Long-term iHFQRS analysis – In this method, the mean and standard deviation of the HFQRS signal are examined hourly or daily to determine the decreasing trend of HFQRS signal values ​​associated with stenosis progression. The above method is an uncorrelated HR analysis. Similar analytical methods were applied to ST segment measurements, and the performance of the two markers in detecting ischemia was compared.

[0294] Data collection

[0295] Similar to the acute model procedure, intracardiac bipolar pacemaker leads were placed in the left ventricle (LV), right ventricle (RV), and right atrium (RA). A subcutaneous electrode in the canister position served as a reference lead, and another subcutaneous electrode was used for noise cancellation (neutralization). Continuous signal measurements were performed using a six-channel Holter monitor, a portable acquisition device specifically designed for this project (manufactured by Beecardia in Haifa, Israel). The Holter was placed on the animal's back with a custom-fitted vest. Signals were acquired at a high sampling rate (1 kHz) and recorded to an internal memory card, unloaded daily. It could also communicate with a laptop via Bluetooth for real-time signal monitoring.

[0296] result

[0297] Acute phase

[0298] Electrode positioning

[0299] All bipolar electrodes were successfully implanted in all three animals in the following order: right ventricle (RV), right atrium (RA), and left ventricle (LV). The animals underwent a series of consecutive occlusions: 12, 13, and 2. The occlusions were performed along the LAD, CRX (left circumflex artery), and RCA, respectively. Fluoroscopy and visual assessments were repeatedly performed to ensure correct electrode placement.

[0300] EGM Records - Verification

[0301] EGM signals were recorded simultaneously from all animals and all three leads during baseline.

[0302] Now for reference Figure 6 It shows a graph of the results from the acute phase of the experiment according to an example embodiment.

[0303] Figure 6The two graphs 602 and 604 in the left column depict the EGM signals recorded during baseline from the distal 622 and proximal 624 tips of the RV (top graph 602) and LV (bottom graph 604) electrodes.

[0304] Examples of high-frequency (HF) content of EGM signals after filtering to the HF band (Figures 606 and 610 show the right ventricle (RV), and Figures 608 and 612 show the left ventricle (LV)).

[0305] Figures 602 and 604 depict EGM signals recorded from the distal and proximal tips of the right ventricle (RV) (Figure 602) and coronary sinus (CS) (Figure 604) electrodes under normal perfusion conditions. Although their morphologies differ, the depolarization and repolarization complexes (representing the QRS and T wave complexes in conventional ECG) are temporally consistent and clearly visible. The EGM voltage is approximately a few millivolts, with the largest peak-to-peak amplitude, almost 11 millivolts, observed at the distal tip of the coronary sinus (CS). The signal amplitude measured from the distal (RV and LV electrodes) is larger compared to the EGM recorded from the proximal end. The stable EGM signal from the right atrium, measured in the RA channels (distal and proximal), exhibits the dominant amplitude during atrial contraction and relatively smaller fluctuations during ventricular depolarization.

[0306] Figures 606, 608, 610, and 612 show examples of iHFQRS signals extracted from EGM signals recorded from the distal and proximal tips during the baseline period. These signals exhibit significantly lower amplitudes (ranging from tens to hundreds of microvolts) compared to low-frequency signals, even within the depolarization timeframe (approximately 70 milliseconds), and are virtually undetectable outside this timeframe.

[0307] Figures 602 and 604 show examples of electrogrammage (EGM) signals measured from the distal and proximal tips (solid line 622 and dashed line 624, respectively). Figures 606, 608, 610, and 612 show examples of the high-frequency (HF) content of the EGM signal after filtering to the HF band (Figures 606 and 610 show the right ventricle (RV), and Figures 608 and 612 show the left ventricle (CS)).

