Detection of cardiac signal qt interval

Abstract

Disclosed herein are example devices for detecting one or more parameters of a cardiac signal. The devices include one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The devices further include processing circuitry configured to determine an R-wave of the cardiac signal and determine a previous RR interval of the cardiac signal and a current RR interval of the cardiac signal based on the determined R-wave. The processing circuitry is further configured to determine a search window based on one or more of the current RR interval or the previous RR interval, determine a T-wave of the cardiac signal in the search window, and determine a QT interval based on the determined T-wave and the determined R-wave.

Description

Technical Field

[0001] This disclosure relates to cardiac monitoring, and more specifically, to the detection of the QT interval or corrected QT interval (QTc) in cardiac signals. Background Technology

[0002] Cardiac signal analysis can be performed by a variety of devices, such as implantable medical devices (IMDs), insertable cardiac monitors (ICMs), and external devices (e.g., smartwatches, fitness trackers, mobile devices, Holter monitors, wearable defibrillators, etc.). For example, a device can be configured to process cardiac signals sensed by one or more electrodes (e.g., electrocardiography (EGM) and electrocardiography (ECG)). Characteristics of cardiac signals may include the P wave, Q wave, R wave, S wave, QRS complex, and T wave. The QT interval is the time from the beginning of the QRS complex to the end of the T wave. The QTc interval is the QT interval that has been normalized or corrected relative to heart rate using a formula. Accurately detecting and characterizing features in cardiac signals (e.g., the QT interval or QTc interval) can be important for monitoring patient health, such as the risk of sudden cardiac death. Summary of the Invention

[0003] Generally, this disclosure relates to apparatus and techniques for identifying one or more features and / or determining one or more parameters of a patient's cardiac signals (e.g., EGM and / or ECG). For example, this disclosure describes techniques for identifying the QT interval or QTc interval, which can enable the prediction of whether a patient is experiencing or will experience a tachyarrhythmia or other abnormal rhythms that may lead to sudden cardiac death. In some instances, the IMD can deliver a therapy to the patient to terminate or prevent a predicted tachyarrhythmia.

[0004] In one example, the device includes one or more electrodes; a sensing circuit system configured to sense cardiac signals via the one or more electrodes; and a processing circuit system configured to: determine an R wave of the cardiac signal; determine a previous RR interval of the cardiac signal based on the determined R wave; determine a current RR interval of the cardiac signal based on the determined R wave; determine a search window based on one or more of the current RR interval or the previous RR interval; determine a T wave of the cardiac signal within the search window; and determine a QT interval based on the determined T wave and the determined R wave.

[0005] In another instance, the method includes sensing a cardiac signal; determining an R wave of the cardiac signal; determining a previous RR interval of the cardiac signal based on the determined R wave; determining a current RR interval of the cardiac signal based on the determined R wave; determining a search window based on one or more of the current RR interval or the previous RR interval; determining a T wave of the cardiac signal in the search window; and determining a QT interval based on the determined T wave and the determined R wave.

[0006] In another example, a non-transitory computer-readable storage medium stores a set of instructions that, when executed, cause a system to determine an R wave of a cardiac signal; determine a previous RR interval of the cardiac signal based on the determined R wave; determine a current RR interval of the cardiac signal based on the determined R wave; determine a search window based on one or more of the current RR interval or the previous RR interval; determine a T wave of the cardiac signal within the search window; and determine a QT interval based on the determined T wave and the determined R wave.

[0007] This summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the systems, apparatus, and methods described in detail in the following drawings and specification. Further details of one or more examples of this disclosure are set forth in the following drawings and specification. Other features, targets, and advantages will become apparent from the description, drawings, and claims. Attached Figure Description

[0008] Figure 1 The patient described the environment of the medical system.

[0009] Figure 2 This is an explanation Figure 1 Functional block diagram of an instance configuration of an insertable cardiac monitor (IMD) for a medical system.

[0010] Figure 3 This is an explanation Figure 1 and 2 A conceptual side view of the ICM instance configuration.

[0011] Figure 4 This is an explanation Figure 1 Functional block diagram of the instance configuration of external devices.

[0012] Figure 5A and 5B This is a conceptual diagram illustrating examples of primary and secondary sensing channels for R-wave and T-wave detectors according to the technology disclosed herein.

[0013] Figure 6 This is a conceptual diagram illustrating an example segment of the EGM signal and its corresponding rectified waveform.

[0014] Figure 7 This is a conceptual diagram illustrating an example segment of an EGM signal with different parameters that can be calculated by the QT detection algorithm according to the technology described in this disclosure.

[0015] Figure 8 This is a conceptual diagram illustrating an example segment of the EGM signal describing the calculation of the window_start and window_end parameters according to the technology described in this disclosure.

[0016] Figure 9 This is a conceptual diagram illustrating an example segment of an EGM signal that depicts the calculation of the window_start and window_end parameters when the difference between the current RR interval and the previous RR interval is greater than 500 milliseconds (ms) according to the technique of this disclosure.

[0017] Figure 10 This is a conceptual diagram illustrating an instance of the MATLAB GUI used to assist in manually annotating datasets.

[0018] Figure 11 This is a concept map illustrating an example of manual annotation.

[0019] Figure 12 This is a conceptual diagram illustrating the difference between manually annotated QT intervals for each heartbeat in the development dataset and QT interval detection results based on the techniques of this disclosure.

[0020] Figure 13 This is a conceptual diagram illustrating the difference between the manually annotated QT correction (QTc) interval for each heartbeat in the development dataset and the QTc interval detection result based on the technology of this disclosure.

[0021] Figure 14 This is a conceptual diagram illustrating the average histogram of the difference between manually annotated QTc intervals for 46 unique devices in the development dataset and QTc interval detection results based on the techniques of this disclosure.

[0022] Figure 15A -D is a conceptual diagram illustrating the description of the technology according to this disclosure, including manual annotation and detection of both from an example EGM bar of the device, wherein the average value of (QTc interval (true) - QTc interval (detection algorithm)) is greater than 25 ms.

[0023] Figure 16 This is a conceptual diagram illustrating an example of manually annotating and detecting EGM bars from a development dataset according to the technique described in this disclosure, wherein the EGM bars are T waves with different morphologies and orientations at different RR intervals.

[0024] Figure 17A -B is a conceptual diagram depicting an instance of an EGM bar from a development dataset according to the technique of this disclosure, the EGM bar depicting beatwise changes in the QTc interval detected by both manual annotation and detection.

[0025] Figure 18A -B is a flowchart illustrating an example of the technique disclosed herein.

[0026] Figure 19This is a flowchart illustrating an example of the technique disclosed herein.

[0027] Figure 20 This is a flowchart illustrating an example of the technique disclosed herein. Detailed Implementation

[0028] This disclosure describes techniques for identifying one or more parameters, such as the QT interval, of cardiac signals. These parameters can be used, for example, to detect or predict arrhythmias, assess other patient conditions such as electrolyte changes, changes in diabetic status, fluid overload, or dehydration, or to configure and / or evaluate therapies, such as pharmacological therapies.

[0029] The T wave represents ventricular repolarization. Ventricular repolarization begins at the epicardial surface of the ventricle and progresses inward through the ventricular wall to the endocardial surface. The T wave occurs during the final phase of ventricular systole. The T wave begins with the first, abrupt, or gradual deviation from the ST segment. The point where the T wave returns to baseline marks the end of the T wave.

[0030] The QT interval on an electrocardiogram (ECG) is measured from the beginning of the QRS complex to the end of the T wave. The QT interval represents the time spent in ventricular depolarization and repolarization, or contraction and relaxation. This electrical activity of the heart is mediated by channels, complex molecular structures within the myocardial cell membrane that regulate the flow of ions inside and outside the myocardial cells. See, for example, Viskin S., “Long QT Syndrome and Torsades de Pointes Ventricular Tachycardia,” The Lancet, Vol. 62(13), pp. 1625–1633, 1999 (hereinafter referred to as Viskin). A rapid influx of positively charged ions (sodium and calcium) leads to normal myocardial depolarization. Myocardial repolarization occurs when potassium ion efflux exceeds this influx. Dysfunction of ion channels leads to an excess of positively charged ions within the cells through insufficient potassium ion efflux or excessive sodium ion influx. This excess of positively charged ions within the cells prolongs ventricular repolarization and results in a prolonged QT interval. See, for example, “QT intervals that clinicians should know,” JAMA, Vol. 289(16), pp. 2120-2127, 2003 (hereinafter referred to as Al-Khatib).