[0308] Despite their different morphologies, the depolarization and repolarization complexes (representing the QRS and T wave complexes in a conventional ECG) are temporally consistent and clearly visible between the electrodes and the tip. The EGM voltage is approximately a few millivolts, with the largest peak-to-peak amplitude, almost 11 millivolts, observed at the distal tip of the left ventricle (LV). The signal amplitude measured from the distal tip (from the RV and LV electrodes) is larger than that recorded from the proximal tip. Furthermore, the RAD (distal tip of the right atrium) and RAP (proximal tip of the right atrium) signals were successfully measured, exhibiting dominant amplitudes during atrial systole and relatively smaller fluctuations during the depolarization phase.

[0309] HFQRS Analysis

[0310] Frame-by-frame HFQRS analysis was performed on EGM signals from all leads and all three animals. The intensity and morphology of iHFQRS signals during normal perfusion (i.e., baseline) and balloon occlusion were examined and compared with conventional ST-segment analysis.

[0311] Figure 6 An example of the iHFQRS signal extracted from the EGM signal (recorded from the distal and proximal tips) during baseline is shown. Figure 6 The middle and right columns of the charts are 606, 608, 610, and 612. Compared to the recorded signals, these signals have significantly lower amplitudes (ranging from tens to hundreds of microvolts) and oscillate rapidly within the depolarization timeframe (approximately 70 milliseconds), while outside this timeframe, the signal is almost undetectable. Furthermore, the HF signals recorded from the distal leads have much larger amplitudes (approximately four times greater) compared to the proximal leads.

[0312] The iHFQRS time curves showed a significant decrease in intensity and a change in envelope morphology during occlusion and in comparisons between the perfusion and reperfusion phases. Both RV and LV leads demonstrated the sensitivity of the iHFQRS to ischemia caused by multiple occlusions. This phenomenon was equally evident in occlusions of all major arteries: LAD, RCA, and LCX, including distal locations and very short occlusions (see Table 2 below). The table uses "+" to indicate a response and "-" to indicate no response.

[0313] Table 2 - Indicators of iHFQRS and ST response in different coronary artery occlusions

[0314]

[0315] Now for reference Figure 7A It shows two significant response diagrams of the ST and iHFQRS signals according to an example embodiment.

[0316] Figure 7AThe significant responses of the ST signal (top figure 702) and the iHFQRS signal (bottom figure 704) during two consecutive, relatively long (greater than 40 seconds) complete blockages in the LAD are shown.

[0317] Figure 7A The first line 701 shows the absolute ST segment deviation (top figure 702), and the second line 705 shows the HFQRS signal recorded from the distal tip of the right ventricular lead (RVd) during two complete occlusions of the left coronary artery (LAD) (50 and 70 seconds, respectively) (bottom figure 704). Vertical line 706 indicates the balloon inflation time, and vertical line 708 indicates the balloon deflation time.

[0318] As can be seen, during the first and second blockages, the RMS of the iHFQRS decreased significantly by 28% and 40% respectively (equivalent to the absolute values ​​of 7uV and 10uV, respectively), with major ST offsets of 100uV and 200uV, respectively. In contrast to RV and LV, the RA signal did not exhibit sufficient sensitivity in most measurements and is therefore not shown here.

[0319] Now for reference Figure 7B and 7C The two figures illustrate an electrical recording and an HFQRS envelope drawn during occlusion according to an example embodiment.

[0320] Figure 7B and 7C The following curves 722, 723, 724 and HFQRS signal envelopes 742, 743, 744 (curve 740) of the electrophoresis records (curve 720) recorded at different stages of the first occlusion are shown: before - 691 seconds (722, 742), in the early stage - 724 seconds (723, 743), and near the end of the occlusion - 754 seconds (724, 744).

[0321] ST segment analysis showed significant shifts during most occlusions. However, the iHFQRS response typically appeared much faster, and although no significant changes were observed in the recorded EGM signal (including ST segment, T wave, and QRS amplitude), a significant reduction was observed in the iHFQRS signal.

[0322] Figure 7B and 7C This shows a single-shot comparison between the envelopes of the EGM and iHFQRS signals recorded at different time points during the occupancy period. Figure 7B and 7CIn the figure, the EGM morphology remained almost unchanged after 20 seconds of occlusion (lines 722 and 723 in Figure 720), while the iHFQRS envelope decreased significantly (lines 742 and 743 in Figure 740). However, after 50 seconds, both signals showed significant changes, with ST segment elevation and a significant decrease in the iHFQRS envelope (lines 722 and 724 in Figure 720 and lines 742 and 744 in Figure 740).

[0323] Now for reference Figure 8 The figure illustrates a comparison of the sensitivity of the iHFQRS signal and the ST offset during occupancy, according to an exemplary embodiment.

[0324] Figure 8 Chart 800 is shown, with X-axis 802 displaying time in seconds, left Y-axis 804 displaying HFQRS in microvolts (µV), and right Y-axis 806 displaying ST offset in microvolts (µV).

[0325] Figure 8 The sensitivity of the iHFQRS signal 809 and ST offset 808 during a long (3 minutes) partial occlusion along the distal segment of LAD was compared using time curves, and a significant change in the iHFQRS signal 809 was detected 75 seconds before the ST offset was observable.

[0326] Figure 8 This shows an example of the early response to ischemic conditions to the HFQRS signal 809 compared to the ST segment shift 808. The vertical dashed lines 811 on the left and 812 on the right represent the balloon inflation time and the time at which a significant ST segment signal shift was noticed.

[0327] In some long blockages exceeding 30 seconds, offset 808 was smaller compared to HFQRS analysis, while in some short blockages, only iHFQRS responded when no ST offset was detected (see [link to iHFQRS analysis]). Figure 9 (As an example).

[0328] Now for reference Figure 9 It shows two graphs comparing ST offset and iHFQRS during two occlusions according to an example embodiment.

[0329] Figure 9 The first image 902 shows ST-segment deviation 904, and the second image 912 shows iHFQRS 914. The time curves for ST-segment deviation 904 and iHFQRS 914 were recorded from the left ventricular distal lead (LVd) during two short (10-second) complete occlusions of the distal segment of the left circumflex coronary artery (CRX).

[0330] The vertical lines represent balloon inflation (906) and deflation (908), respectively.

[0331] Now for reference Figures 10A-10C It is a graph showing the ST and HFQRS signal values ​​collected during an induced ischemic attack, according to an example embodiment.

[0332] Figure 10A The first graph 1002 is shown, with the X-axis 1005 representing heart rate and the Y-axis 1004 representing intracardiac HFQRS (iHFQRS) signal values ​​in microvolts [µV].

[0333] First graph 1002 shows a scatter plot 1008 of iHFQRS signal values ​​versus heart rate values ​​collected over a period of time, as a non-limiting example, without inducing ischemia within a day. During this period, the experimental subjects were allowed to exercise, causing the heart rate to change over time.

[0334] The first chart 1002 shows that in non-ischemic subjects, the iHFQRS signal value increases with increasing heart rate. The line 1009 plotted in the first chart 1002 is approximately parallel to the regression line that can be calculated for the points in the scatter plot 1008.

[0335] Figure 10A A second chart 1012 is also shown, with the X-axis 1015 representing heart rate and the Y-axis 1014 representing ST signal values ​​in microvolts [µV].

[0336] The second chart 1012 shows a scatter plot 1018 of ST values ​​versus (VS) heart rate values ​​collected over the same time period as the first chart 1002.

[0337] The second chart 1012 shows that in non-ischemic subjects, the ST signal value decreases as heart rate increases. The line 1019 plotted in the second chart 1012 is approximately parallel to the regression line that can be calculated for the points in the scatter plot 1018.

[0338] Figure 10B The third chart 1022 shows the heart rate on the X-axis (1025) and the iHFQRS signal value on the Y-axis (1024), in microvolts (µV).