[0031] Abnormally long or short QT intervals are associated with an increased risk of arrhythmias and sudden cardiac death. QT interval abnormalities can be caused by genetic conditions (e.g., long QT syndrome), certain medications (e.g., sotalol or pitostam), disturbances in the concentration of certain salts in the blood (e.g., hypokalemia and hypomagnesemia), hormonal imbalances (e.g., hypothyroidism), or changes in blood glucose. Normal QT intervals vary with age and sex and are typically about 0.36 to 0.44 seconds. Any value greater than or equal to 0.50 seconds may be considered dangerous for any age or sex. See, for example, Cox and Natalie K, “QT Interval: How Long Is Too Long?”, Nursing Becomes Incredibly Simple, Vol. 9(2), pp. 17–21, 2011. The QT interval on ECG has gained clinical importance, primarily because a prolonged QT interval can make individuals susceptible to potentially fatal ventricular arrhythmias, known as torsades de pointes ventricular tachycardia, which can lead to sudden cardiac death, as discussed in Viskin.

[0032] In clinical settings, it is now widely recognized that typical measurements of the QT interval exhibit significant variability, which can affect interpretation. This variability in QT interval measurements is caused by: biological factors, such as diurnal effects, autonomic tone, electrolyte and drug differences; technical factors, including environment, recording processing, and ECG recording acquisition; and intra- and inter-observer variability due to T-wave morphology, noisy baseline, and the presence of U waves. Inter-observer variability also stems from a lack of consensus among experts on standardized methods for measuring the QT interval. See, for example, Al-Khatib; Morganroth J et al., “Variability of QT measurements in healthy men and its influence on the selection of abnormal QT values ​​to predict drug toxicity and arrhythmia,” *American Journal of Cardiology*, Vol. 67(8), pp. 774–776, 1991; and Molnar J et al., “Diurnal patterns of the QTc interval: how long is the prolongation? and its possible relationship with diurnal triggers of cardiovascular events,” *Journal of the American College of Cardiology*, Vol. 27(1), pp. 76–83, 1996.

[0033] The QT interval on ECG has gained clinical importance, primarily because its prolongation can increase susceptibility to a potentially fatal ventricular arrhythmia known as torsades de pointes (TDPT), which can lead to sudden cardiac death. Several factors are associated with QT prolongation and TDPT. One significant risk factor for long QT syndrome is the use of QT-prolonging medications. As described with Al-Khatib, a QT interval greater than 500 ms has been shown to be associated with a higher risk of TDPT.

[0034] Morganroth J et al., “Using ECG to assess and manage cardiac safety in oncology clinical trials: focusing on cardiac repolarization (QTc interval),” Clinical Pharmacology and Therapeutics, Vol. 87(2), pp. 166–74, 2010, suggested that a QT correction (QTc) variation of 10–20 ms be considered clinically relevant, and that patients within this range, especially those with QT-related risk factors, should be protected during treatment through careful ECG assessment. Based on a thorough risk-benefit assessment, the authors suggested that oncology drugs can tolerate a higher tolerance for QTc prolongation effects, as they meet specific medical needs of patients.

[0035] The study by Chouchoulis K et al., "The Effect of QT Interval Prolongation Following Antiarrhythmic Drug Therapy on Left Ventricular Function," *Future Cardiology*, Vol. 13(1), 2016, assessed whether QT interval prolongation induced by antiarrhythmic drugs affects left ventricular function. The study population included 54 patients with recently symptomatic atrial fibrillation whose spontaneous rhythm converted to sinus rhythm. After administration of sotalol (from 424±40 ms to 460±57 ms and from 446±35 ms to 474±48 ms, respectively, both p<0.01) and amiodarone (from 437±41 ms to 504±39 ms and from 469±35 ms to 527±50 ms, respectively, both p<0.01), significant prolongations were found in both the maximum QT value (QTmax) and the QTc interval. Therefore, in this study, QTc was significantly prolonged after administration of antiarrhythmic drugs such as sotalol and amiodarone.

[0036] Multiple studies have also shown the association between QT changes and diabetes. Compared with the general population, type 1 diabetes may increase the risk of death, mainly due to the increased risk of cardiovascular disease. Nearly half of patients with type 1 diabetes have a prolonged QTc interval (>440 ms). According to Rossing P et al., “QTc interval prolongation predicts mortality in patients with type 1 diabetes,” Diabetes Medicine, Vol. 18(3), pp. 199-205, 2001, diabetes with a prolonged QTc interval was associated with a 29% mortality rate within 10 years, compared to 19% with a normal QTc interval.

[0037] A study has shown that QTc dispersion is an important predictor of cardiac mortality. In the Rotterdam Study (deBruyne MC et al., “QTc dispersion predicts cardiac mortality in older adults: The Rotterdam Study,” Circulation, Vol. 97(5), pp. 467–472, 1998), individuals in the highest tertiary of QTc dispersion (>60 ms) had a twice the risk of cardiac death compared to those in the lowest tertiary (<39 ms). The Rotterdam Study also showed that, according to Marfella et al., “QTc dispersion, hyperglycemia, and hyperinsulinemia,” Circulation, Vol. 100(25), 1999 (hereinafter referred to as Marfella), diabetic patients had greater QTc dispersion than non-diabetic patients.

[0038] Marfella evaluated the effects of acute hyperglycemia on QTc duration and QTc dispersion in 27 healthy subjects. During glucose clamp administration, blood glucose stabilized at 15 mmol / L, and plasma insulin exhibited a biphasic response pattern, with an early increase at 10 minutes (327 ± 89 pmol / L) followed by a gradual and sustained increase (456 ± 120 pmol / L). At the end of clamp administration, QTc increased from 413 ± 26 ms to 442 ± 29 ms (P < 0.05), and QTc dispersion increased from 32 ± 9 ms to 55 ± 12 ms (P < 0.01). This indicates that acute hyperglycemia in healthy subjects leads to a significant increase in QTc and QTc dispersion.

[0039] Lee SP et al., “Effects of autonomic neuropathy during hypoglycemia in type 1 diabetes on QTc interval prolongation,” Diabetes, Vol. 53(6), pp. 1535–42, 2004, discusses a study of 28 adults with type 1 diabetes and 8 non-diabetic controls. QTc was then measured using a hyperinsulin clamp during controlled hypoglycemia (2.5 mmol / L). The mean (+ / - SE) QTc in the diabetic participants (BRS+ subjects) prolonged from 377+ / -9 ms (baseline) to a maximum of 439+ / -13 ms during hypoglycemia, while the mean (+ / - SE) QTc in the controls prolonged from 378+ / -5 ms to 439+ / 10 ms. This study suggests that hypoglycemia leads to ECG QTc prolongation, a predictor of arrhythmia risk and sudden death.

[0040] Therefore, continuous monitoring of the QT interval can identify long QT intervals, which may indicate the need for medical intervention. In some instances, continuous monitoring of the QT interval can be performed using an insertable cardiac monitor (ICM). This disclosure describes an example algorithm that can be used with an ICM, such as the LINQ manufactured by Medtronic Ltd. in Dublin, Ireland. TM ICM monitoring of QT interval.

[0041] Various types of medical devices sense cardiac EGM. Some medical devices for sensing cardiac EGM are non-invasive, such as those using multiple electrodes placed in contact with external parts of the patient, such as at various locations on the patient's skin. As an example, electrodes used for monitoring cardiac EGM in these non-invasive procedures can be attached to the patient using adhesive, tape, belt, or vest, and electrically coupled to a monitoring device, such as an electrocardiograph, Holter monitor, or other electronic device. The electrodes are configured to sense electrical signals associated with the electrical activity of the patient's heart or other cardiac tissue, and these sensed electrical signals are provided to electronic devices for further processing and / or display of the electrical signals. Non-invasive devices and methods can be used on a temporary basis, such as to monitor the patient during a clinical visit, such as during a physician appointment, or, for example, within a predetermined time period, such as a day (24 hours), or a period of several days.

[0042] External devices that can be used for non-invasive sensing and monitoring of cardiac EGM include wearable devices, such as patches, watches, or necklaces, with electrodes configured to contact a patient's skin. An example of a wearable physiological monitor configured to sense cardiac EGM is the SEEQ, commercially available from Medtronic plc in Dublin, Ireland. TM Mobile cardiac telemetry systems. These external devices facilitate relatively long-term monitoring of patients during normal daily activities and can periodically transmit collected data to network services, such as Medtronic's Carelink. TM network.

[0043] Some implantable medical devices (IMDs) also sense and monitor cardiac EGM. Electrodes used by the IMD to sense cardiac EGM are typically integrated into the IMD housing and / or coupled to the IMD via one or more thin leads. Examples of IMDs for monitoring cardiac EGM include pacemakers and implantable cardioverter defibrillators that can be coupled to intravascular or extravascular leads, and pacemakers with a housing configured for implantation in the heart, which may be leadless. An example of a pacemaker configured for intracardiac implantation is the Micra from Medtronic. TM Transcatheter pacing systems. Some implantable device-controlled devices (IMDs) do not provide treatment, such as implantable patient monitors that sense cardiac EGM. One example of this IMD is the subcutaneously insertable LINQ. TM ICM. This type of IMD facilitates relatively long-term monitoring of patients during normal daily activities and allows for the periodic transmission of collected data to network services, such as Medtronic's Carelink. TM network.

[0044] Although this disclosure discusses techniques for measuring QT intervals via example ICM, any medical device configured to sense cardiac EGM via implanted or external electrodes (including the examples identified herein) can implement the techniques of this disclosure for measuring QT intervals. These techniques involve assessing cardiac EGM using criteria configured to provide the desired sensitivity and specificity for QT interval detection, despite noise and depolarization morphological variations due to different electrode locations. The techniques of this disclosure for identifying QT intervals can facilitate the determination of cardiac health and risk of sudden cardiac death, and can lead to clinical interventions that suppress the risk of sudden cardiac death.

[0045] Figure 1 An example medical system 2 for patient 4 is illustrated, combining one or more technologies according to this disclosure. The example technologies can be used with an ICM 10, which can be integrated with an external device 12 and... Figure 1 At least one of the other devices not depicted communicates wirelessly. In some instances, the ICM 10 is implanted outside the thoracic cavity of patient 4 (e.g., subcutaneously). Figure 1 The ICM 10 can be positioned near the sternum at or just below the patient's heart level, for example, at least partially within the heart's outline. The ICM 10 contains multiple electrodes (as described in the text). Figure 1 (Not shown in the image), and is configured to sense cardiac EGM via multiple electrodes. In some instances, the ICM 10 employs LINQ. TM In the form of ICM, or similar to, for example, LINQ TM Another form of ICM, either a version or a modified version.

[0046] External device 12 may be a computing device having a user-viewable display and an interface (i.e., a user input mechanism) for providing input to external device 12. In some instances, external device 12 may be a laptop computer, tablet computer, workstation, one or more servers, cellular phone, personal digital assistant, or another computing device that can run an application that enables the computing device to interact with ICM 10. External device 12 is configured to communicate wirelessly with ICM 10 and optionally another computing device. Figure 1 (Not specified in the text) Communication. For example, external device 12 can communicate via near-field communication technology (e.g., inductive coupling, NFC, or other communication technology that can operate within a range of less than 10 to 20 cm) and far-field communication technology (e.g., according to 802.11 or...). RF telemetry of the specification set, or other communication technologies that can operate within a range greater than near-field communication technologies, can be used for communication.

[0047] External device 12 can be used to configure the operating parameters of ICM 10. External device 12 can be used to retrieve data from ICM 10, such as QT intervals. The retrieved data may include values ​​of physiological parameters measured by ICM 10, indications of arrhythmias or other disease episodes detected by ICM 10, and physiological signals recorded by ICM 10. For example, external device 12 can retrieve information related to ICM 10's detection of QT intervals, such as the average, median, minimum, maximum, range, or mode of QT intervals over a period of time. The time period can be predetermined, such as hourly, daily, or weekly, or it can be additionally based on the time sequence of the last information retrieved by external device 12, or it can be determined by the user of external device 12, for example, by entering a command on external device 12 to request information from ICM 10. External device 12 can also retrieve electrocardiogram (EGM) segments recorded by ICM 10, for example, when ICM 10 determines that an episode of arrhythmia or other disease occurs during said segment, or in response to a request to record segments from patient 4 or another user.

[0048] For example, the processing circuitry of medical system 2, including ICM 10, external device 12, and / or one or more other computing devices, can be configured to perform the exemplary techniques of this disclosure for determining the QT interval. In some instances, the processing circuitry of medical system 2 can analyze the cardiac EGM sensed by ICM 10 to determine the QT interval within the cardiac EGM. Although described in the context of instances where the ICM 10 sensing the cardiac EGM includes an insertable cardiac monitor, exemplary systems comprising one or more implantable or external devices of any type configured to sense the cardiac EGM can be configured to implement the techniques of this disclosure.

[0049] Figure 2 This describes one or more technologies based on the techniques described herein. Figure 1 A functional block diagram of an example configuration of ICM 10 is provided. In the illustrated example, ICM 10 includes electrodes 16A and 16B (collectively referred to as "electrodes 16"), antenna 26, processing circuitry 50, sensing circuitry 52, communication circuitry 54, storage device 56, switching circuitry 58, and sensor 62. Although the illustrated example includes two electrodes 16, in some examples, an IMD including or coupled to more than two electrodes 16 may implement the techniques of this disclosure.

[0050] Processing circuitry system 50 may include fixed-function circuitry systems and / or programmable processing circuitry systems. Processing circuitry system 50 may include any one or more of the following: microprocessors, controllers, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or equivalent discrete or analog logic circuitry systems. In some instances, processing circuitry system 50 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry systems. The functionality attributed to processing circuitry system 50 herein may be embodied in software, firmware, hardware, or any combination thereof.

[0051] Sensing circuitry 52 can be selectively coupled to electrode 16 via switching circuitry 58, for example, to select electrode 16 for sensing cardiac EGM and its polarity, referred to as the sensing vector, as controlled by processing circuitry 50. Sensing circuitry 52 can sense signals from electrode 16, for example, to generate cardiac EGM, in order to monitor the electrical activity of the heart. As an example, sensing circuitry 52 can also monitor signals from sensor 62, which may include one or more accelerometers, pressure sensors, and / or optical sensors. In some instances, sensing circuitry 52 may include one or more filters and amplifiers for filtering and amplifying signals received from electrode 16 and / or sensor 62.

[0052] Sensing circuitry 52 and / or processing circuitry 50 can be configured to detect R-waves and T-waves. In some instances, sensing circuitry 52 may include one or more rectifiers, filters, amplifiers, comparators, and / or analog-to-digital converters. In some instances, sensing circuitry 52 may output an indication to processing circuitry 50 in response to sensing an R-wave or T-wave. In some instances, processing circuitry 50 may determine the R-wave or T-wave from the indication received from sensing circuitry 52. ​​Processing circuitry 50 may use the indication of detected R-waves and T-waves to determine or correct the QT interval (QTc).

[0053] The sensing circuitry 52 can also provide one or more digitized cardiac EGM signals to the processing circuitry 50 for analysis, such as for rhythm differentiation, and / or for analysis to determine the QT interval or QTc interval according to the techniques of this disclosure. In some instances, the processing circuitry 50 can store the digitized cardiac EGM in a storage device 56. The processing circuitry 50 of the ICM 10 and / or the processing circuitry of another device retrieving data from the ICM 10 can analyze the cardiac EGM to determine the QT interval or QTc interval according to the techniques of this disclosure.

[0054] The communication circuitry 54 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device such as external device 12, another networked computing device, or another IMD or sensor. Under the control of the processing circuitry 50, the communication circuitry 54 can receive downlink telemetry from external device 12 or another device and send uplink telemetry to it via internal or external antennas, such as antenna 26. Additionally, the processing circuitry 50 can communicate with external devices (e.g., external device 12) and, for example, Medtronic. The network of computers communicates with the networked computing device. Antenna 26 and communication circuitry 54 can be configured to communicate via inductive coupling, electromagnetic coupling, near-field communication (NFC), radio frequency (RF) communication, WiFi or other proprietary or non-proprietary wireless communication solutions are used to transmit and / or receive signals.

[0055] In some instances, storage device 56 contains computer-readable instructions that, when executed by processing circuitry system 50, cause ICM 10 and processing circuitry system 50 to perform various functions categorized herein. Storage device 56 may contain any volatile, non-volatile, magnetic, optical, or electrical medium, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other digital medium. As an example, storage device 56 may store programmed values ​​of one or more operating parameters of ICM 10 and / or data collected by ICM 10 for transmission to another device using communication circuitry system 54. As an example, data stored by storage device 56 and transmitted by communication circuitry system 54 to one or more other devices may include quantification of ventricular premature beats (PVCs) detection and / or digitization of cardiac EGM.

[0056] Figure 3 This is an explanation Figure 1 and 2 A conceptual side view of an ICM 10 instance configuration. Figure 3 In the example shown, the ICM10 may comprise a leadless, subcutaneously implantable monitoring device having a housing 15 and an insulating cover 76. Electrodes 16A and 16B may be formed or placed on the outer surface of the cover 76. (The above refers to...) Figure 2The described circuit systems 50-62 can be formed or placed on the inner surface of the cover 76 or within the housing 15. In the illustrated example, the antenna 26 is formed or placed on the inner surface of the cover 76, but in some examples, it can be formed or placed on the outer surface. In some examples, one or more sensors 62 can be formed or placed on the outer surface of the cover 76. In some examples, the insulating cover 76 can be positioned over the open housing 15 such that the housing 15 and the cover 76 surround the antenna 26 and the circuit systems 50-62, and protect the antenna and circuit systems from fluids (such as bodily fluids).