[0339] The third figure, 1022, shows a scatter plot 1028 of HFQRS signal values ​​versus heart rate values ​​collected over a period of time. As a non-limiting example, ischemia was induced in the subjects over a day. During this period, the experimental subjects were allowed to exercise, causing their heart rate to change over time.

[0340] The third chart 1022 shows that in ischemic subjects, the HFQRS signal value decreases as heart rate increases. The line 1029 plotted in the third chart 1022 is approximately parallel to the regression line that can be calculated for the points in the scatter plot 1028.

[0341] Figure 10B A fourth chart 1032 is also shown, with the X-axis 1035 representing heart rate and the Y-axis 1034 representing ST signal values, in microvolts [µV].

[0342] The fourth chart 1032 is a scatter plot 1038 of ST values ​​versus heart rate values ​​collected during the same time period as the third chart 1022.

[0343] The second chart 1032 shows that in non-ischemic subjects, ST signal values ​​decrease with increasing heart rate. The line 1039 plotted in the fourth chart 1032 is approximately parallel to the regression line that can be calculated for the points in the scatter plot 1038.

[0344] Comparing the second chart 1012 with the fourth chart 132, it can be seen that in both non-ischemic and ischemic subjects, the ST signal scatter plot decreases with increasing heart rate.

[0345] Comparing the first chart 1002 with the third chart 122, it can be seen that the iHFQRS signal scatter plot of non-ischemic subjects increases with increasing heart rate, while the iHFQRS signal scatter plot of ischemic subjects decreases with increasing heart rate.

[0346] In some embodiments, the HFQRS signal and heart rate values ​​are measured at different time points, and the relationship between the HFQRS signal values ​​and heart rate is estimated.

[0347] In some embodiments, if the HFQRS signal value does not increase when the heart rate increases, an indication of potential ischemia is generated.

[0348] In some embodiments, a potential ischemia is indicated if the HFQRS signal value decreases as heart rate increases.

[0349] In some embodiments, a regression line is calculated for the HFQRS and heart rate values.

[0350] In some implementations, if the slope of the regression line is not positive, it indicates potential ischemia.

[0351] In some implementations, a negative slope of the regression line indicates potential ischemia.

[0352] It should be noted that the downward sloping line in the third chart 1022 can be detected using only the lower heart rate portion of the scatter plot 1028.

[0353] In some embodiments, an indication of potential ischemia may be detected in a scatter plot 1028 of HFQRS signal values ​​versus heart rate values ​​collected over a period of time before the subject has reached the maximum heart rate planned for and / or allowed for that subject.

[0354] As a non-limiting example, the subject may begin with a standard six-minute test, or a Bruce test, or a similar stress test, and, in accordance with current practice, an indication of potential ischemia may be obtained before the end of the test by using a scatter plot 1028 of the HFQRS signal values ​​versus heart rate values.

[0355] By way of another non-limiting example, the subject can perform his / her normal activities, whether or not they exert enough effort to reach the load heart rate, and according to current practice, potential ischemia can be indicated by using a scatter plot 1028 of HFQRS signal values ​​versus heart rate values ​​before the end of the test.

[0356] In some embodiments, an implantable cardiac device (ICD) may selectively collect paired HFQRS signal values ​​and heart rate values ​​based on the analysis of cardiac signals over a time period, and may generate indications of potential ischemia.

[0357] In some embodiments, the time period can be very short—optionally a few minutes—and can be automatically selected by the ICD immediately after a sufficient range of heart rates has been collected.

[0358] In some embodiments, the ICD may optionally calculate a rolling regression slope value for paired HFQRS signal values ​​and heart rate values, adding the latest pair of values ​​and deleting the oldest pair of values.

[0359] In some embodiments, when the rolling slope of the regression line falls below a threshold, the ICT may optionally generate an indication of potential ischemia.

[0360] In some embodiments, the ICT may optionally generate an indication of potential ischemia when the rolling slope of the regression line falls below a certain percentage of the base threshold of the rolling slope.