[0057] One or more of antenna 26 or circuit systems 50 to 62 may be formed on the inside of insulating cover 76, for example, using flip-chip technology. Insulating cover 76 may be flipped onto housing 15. When flipped and placed onto housing 15, components of ICM 10 formed on the inside of insulating cover 76 may be located in the gap 78 defined by housing 15. Electrode 16 may be electrically connected to switching circuit system 58 through one or more through-holes (not shown) formed in insulating cover 76. Insulating cover 76 may be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. Housing 15 may be formed of titanium or any other suitable material (e.g., biocompatible material). Electrode 16 may be formed of stainless steel, titanium, platinum, iridium, or alloys thereof. Furthermore, electrode 16 may be coated with a material such as titanium nitride or fractal titanium nitride, but other suitable materials and coatings for such electrodes may be used.

[0058] Figure 4 This is a block diagram illustrating an example configuration of the components of external device 12. Figure 4 In one example, the external device 12 includes a processing circuit system 80, a communication circuit system 82, a storage device 84, and a user interface 86.

[0059] The processing circuitry system 80 may include one or more processors configured to implement functions and / or processing instructions for execution within the external device 12. For example, the processing circuitry system 80 may be capable of processing instructions stored in the storage device 84. The processing circuitry system 80 may include, for example, a microprocessor, DSP, ASIC, FPGA, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuits. Therefore, the processing circuitry system 80 may include any suitable structure, whether hardware, software, firmware, or any combination thereof, to perform the functions of the processing circuitry system 80 described herein.

[0060] The communication circuitry 82 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as ICM 10. Under the control of the processing circuitry 80, the communication circuitry 82 can receive downlink telemetry from ICM 10 or another device, and send uplink telemetry to it. The communication circuitry 82 may be configured to communicate via inductive coupling, electromagnetic coupling, near-field communication (NFC), radio frequency (RF) communication, The communication circuit system 82 can also be configured to communicate with devices other than ICM 10 via any of a variety of wired and / or wireless communication and / or network protocols.

[0061] Storage device 84 can be configured to store information within external device 12 during operation. Storage device 84 may comprise a computer-readable storage medium or a computer-readable storage device. In some instances, storage device 84 comprises one or more of short-term or long-term memory. Storage device 84 may comprise, for example, RAM, DRAM, SRAM, magnetic disk, optical disk, flash memory, or various forms of EPROM or EEPROM. In some instances, storage device 84 is used to store data indicating instructions executed by processing circuitry system 80. Storage device 84 can be used by software or applications running on external device 12 to temporarily store information during program execution.

[0062] The data exchanged between the external device 12 and the ICM 10 may include operating parameters. The external device 12 may transmit data containing computer-readable instructions that, when implemented by the ICM 10, can control the ICM 10 to change one or more operating parameters and / or export collected data, such as the QT interval or QTc interval. For example, the processing circuitry 80 may transmit an instruction to the ICM 10 requesting it to export collected data (e.g., QT interval data, QTc interval data, and / or digitized cardiac EGM) to the external device 12. Furthermore, the external device 12 can receive the collected data from the ICM 10 and store it in a storage device 84. The processing circuitry 80 may implement any of the techniques described herein to analyze the cardiac EGM received from the ICM 10, for example, to determine the QT interval or QTc interval.

[0063] Users such as clinicians or patients can interact with external devices 12 through user interface 86. User interface 86 includes a display (not shown), such as a liquid crystal display (LCD) or a light-emitting diode (LED) display or other type of screen, through which processing circuitry 80 can present information related to the ICM 10, such as indications of cardiac EGM, QT interval, or QTc interval. Additionally, user interface 86 may include an input mechanism for receiving input from the user. The input mechanism may include, for example, buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, or a touchscreen, or another input mechanism that allows the user to navigate the user interface presented by processing circuitry 80 of external device 12 and provides input. In other instances, user interface 86 may also include an audio circuitry for providing auditory notifications, instructions, or other sounds to the user, receiving voice commands from the user, or both.

[0064] Figure 5A and 5B This is a conceptual diagram illustrating the primary and secondary sensing channels used in R-wave and T-wave detection. Figure 5A In the middle, the sensing circuit system 52 of ICM 10 can be used Figure 5A and 5B The dual-channel sensing technology senses the R-wave. The cardiac signal (e.g., signals from electrodes 16A and 16B) can be filtered by a bandpass filter 100. In some instances, the bandpass filter 100 may have a passband in the range of approximately 10 Hz to 32 Hz. In some instances, the bandpass filter 100 may have a non-linear response as shown. In other instances, the bandpass filter 100 may have a substantially linear response. The bandpass signal can then be rectified by a rectifier 102.

[0065] The rectified signal can then be input to the automatic threshold adjustment process 104. For example, when the amplitude of the rectified signal from rectifier 102 exceeds the automatic threshold, the automatic threshold adjustment process can sense an event that has occurred in the cardiac signal. The automatic threshold adjustment process 104 can use an automatic adjustment sensitivity with a short blanking period (e.g., approximately 150 ms). During the blanking period, sensing processes such as the automatic threshold adjustment process 104 or the fixed threshold process 106 may not sense events in the cardiac signal to avoid a single depolarization generating multiple sensed events. The automatic threshold adjustment process 104 can form a primary sensing channel 108, which can be the primary R-wave sensing mechanism in the ICM 10 and is configured to accommodate the detection of both tachyarrhythmias and bradycardias.

[0066] Once the primary sensing channel 108 detects an R-wave, the threshold of the automatic thresholding process 104 is set at 65% of the amplitude of the detected R-wave (this can be a relatively high threshold so that the R-wave is not detected immediately). The threshold then decays from the 65% value to 35 microvolts, allowing the next R-wave to be detected. In some instances, there may be a point where the threshold drops sharply, such as after the expected T-wave and P-wave, to avoid oversensing T-waves and / or P-waves.

[0067] In some instances, the rectified signal can be input into a fixed threshold process 106. The fixed threshold process 106 can have a fixed threshold and a relatively longer blanking period (e.g., approximately 520 ms) compared to the automatic threshold adjustment process 104 to reduce undersensitivity. Similar to the automatic threshold adjustment process 104, the fixed threshold process 106 can sense events in the cardiac signal when the amplitude of the rectified signal exceeds a fixed threshold. The output of the fixed threshold process 106 can form an auxiliary sensing channel 110. In other instances (not shown), the auxiliary sensing channel 110 may use different filtering and / or different rectification than the primary sensing channel 108.

[0068] Figure 5A and 5B The example dual-channel sensing scheme can be used to avoid undersensitivity of certain R waves, such as those in a PVC heartbeat. To capture these heartbeats, an auxiliary channel can be used, such as auxiliary sensing channel 110 used with a lower threshold.

[0069] For example, when the primary sensing channel 108 senses an R-wave, it can blank the automatic threshold adjustment process 104 and the fixed threshold process 106 for a period of time, such as 150 ms, to prevent the auxiliary sensing channel 110 from sensing the same heartbeat. If the auxiliary sensing channel 110 senses an R-wave that was not sensed by the primary sensing channel 108, it can blank the fixed threshold process 106 for 520 ms after the R-wave is sensed. In this example, the auxiliary sensing channel 106 may not blank the primary channel during sensing.

[0070] To determine the T-wave position, the ICM 10 can, for example, use a bandpass filter 90 to pass through the EGM signals from electrodes 16A and 16B. In some instances, the bandpass filter can be a 6 to 20 Hz bandpass filter. The bandpass signal can be rectified by rectifier 92. Figure 5B In this process, the main sensing channel 108 and the auxiliary sensing channel 110 determine the R-wave sensing 95. The T-wave sensor 94 can use the R-wave sensing 95 to determine the search window for the T-wave.

[0071] Figure 6 This is a conceptual diagram illustrating an example segment of the EGM signal 105 and the corresponding rectified waveform 107.

[0072] According to the technology disclosed herein, ICM 10 can determine a window following the QRS complex to search for the T wave. ICM 10 can determine the window based on one or more of the current RR interval (the time between two consecutive R waves) and the RR interval of the previous heartbeat (the previous RR interval). To accurately determine the start and end samples of the search window, ICM 10 can determine the R-wave peak sample in the rectified signal (e.g., in the primary sensing channel 108 and / or the auxiliary sensing channel 110). To determine the R-wave peak in the rectified signal, the processing circuitry system 50 of ICM 10 can acquire a first predetermined number of samples, such as 14 samples, before the sensed R wave, and a second predetermined number of samples, such as 25 samples, at a predetermined frequency (e.g., 256 Hz) after the sensed R wave, and determine that the sample with the maximum amplitude is the R-wave peak sample in the rectified signal. In some instances, the first predetermined number of samples and the second predetermined number of samples can be the same. In other instances, the first predetermined number of samples and the second predetermined number of samples can be different. ICM 10 can use this technique to determine the peak R-wave samples of the current, previous, and next heartbeats as parameters peak_current_sample, peak_previous_sample, and peak_next_sample, respectively, in order to obtain the current and previous RR intervals. ICM can determine the peak R-wave samples of the current, previous, and next heartbeats to improve the accuracy of the window determination process in which the T-wave location is searched.

[0073] After identifying the R-wave peak sample, ICM 10 can determine four parameters used to determine the T-wave search window. ICM 10 can determine the parameters `window_start_current` and `window_end_current` based on the current RR interval between the current and next heartbeat. Similarly, ICM 10 can determine the parameters `window_start_previous` and `window_end_previous` based on the previous RR interval between the current and previous heartbeats. Tables 1 and 2 below show examples of determining these four parameters based on the current and previous RR intervals.

[0074] Current RR interval (sampled at 256Hz) Window_start_current parameter Window_end_current parameter <＝75 27 20 >=75 and <=105 29 25 >105 and <=125 40 40 >125 and <150 45 55 >=150 and <170 50 60 >=170 and <190 55 65 >=190 and <220 55 75 >=220 and <240 60 80 >=240 and <260 60 85 >=260 and <300 70 90 >=300 and <350 70 95 >＝350 75 100

[0075] Table 1 - Determination of window_start_current and window_end_current parameters based on the current RR interval

[0076] Current RR interval (sampled at 256Hz) Window_start_current parameter Window_end_current parameter <＝75 27 20 >=75 and <=105 29 25 >105 and <=125 40 40 >125 and <150 45 55 >=150 and <170 50 60 >=170 and <190 55 65 >=190 and <220 55 75 >=220 and <240 60 80 >=240 and <260 60 85 >=260 and <300 70 90 >=300 and <350 70 95 >＝350 75 100

[0077] Table 2 - The parameters window_start_previous and window_end_previous are determined based on the previous RR interval.

[0078] After determining these parameters, the window length is determined based on the previous RR interval using the following terms:

[0079] window_start1=peak_previous_sample+window_start_previous

[0080] window_end1=peak_current_sample-window_end_previous

[0081] previous_window_length=window_end1-window_start1

[0082] Similarly, the length of the window based on the current RR interval is determined by the following:

[0083] window_start2=peak_current_sample+window_start_current

[0084] window_end2=peak_next_sample-window_end_current

[0085] current_window_length=window_end2-window_start2

[0086] Figure 7 This is a conceptual diagram illustrating example segments of EGM signals with different parameters calculated by the QT detection algorithm according to the technology of this disclosure. Figure 7 In this example, ICM 10 can determine the start and end samples of the search window for the T wave used for the current heartbeat using the following:

[0087] window_start=peak_current_sample+window_start_previous

[0088] window_end=window_start+previous_window_length.

[0089] Figure 8This is a conceptual diagram illustrating instance fragments of EGM signals that depict the defined parameters of window_start and window_end. It can be initially based on, for example... Figure 8 The preceding RR interval shown determines the start and end samples for the search window. For example, ICM 10 can determine whether the condition (window_end > peak_next_sample - a_predetermined_number_of_samples (e.g., 60)) is met, and if the condition is met, ICM can set the parameter window_end to:

[0090] window_end = window_end2.

[0091] ICM 10 allows setting the parameter `window_end = window_end2` to ensure that the P wave of the next heartbeat is not incorrectly identified as a T wave. Therefore, if the end of the window determined using the previous RR interval is close to the QRS complex of the next heartbeat, the end of the window can be set based on the current RR interval, rather than the previous RR interval.

[0092] If the difference between the previous RR interval and the current RR interval is greater than a third predetermined number of samples, such as multiple samples, for example, 128 samples at 256Hz (500ms), then ICM 10 can set the window_start and window_end parameters to:

[0093] window_start = window_start2

[0094] window_end = window_end2

[0095] The third predetermined number of samples can be any number of samples, for example, 102 samples at 256 Hz (400 ms); 154 samples at 256 Hz (600 ms); or 205 samples at 256 Hz (800 ms). In some instances, the frequency may be different from 256 Hz.

[0096] Figure 9 This is a conceptual diagram illustrating an example segment of an EGM signal used to calculate the window_start and window_end parameters when the difference between the current RR interval and the previous RR interval is greater than 500 ms (128 samples at 256 Hz). If the difference between the previous RR interval and the current RR interval is greater than a third predetermined number of samples, such as 128 samples (500 ms), then ICM 10 can determine the window start and end samples based on the current RR interval, rather than the previous RR interval, to obtain... Figure 9 The accuracy shown is similar to that of previous heartbeats. Similarly, if a previous heartbeat is determined to be noisy, ICM 10 can determine the start and end points of the sampling window based on the current RR interval, rather than the previous RR interval.

[0097] ICM 10 can determine the search window for the T-wave, starting at the `window_start` sample and ending at the `window_end` sample. To determine the location of the T-wave, for each sample in the window, a fourth predetermined number of samples can be acquired, such as the median of 9 samples (e.g., 4 samples before and 4 samples after a given sample at 256 Hz). ICM 10 can determine the sample with the maximum median as the location of the T-wave. In some instances, ICM 10 can calculate the QT interval from the `peak_current_sample` to this maximum median sample. For example, processing circuitry 50 can determine the QT interval, which determines the time or number of samples between the `peak_current_sample` and the maximum median sample.

[0098] Normal cardiac repolarization adapts to heart rate. This phenomenon means that as heart rate increases, the myocardium remains continuously excited until the next depolarization wave occurs, such as complete repolarization. This prevents incomplete repolarization and the possibility of subsequent reentrant tachycardia. In long QT syndrome, the heart's adaptation to heart rate changes is disrupted, thereby promoting arrhythmias. See Postema PG et al., "Measurement of the QT interval," Current Cardiology Review, Vol. 10(3), pp. 287–294, 2014.

[0099] The QT interval depends on the RR interval and is longer when the heart rate is slower and shorter when the heart rate is faster. Therefore, ICM 10 can calculate the corrected QT interval (QTc). Using QTc can improve the detection of patients at increased risk of ventricular arrhythmias. Three methods are commonly used to calculate the QTc interval—the Bazett formula, the Fridericia formula, and the Framingham formula:

[0100] Bazett's formula: QTc = QT / √RR

[0101] Fridericia's formula: QTc=QT / RR 1 / 3

[0102] Framingham formula: QTc=QT+0.154(1-RR)

[0103] Although the Bazett formula (logarithmic correction) is the most commonly used QT correction formula, it is not optimal outside the 60-100 bpm heart rate range. It overcorrects at heart rates greater than 100 bpm and undercorrects at heart rates less than 60 bpm. Both the Friderica and Framingham formulas perform better for heart rates outside the 60-100 range. One study showed that the Friderica and Framingham correction formulas showed better rate correction and significantly improved prediction of 30-day and 1-year mortality when compared to the Bazett formula. See Vandenberk B et al., “Which QT correction formulas are used for QT monitoring?”, *Journal of the American Heart Association*, Vol. 5(6), 2016. For example, the Framingham formula (linear correction formula) can be used to calculate the QTc interval in ICM 10. In other instances, the Friderica formula can be used in ICM 10. In other instances, the Bazett formula can be used in ICM 10. In other instances, ICM 10 can employ several other formulas or techniques to determine the QTc interval. In some instances, ICM 10 can use more than one of the Framingham, Friderica, or Bazett formulas. In such instances, ICM 10 can determine the mean, median, or mode QTc based on the formula used.

[0104] A QT detection algorithm that can be implemented in the ICM 10 has been developed using real-world clinical data from an unidentified Medtronic CareLink™ data warehouse. The algorithm was developed in March 2014 during a one-year follow-up of patients with implanted ICMs (such as the ICM 10) using 74 nocturnal spreading episodes (each 10 seconds long) and 70 patient activation episodes (30-second EGM fragments) from patients with diabetes and long QT syndrome, for use as an indication for syncope (revealing LINQ). TM The developed dataset contains over 3,800 heartbeats from more than 45 patients for analysis. This dataset provides T-wave morphology at different locations and orientations from the ICM.

[0105] After extracting the EGM from the patient's activated seizures, R waves are sensed by running an algorithm according to the techniques of this disclosure. Primary and secondary event markers are used to manually annotate T wave locations to obtain manual data. The algorithmic results are compared with the manually annotated T wave locations to evaluate the performance of the QT detection algorithm.