[0361] In some embodiments, the baseline threshold for the rolling slope may optionally be generated during a medical examination or stress test as described above, whereby the physician may optionally determine that the baseline value represents a reasonable, non-ischemic value.

[0362] In some embodiments, the base threshold for the rolling slope may optionally be an initial value generated when the ICD begins such measurements and analyses, and used as the base value from then on.

[0363] In some implementations, the baseline threshold for the rolling slope may optionally be determined by a physician, may optionally be adjusted when the subject presents an indication of potential ischemia based on the rolling slope, and may be determined by the physician to be that the subject is not ischemic.

[0364] Figure 10C Four charts are displayed: 1042, 1052, 1062, and 1072.

[0365] These four diagrams and references Figure 10A and 10B The subjects described were the same, and the values ​​measured on the same day before (Fig. 102, 1062) and after (Fig. 1052, 1072) induced ischemia are shown.

[0366] Figure 10C Figure 1042 and Figure 1052 show the values ​​of the HFQRS signal versus time during a short period of time during a heartbeat when the HFQRS signal is at its highest.

[0367] In the first graph 1042, the X-axis 1043 is in microvolts (µV) and the Y-axis 1044 is in milliseconds (mSec). In the second graph 1052, the X-axis 1053 is in microvolts (µV) and the Y-axis 1054 is in milliseconds (mSec).

[0368] Figure 10C Also shown are a third figure 1062 and a fourth figure 1072, which show the ECG signal versus time values ​​during a single heartbeat.

[0369] The third graph 1062 uses microvolts (µV) as the X-axis and milliseconds (mSec) as the Y-axis (mSec) as the Y-axis (mSec). The fourth graph 1072 uses microvolts (µV) as the X-axis and milliseconds (mSec) as the Y-axis (mSec) as the Y-axis (mSec).

[0370] It should be noted that the X-axis 1044 and 1054 of the first figure 1042 and the second figure 1052 are not aligned with the X-axis 1064 and 1074 of the third figure 1062 and the fourth figure 1072.

[0371] Figure 1042 shows a first line 1045 associated with a higher heart rate (205 beats per minute (bpm)) in non-ischemic subjects, and a second line 1046 associated with a lower heart rate (115 beats per minute (bpm)) in non-ischemic subjects. Figure 1042 shows the correlation between... Figure 10A Consistent with the findings in Figure 1002, higher HFQRS signal values ​​are associated with higher heart rates in non-ischemic subjects.

[0372] The second figure 1052 shows the first line 1055 associated with the higher heart rate (215 beats per minute (bpm)) in the subjects after induced ischemia, and the second line 1056 associated with the lower heart rate (90 beats per minute (bpm)) in the subjects after induced ischemia. The second figure 1052 shows the correlation between... Figure 10A Consistent with the findings in Figure 1012, higher HFQRS signal values ​​are associated with lower heart rates in ischemic subjects.

[0373] The third figure 1062 shows the first line 1065 associated with a higher heart rate (205 beats per minute (bpm)) in non-ischemic subjects, and the second line 1066 associated with a lower heart rate (115 beats per minute (bpm)) in non-ischemic subjects. The third figure 1062 illustrates the difference between heart rates—the higher the heart rate, the shorter the duration of the heartbeat line, and no significant difference in amplitude is shown between the first line 1065 (higher heart rate) and the second line 1066 (lower heart rate).

[0374] Figure 1072 shows the first line 1075 associated with the subject's higher heart rate (215 beats per minute (bpm)) after induced ischemia, and the second line 1076 associated with the subject's lower heart rate (90 beats per minute (bpm)) after induced ischemia. Figure 1072 illustrates the difference between heart rates—the higher the heart rate, the shorter the duration of the heartbeat line—and also shows the amplitude difference between the first line 1075 (higher heart rate) and the second line 1076 (lower heart rate).