[0106] Figure 10This is a conceptual diagram illustrating an instance GUI used to assist in manually annotating a dataset. The EGM file to be annotated and algorithmically generated markers can be input into the GUI. The GUI can display heartbeats where the user should mark the T-wave locations. For each R-wave marker 112 displayed on the GUI, the user can select the location of the T-wave 114 by clicking. Additionally, the user can assign annotations to each heartbeat: 1) normal heartbeat; 2) noisy T-wave; or 3) incorrect R-wave marker.

[0107] Figure 11 This is a conceptual diagram illustrating an example of manual annotation. After annotating all R-waves on the GUI window (e.g., adding R-wave marker 112), the user can press the input button to continue to the next group of R-waves that needs annotation. Figure 11 Annotation 1 shows a normal heartbeat, annotation 2 shows a noisy heartbeat, and annotation 3 shows an incorrect heartbeat with R wave 116.

[0108] Figure 12 This is a conceptual diagram illustrating the histogram of the difference between manually annotated QT intervals for each heartbeat in a development dataset and QT intervals calculated based on the detected QT intervals of this disclosure, according to the technology of this disclosure. To determine the performance of the QT interval detection technology of this disclosure, for example... Figure 12 The heartbeat calculations in the development dataset shown are based on the difference (QT(true) - QT(detected)) between the manually annotated QT interval and the QT interval calculated using the detection technique of this disclosure. The mean absolute value of this parameter is 22.7 ms and the median is 11.7 ms. The mode was found to be 4 ms. It should be noted that the resolution of the QT calculation is 4 ms because the EGM data is at 256 Hz.

[0109] Table 3 below shows the percentage of heartbeats in the development data corresponding to the absolute value of the parameter (QT(real) - QT(detected)) for each heartbeat in the development dataset, in terms of both samples and ms, where the total number of heartbeats is 3829.

[0110]

[0111]

[0112]

[0113] Table 3 - Percentage of heartbeats in the development dataset corresponding to the difference between manually annotated QT intervals and algorithmically detected QT intervals, in terms of both sample number at ms and 256Hz.

[0114] Figure 13This is a conceptual diagram illustrating the histogram of the difference between the manually annotated QTc interval for each heartbeat in the development dataset and the QTc interval calculated based on the detection technique of this disclosure. It can be... Figure 13 As observed in Table 4 below, for 46.5% of heartbeats in the development dataset, the difference between the manually annotated QT interval and the QT interval based on the detection technique of this disclosure is less than or equal to 2 samples (7.8 ms). For more than 78% of heartbeats in the development dataset, this parameter is less than 25 ms.

[0115]

[0116] Table 4 - Percentage of heartbeats in the development dataset corresponding to the difference (in ms) between manually annotated QTc intervals and algorithmically detected QTc intervals.

[0117] The QTc interval was calculated for each heartbeat in the development dataset using the Framingham correction formula. Similar to... Figure 12 For example, for each heartbeat in the development dataset, the difference between the QTc interval calculated based on manual annotation and the QTc interval based on the detection technique of this disclosure is calculated. Figure 13 The histogram for this parameter is shown below. The mean of the absolute values ​​of this parameter is 22.6 ms, and the median is 12 ms. The mode is found to be 4 ms.

[0118] Figure 14 This is a conceptual diagram illustrating the average histogram of the difference between manually annotated QTc intervals and the QTc intervals based on the detection technique of this disclosure for 46 unique devices in the development dataset. To determine the performance of the technique of this disclosure based on unique devices, a parameter of (QTc interval (real) - QTc interval (detected algorithm)) is calculated individually for heartbeats in each unique ICM, and the average of this parameter is calculated for each device. Figure 14 It was observed that 37 out of 46 devices had an average duration of less than 25 ms.

[0119] Figure 15A -D is a conceptual diagram illustrating instance devices where the average value of (QTc interval (real) - QTc interval (detected algorithm)) is greater than 25 ms. There are 9 devices with an average value greater than 25 ms. Figure 15A The -D directive shows some instances from these specific ICMs. Among several of these ICMs, such as... Figure 15A As shown and in Figure 15B In some cases, the techniques of this disclosure detect T waves at different locations when compared with manual annotation, but T waves are generally detected consistently at the same locations. For example, in Figure 15A and15C In the sequence, the R-wave marker 112 is always followed by the T-wave 118, which is determined manually, and then by the T-wave 120, determined according to the detection technique of this disclosure. This pattern continues throughout the entire sequence. Figure 15A and 15C The process continues. Therefore, even if the average value of (QTc interval (real) - QTc interval (detected algorithm)) is large in these devices, changes in the QT interval can still be measured in these cases.

[0120] In some ICMs, for example Figure 15D As shown, the detection of the T wave varies between heartbeats, which in some cases is due to noise between heartbeats or changes in the RR interval.

[0121] Figure 16 This is a conceptual diagram illustrating instances of manually annotating and detecting EGM bars from a development dataset according to the techniques described in this disclosure, where the EGM bars exhibit different morphologies and orientations of T waves at different RR intervals. These instances include nocturnal transport and patient-activated episodes from ICM, where the algorithm accurately detects the QT interval.

[0122] Another observation from the analysis of the development dataset is that ICM is able to capture beat-by-beat variations in the QT interval very well, such as... Figure 17A As shown in -B. In Figure 17A In the middle, QTc changes by 43ms between heartbeat 122 and heartbeat 124, which is well captured by ICM. Similarly, in Figure 17B In the meantime, the QTc interval changes by 54 ms between heartbeats 126 and 128, and by 15 ms between heartbeat 128 and the next heartbeat, which is also captured by ICM 10.

[0123] In some instances, the amplitude of samples within a search window can be weighted by giving more weight (more weighting) to the amplitude of samples most likely to detect a T wave based on previous QT or QTc intervals (e.g., 12 QT or QTc intervals), and less weight to samples at the end of the window. For example, the processing circuitry 50 of the ICM 10 can acquire the last 12 QT or QTc intervals and form a weighted window based on the position of the T wave in the fastest or QTc interval and the position of the T wave in the slowest or QTc interval, applying weights to the amplitude of samples within the window such that the sample amplitude is greater than the original amplitude. For example, the processing circuitry 50 of the ICM 10 can apply weights to the amplitude of samples outside the window such that the sample amplitude is less than the original amplitude. In some instances, the weighted window can be equal to the position of the T wave in the fastest or QTc interval and the position of the T wave in the slowest or QTc interval. In other instances, the weighting window can be larger or smaller than the position of the T wave in the fastest QT or QTc interval, and the position of the T wave in the slowest QT or QTc interval. In some instances, noise detection can be combined by ICM 10 to detect noisy heartbeats by detecting noisy QRS complexes and to determine whether the search window for the T wave following the QRS complex is noisy.

[0124] In some instances, the ICM 10 can apply an amplitude threshold for T-wave detection based on a predetermined number of previous T-wave detections (e.g., 12) to ensure that P-waves or noise are not detected as T-waves. For example, the processing circuitry 50 of the ICM 10 can determine whether the amplitude of a sample in the search window is less than a threshold, and determine that the sample is not a T-wave based on the sample's amplitude being less than the threshold. In some instances, the processing circuitry 50 of the ICM 10 can determine a confidence level for the detected T-wave. For example, the processing circuitry 50 of the ICM 10 can determine the confidence level based on one or more of a predetermined number of previous T-wave amplitudes, QT intervals, or QTc intervals (e.g., 12). If the amplitude of the detected T-wave is too low compared to previous T-waves, or if the QT interval (or QTc interval) is significantly different from the previous 12 QT intervals (or QTc intervals), the ICM 10 can provide a low confidence level for the detected T-wave.

[0125] In some instances, the processing circuitry 50 of the ICM 10 can determine the mean, median, mode, standard deviation, or any other trend of the determined QT interval or QTc interval over time. The ICM 10 can transmit the mean, median, mode, standard deviation, or any other trend of the determined QTc interval to an external device 12. In some instances, the ICM 10 can determine the time or count by which the QT interval or QTc interval is longer than a predetermined threshold. For example, this predetermined threshold could be approximately 500 ms, as a QT interval longer than 500 ms may be associated with a higher risk of torsades de pointes. In some instances, the ICM 10 can determine the time or count by which the QT interval or QTc interval changes beyond a threshold. For example, the ICM 10 can determine the time or count by which the QT interval or QTc interval changes beyond 30 ms or 40 ms or some other threshold (which may even be patient-specific) within a specific time period.

[0126] Figure 18A This is a flowchart illustrating an example of the technology disclosed herein. The sensing circuitry 52 of the ICM 10 can sense cardiac signals (130). In some instances, the sensing circuitry 52 can apply one or more bandpass filters (e.g., bandpass filter 100) or rectifiers (e.g., rectifier 102) to the cardiac signals. In some instances, the sensing circuitry 52 can use a primary sensing channel 108 and an auxiliary sensing channel 110 to sense cardiac signals.

[0127] Sensing circuitry 52 can determine the R wave (132) of the cardiac signal. For example, an automatic threshold adjustment process 104 and / or a fixed threshold process 106 can determine that the rectified signal from rectifier 102 has exceeded the automatic threshold and / or fixed threshold. Processing circuitry 50 of ICM 10 can determine the previous RR interval (134). For example, to accurately determine the previous RR interval, processing circuitry 50 can determine the peak R value (e.g., peak_current_sample and peak_previous_sample) in two consecutive sensed R waves. Processing circuitry 50 can determine the previous RR interval as the time between two consecutive peak R values ​​peak_current_sample and peak_previous_sample. In some instances, processing circuitry 50 can acquire a first predetermined number of samples before the sensed R wave and a second predetermined number of samples at a predetermined frequency after the sensed R wave, and the sample with the maximum amplitude is the R wave peak sample.

[0128] The processing circuitry system 50 of the ICM 10 can also determine the current RR interval (136). For example, to accurately determine the current RR interval, the processing circuitry system 50 can determine the peak R value of the next sensed R wave (e.g., peak_next_sample). The processing circuitry system 50 can determine peak_next_sample in the same or similar manner as peak_current_sample and peak_previous_sample. The processing circuitry system 50 can determine the current RR interval as the time between two consecutive peak R values, peak_next_sample and peak_current_sample.

[0129] The processing circuitry 50 can then determine a search window for searching the T wave based on one or more of the current RR interval or previous RR intervals. The processing circuitry 50 can determine the search window to begin at multiple samples following the R-wave peak and to end at a different number of samples following the R-wave peak. These sample numbers can be based on the length of the current RR interval or previous RR intervals. In some instances, the number of samples can be stored in storage device 56, such as a lookup table. In some instances, the number of samples can be those described above in Tables 1 and 2.

[0130] In some instances, the processing circuitry 50 may use a threshold to help determine the T-wave. In other instances, the processing circuitry may not use a threshold. For example, the processing circuitry 50 may determine whether the amplitude of the sample is less than a threshold (or, in some cases, less than or equal to a threshold) (140). If the amplitude of the sample is less than a threshold (or, in some cases, less than or equal to a threshold) (in... Figure 18A If the sample is identified as a "yes" path, then the processing circuitry 50 can determine that the sample is not a T-wave (142). The processing circuitry 50 can then examine the next sample. If the amplitude of the sample is equal to or greater than a threshold (or, in some cases, greater than a threshold), then the processing circuitry 50 can maintain the sample as a candidate for a T-wave (142). Figure 18A (The "No" path).

[0131] The processing circuitry 50 can determine the T wave (144) of the cardiac signal in the search window. For example, the processing circuitry 50 can determine the highest amplitude sample in the search window as the T wave. In other instances, the processing circuitry 50 can acquire a predetermined number of samples around a given sample and determine the median, mean, or mode, and determine the T wave as the maximum amplitude median, mean, or mode in the search window.

[0132] In some instances, the processing circuitry 50 can determine the confidence level of the T wave (146). In some instances, the processing circuitry 50 may not be able to determine the confidence level of the T wave. For example, the processing circuitry 50 may determine the confidence level based on one or more of a predetermined number of previous T wave amplitudes or QT intervals. For example, if the amplitude of the detected T wave is too low compared to previous T waves, or if the QT interval is very different from the previous 12 QT intervals, the ICM 10 may provide a low confidence level for the detected T wave.

[0133] Figure 18B yes Figure 18A Continuing on, the processing circuit system 50 can determine the QT interval (148) based on the determined T wave and the determined R wave. For example, the processing circuit system 50 can determine the QT interval as the time between the peak value of the R wave and the determined T wave. In other instances, the processing circuit system 50 can determine the QT interval as the number of samples between the peak value of the R wave and the determined T wave. The processing circuit system 50 can also determine the QTc interval (150). For example, the processing circuit system 50 can apply at least one of the Framingham formula, the Friderica formula, or the Bazett formula, or another formula or technique, to the QT interval to determine the QTc interval.

[0134] The processing circuit system 50 can determine the trend of the QT interval or QTc interval over time (152). For example, the processing circuit system 50 can determine the mean, median, mode, standard deviation, or any other trend of the determined QT interval or QTc interval over time.

[0135] Figure 19 It is depicted, for example, as a defined search window ( Figure 18A As part of section 138), the flowchart describes how the processing circuitry system 50 determines the search window based on an instance of a previous RR interval or the current RR interval. Figure 19 In one example, the processing circuit system 50 can determine whether the difference between the current RR interval and the previous RR interval is greater than a predetermined amount (160). For example, the processing circuit system 50 can determine whether the difference between the current RR interval and the previous RR interval is greater than a predetermined time period. In another example, the processing circuit system 50 can determine whether the difference between the current RR interval and the previous RR interval is greater than a predetermined number of samples. In some examples, if the difference between the current RR interval and the previous RR interval is not greater than a predetermined amount (160), the processing circuit system 50 can determine whether the difference between the current RR interval and the previous RR interval is greater than a predetermined number of samples. Figure 19 If the "No" path is selected, the processing circuit system 50 can determine the search window (162) based on the previous RR interval. If the difference between the current RR interval and the previous RR interval is greater than a predetermined amount ( Figure 19If the path is "yes" in the code, then the processing circuit system 50 can determine the search window (164) based on the current RR interval. In some instances, the predetermined amount can be 500 ms. In some instances, the predetermined amount can be 128 samples at 256 Hz.

[0136] Figure 20 It is depicted, for example, as a defined search window ( Figure 18A As part of section 138), the flowchart describes how the processing circuitry system 50 determines the search window based on either a previous RR interval or another instance of the current RR interval. Figure 20 In an example, the processing circuitry system can determine whether a previous heartbeat was noisy (170). For example, ICM 10 can be configured to detect noisy heartbeats. For example, processing circuitry system 50 can determine whether a heartbeat is noisy by determining whether the current R-wave is noisy, or whether the ECG segment between the current R-wave and the next R-wave is noisy, or both. For example, processing circuitry system can determine whether the current R-wave is noisy by defining a window of several samples before the peak of the R-wave and several samples after the peak of the R-wave. Processing circuitry system 50 can determine a noise count by counting the number of samples in the window that have a flag change greater than a first threshold (e.g., 50) or a flag change less than a second threshold (e.g., -50). If this noise count is greater than or equal to the threshold (e.g., 5), processing circuitry system 50 can determine that the heartbeat is noisy. Processing circuitry system 50 can determine whether the segment between the current R-wave and the next R-wave is noisy in a similar manner. In this case, processing circuitry system 50 can define a window that begins several samples after the current R-wave and ends several samples before the next R-wave. The processing circuitry 50 can determine the noise count in a similar manner, but the threshold may be different. If the noise count is greater than or equal to another threshold (e.g., 5 or some other count), the processing circuitry can determine that the heartbeat is noisy.

[0137] If the processing circuit system 50 determines that there was no noise in the previous heartbeat ( Figure 20 If the processing circuitry 50 determines that the previous heartbeat was noisy (in the "No" path), then the processing circuitry 50 can determine the search window (172) based on the previous RR interval. Figure 20 If the path is "yes" in the current interval, then the processing circuit system 50 can determine the search window based on the current interval.

[0138] While the techniques described herein are performed by various elements, such as sensing circuit system 52 and processing circuit system 50, in some instances, other elements or combinations of elements may perform the techniques. For example, sensing circuit system 52 may perform the techniques described as being performed by processing circuit system 50, processing circuit system 50 may perform the techniques described as being performed by sensing circuit system 52, or a combination of sensing circuit system 52 and processing circuit system 50 may perform the techniques described as being performed by either one.

[0139] The techniques described in this disclosure can be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, aspects of these techniques can be implemented in one or more processors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic QRS circuit systems, and any combination of such components embodied in external devices (such as doctor or patient programmers, simulators, or other devices). The terms “processor,” “processing circuit system,” “controller,” or “control module” generally refer to any of the aforementioned logic circuits, either alone or in combination with other logic circuits or any other equivalent circuits, and either alone or in combination with other digital or analog circuits.

[0140] For aspects implemented in software, at least some of the functions of the systems and devices described in this disclosure can be embodied as instructions on a non-transitory computer-readable storage medium (such as RAM, ROM, NVRAM, EEPROM, flash memory, magnetic media, optical media, etc.). These instructions can be executed to support one or more aspects of the functions described in this disclosure.