[0375] chronic phase

[0376] ■ This model successfully created a rapid, progressive progression of stenosis around the lesion.

[0377] ■ In some animals, the progression of occlusion was faster than expected. For example, the diagonal branch of the coronary artery was completely occluded only 14 days after stent implantation, while the left anterior descending artery (LAD) was completely occluded 24 days after implantation.

[0378] ■ In animal 1, although no distinguishing baseline was obtained, a clear indication of ischemic status was observed by iHFQRS stress analysis (LVp). Demand-dependent ischemia was observed 13 days prior to the first myocardial infarction (MI), at which time the LAD was partially occluded.

[0379] ■ The iHFQRS response (RVd) in animal 3 also showed a significant shift between baseline measurements and the time period after stent implantation; this shift was also associated with the progression of stenosis.

[0380] ■ In animal 4, although the occlusion did not develop normally (myocardial infarction (MI) occurred immediately after implantation), the iHFQRS response was significantly reduced after implantation. Animal 5 also showed a significant change in the iHFQRS response during the experiment.

[0381] HFQRS Analysis

[0382] discuss

[0383] Acute model

[0384] ■ The results here indicate that the intensity of the HFQRS was significantly reduced during occlusion compared to the perfusion and reperfusion phases. This phenomenon occurred during occlusion of all major arteries: the left anterior descending artery (LAD), the right coronary artery (RCA), and the left circumflex artery (LCX), including distal locations.

[0385] ■ A detectable shift occurs in the ST segment during occlusion. However, the HFQRS response typically develops significantly faster: a marked decrease in the high-frequency signal is observed even when there are no changes in the low-frequency (LF) signal (including the ST segment, T wave, and QRS amplitude); this suggests that the HFQRS is a more sensitive marker, or that the early ischemic mechanism is expressed in the high-frequency components of the signal. These phenomena can be further observed in the context of chronic studies, measuring the iHFQRS response rather than the ST response in the early stages of occlusion.

[0386] The results showed that the iHFQRS is a sensitive biomarker for detecting the gradual evolution of occlusion under ischemic load. The stress analysis results were generally consistent, showing good responses in all animals, which correlated with their clinical condition.

[0387] ■ In addition to the reaction time, in some measurements (usually at very short occlusions), only the HFQRS response is measured without ST offset.

[0388] ■ Both RV and LV leads showed that the HFQRS was sensitive to ischemia induced by multiple occlusion sites. However, sufficient occlusion duration was not performed to plot the relationship between occlusion sites and these leads.

[0389] ■ The results of this preliminary study indicate that the iHFQRS component of intracardiac signals can provide a sensitive indication of myocardial ischemia, or equivalently, that early ischemic mechanisms are expressed only in the high-frequency band of the signal.

[0390] ■ The results also demonstrate that this method is superior to traditional ST analysis in terms of sensitivity.

[0391] The significant changes in the morphology and time curve of the iHFQRS signal appear before changes in the EGM (between approximately 20-45 seconds and 40-85 seconds in cases of complete and partial occlusion, respectively), indicating that the HFQRS signal is a more sensitive marker of ischemic condition.

[0392] Although the present disclosure has been described in conjunction with specific embodiments, it will be apparent to those skilled in the art that many alternatives, modifications, and variations will be readily apparent. Therefore, it is intended to cover all such alternatives, modifications, and variations falling within the spirit and broad scope of the appended claims.

[0393] The applicant intends that all publications, patents, and patent applications mentioned in this specification be incorporated herein by reference in their entirety, as if each individual publication, patent, or patent application were expressly and separately cited and incorporated herein by reference. Furthermore, any reference or designation of any reference in this application should not be construed as an admission that such reference is prior art to the invention. The use of section headings should not be construed as necessarily limiting. In addition, any priority documents of this application are hereby incorporated in their entirety by reference.

Claims

1. A method for analyzing electrocardiogram (ECG) or electrorecording signals, the method comprising: Acquire ECG or electrophysiological recording signals; The high-frequency HF portion is extracted from the QRS portion of the ECG or electrophysiological record signal to generate an HFQRS signal; The HF value is calculated based on the analysis of the HFQRS signal; Collect at least one physiological value relevant to the subject; And analyze the ECG or electrophysiological signal based on the HF value and the physiological value; The analysis includes calculating a value based on the HF value and the physiological value; wherein the calculated value is equal to: ， in: NHFRMS is the difference between the maximum value of the RMS of the HFQRS signal measured in the current time period and the minimum value of the RMS of the HFQRS signal measured in the resting state. NHR is the difference between the HR measured in the current time period and the resting HR measured in the resting state. max(HF) is the maximum HF value measured within the current time period; min(HF) is the minimum HF value measured within the current time period; currentHR is the average HR measured within the current time period; restingHR is the average HR measured at rest.

2. The method according to claim 1, wherein, The physiological value is the normalized heart rate (NHR), where NHR is the difference between the HR measured in the first time period and the resting HR measured in the second time period while at rest.

3. The method according to claim 2, wherein, The calculated value includes a value obtained by dividing a first value of the measured HF value by a second value of the HF value at rest.

4. The method according to claim 2, characterized in that, The restingHR value is retrieved from storage for the subject.

5. The method according to claim 2, wherein, The restingHR value is retrieved from storage for the subject category associated with the subject.

6. The method according to claim 1, wherein, The physiological value is the measured respiratory rate.

7. The method according to claim 1, wherein, The physiological values ​​mentioned are those related to respiratory depth.

8. The method according to claim 1, wherein, The physiological value mentioned is the tidal volume of respiration.

9. The method according to claim 5, wherein, The physiological value is the respiratory rate measured by analyzing the ECG or electrophysiological signal.

10. The method according to claim 9, wherein, The respiratory rate is calculated based on the interval between R waves in a continuous QRS complex.

11. The method according to claim 10, wherein, The HFQRS signal value includes a value obtained based on the measurement of the attenuation zone RAZ in the HFQRS signal.

12. The method according to claim 11, wherein, The HFQRS signal value includes the ratio of the interval length between two adjacent local maxima of the envelope of the HFQRS signal to the length of the QRS composite wave.

13. The method according to claim 11, wherein, The HFQRS signal value includes the ratio of the basin area of ​​the RAZ to the area of ​​the HFQRS signal envelope.

14. A system for analyzing electrocardiogram (ECG) or electrorecording signals, comprising: Input terminal, used for ECG or electrophysiological recording signals; A high-frequency HF signal extractor that extracts the HF portion from the ECG or electrogram signal; as well as The processor is configured to: calculate an HF value based on analysis of the HF portion, the HF value being the HF component of the ECG or electrophysiological signal; and measure at least one physiological value relevant to the subject. as well as Analyze the ECG or electrophysiological signal based on the HF value and the physiological value; The analysis includes calculating a value based on the HF value and the physiological value; wherein the calculated value is equal to: ， in: NHFRMS is the difference between the maximum value of the RMS of the HFQRS signal measured in the current time period and the minimum value of the RMS of the HFQRS signal measured in the resting state. NHR is the difference between the HR measured in the current time period and the resting HR measured in the resting state. max(HF) is the maximum HF value measured within the current time period; min(HF) is the minimum HF value measured within the current time period; currentHR is the average HR measured within the current time period; restingHR is the average HR measured at rest.

15. The system according to claim 14, wherein, The HF value includes the HFQRS signal value obtained based on the HF component of the QRS composite wave in the ECG or electrogram signal.

16. The system according to claim 15, wherein, The system is configured to store the HF value and the physiological value.

17. The system according to claim 15, wherein, The processor is configured to calculate the calculated value based on the HF value and the physiological value.

18. The system of claim 17, further comprising means configured to store the calculated values.

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