[0141] This disclosure includes the following non-limiting examples.

[0142] Example 1. An apparatus comprising: one or more electrodes; a sensing circuit system configured to sense a cardiac signal via the one or more electrodes; and a processing circuit system configured to: determine an R wave of the cardiac signal; determine a previous RR interval of the cardiac signal based on the determined R wave; determine a current RR interval of the cardiac signal based on the determined R wave; determine a search window based on one or more of the current RR interval or the previous RR interval; determine a T wave of the cardiac signal within the search window; and determine a QT interval based on the determined T wave and the determined R wave.

[0143] Example 2. The apparatus according to Example 1, wherein the processing circuitry is further configured to: determine the QTc interval.

[0144] Example 3. The apparatus according to Example 2, wherein the processing circuitry is configured to determine the QTc interval by applying at least one of the Framingham formula, Friderica formula, Bazett formula, or other formulas or techniques to the QT interval.

[0145] Example 4. The apparatus according to any combination of Examples 1 to 3, wherein the sensing circuit system includes a main sensing channel and an auxiliary sensing channel.

[0146] Example 5. The apparatus according to Example 4, wherein the main sensing channel includes an automatic threshold adjustment process.

[0147] Example 6. The apparatus according to Example 5, wherein the automatic threshold adjustment process includes a blanking period.

[0148] Example 7. The apparatus according to any combination of Examples 4 to 6, wherein the auxiliary sensing channel includes a fixed threshold process.

[0149] Example 8. The apparatus according to Example 7, wherein the fixed threshold process includes a blanking period.

[0150] Example 9. The apparatus according to Example 8, wherein the blanking period of the fixed threshold process is longer than the blanking period of the automatic threshold adjustment process.

[0151] Example 10. The apparatus according to any combination of Examples 1 to 9, wherein the sensing circuitry system includes one or more bandpass filters.

[0152] Example 11. The apparatus according to Example 10, wherein the one or more bandpass filters include a 10Hz to 32Hz bandpass filter and a 6Hz to 20Hz bandpass filter.

[0153] Example 12. The apparatus according to any combination of Examples 1 to 11, wherein the sensing circuit system includes one or more rectifiers.

[0154] Example 13. The apparatus according to any combination of Examples 1 to 12, wherein the processing circuitry is further configured to: determine whether the difference between the current RR interval and the previous RR interval is greater than a predetermined time period or greater than a predetermined number of samples; and determine the search window based on the current RR interval based on the fact that the difference between the current RR interval and the previous RR interval is greater than the predetermined time period or greater than the predetermined number of samples.

[0155] Example 14. The apparatus according to Example 13, wherein the predetermined time period is less than one second, or the predetermined number of samples is greater than 100.

[0156] Example 15. The apparatus according to any one of Examples 1 to 14, wherein the processing circuitry is further configured to: determine whether a previous heartbeat was noisy; and determine the search window based on the current RR interval based on the fact that the previous heartbeat was noisy.

[0157] Example 16. The apparatus according to any one of Examples 1 to 15, wherein the processing circuitry is configured to determine the T-wave by determining the maximum median of a sample in the search window.

[0158] Example 17. The apparatus according to any combination of Examples 1 to 16, wherein the processing circuitry is further configured to: based on a previous QT interval or a previous QTc interval, to more heavily weight the amplitude of samples in the search window that are more likely to be located by the T wave compared to samples that are less likely to be located by the T wave.

[0159] Example 18. The apparatus according to any combination of Examples 1 to 17, wherein the processing circuitry is further configured to: determine whether the amplitude of a sample in the search window is less than a threshold; and determine that the sample is not the T wave based on the fact that the amplitude of the sample is less than the threshold.

[0160] Example 19. The apparatus according to any combination of Examples 1 to 17, wherein the processing circuitry is further configured to determine the confidence level of the T wave based on one or more of a predetermined number of previous T wave amplitudes, QT intervals, or QTc intervals.

[0161] Example 20. The apparatus according to Example 19, wherein the predetermined number of preceding T-wave amplitudes, QT intervals, or QTc intervals is 12.

[0162] Example 21. The apparatus according to any combination of Examples 1 to 20, wherein the processing circuitry is further configured to: determine the trend of the determined QT interval or QTc interval over time.

[0163] Example 22. The apparatus according to Example 21, wherein the trend is one or more of the mean, median, mode, or standard deviation of the determined QT interval or QTc interval.

[0164] Example 23. The apparatus according to any combination of Examples 1 to 22, wherein the processing circuitry is further configured to: determine a time or count during which the QT interval or QTc interval is longer than a predetermined threshold.

[0165] Example 24. A method comprising: sensing a cardiac signal; determining an R wave of the cardiac signal; determining a previous RR interval of the cardiac signal based on the determined R wave; determining a current RR interval of the cardiac signal based on the determined R wave; determining a search window based on one or more of the current RR interval or the previous RR interval; determining a T wave of the cardiac signal in the search window; and determining a QT interval based on the determined T wave and the determined R wave.

[0166] Example 25. The method according to Example 24 further includes: determining the QTc interval.

[0167] Example 26. The method according to Example 25, wherein determining the QTc interval includes applying at least one of the Framingham formula, Friderica formula, Bazett formula, or other formulas or techniques to the QT interval.

[0168] Example 27. The method according to any combination of Examples 24 to 26, wherein sensing the cardiac signal includes sensing with a primary sensing channel and an auxiliary sensing channel.

[0169] Example 28. The method according to Example 27, wherein the main sensing channel includes an automatic threshold adjustment process.

[0170] Example 29. The method according to Example 28, wherein the automatic threshold adjustment process includes a blanking period.

[0171] Example 30. The method according to any combination of Examples 27 to 29, wherein the auxiliary sensing channel includes a fixed threshold process.

[0172] Example 31. The method according to Example 30, wherein the fixed threshold process includes a blanking period.

[0173] Example 32. According to the method of Example 31, wherein the blanking period of the fixed threshold process is longer than the blanking period of the automatic threshold adjustment process.

[0174] Example 33. The method according to any combination of Examples 24 to 32, wherein the sensing includes applying one or more bandpass filters to the sensed signal.

[0175] Example 34. According to the method of Example 33, the one or more bandpass filters include a 10Hz to 32Hz bandpass filter and a 6Hz to 20Hz bandpass filter.

[0176] Example 35. The method according to any combination of Examples 33 to 34, further comprising applying a rectifier to a bandpass signal.

[0177] Example 36. The method according to any combination of Examples 24 to 35, further comprising: determining whether the difference between the current RR interval and the previous RR interval is greater than a predetermined time period or greater than a predetermined number of samples; and determining the search window based on the current RR interval based on the fact that the difference between the current RR interval and the previous RR interval is greater than the predetermined time period or greater than the predetermined number of samples.

[0178] Example 37. The method according to Example 36, wherein the predetermined time period is less than one second, or the predetermined number of samples is greater than 100.

[0179] Example 38. The method according to any one of Examples 24 to 37, further comprising: determining whether the previous heartbeat was noisy; and determining the search window based on the current RR interval based on the fact that the previous heartbeat was noisy.

[0180] Example 39. The method according to any one of Examples 24 to 38, wherein determining the T wave includes determining the maximum median of the samples in the search window.

[0181] Example 40. The method according to any combination of Examples 24 to 39, further comprising: weighting the amplitude of samples in the search window that are more likely to be located by the T wave more heavily than samples that are less likely to be located by the T wave, based on a previous QT interval or a previous QTc interval.

[0182] Example 41. The method according to any combination of Examples 24 to 40, further comprising: determining whether the amplitude of a sample in the search window is less than a threshold; and determining that the sample is not the T wave based on the fact that the amplitude of the sample is less than the threshold.

[0183] Example 42. The method according to any combination of Examples 24 to 41, further comprising: determining the confidence level of the T wave based on one or more of a predetermined number of previous T wave amplitudes, QT intervals, or QTc intervals.

[0184] Example 43. The method according to Example 42, wherein the predetermined number of previous T-wave amplitudes, QT intervals or QTc intervals is 12.

[0185] Example 44. The method according to any combination of Examples 24 to 43, further comprising: determining the trend of the determined QT interval or QTc interval over time.

[0186] Example 45. The method according to Example 44, wherein the trend is one or more of the mean, median or mode of the determined QT interval or QTc interval.

[0187] Example 46. The method according to any combination of Examples 24 to 45, further comprising: determining a time or count during which the QT interval or QTc interval is longer than a predetermined threshold.

[0188] Example 47. A non-transitory computer-readable storage medium storing a set of instructions that, when executed, cause a system to: determine an R wave of a cardiac signal; determine a previous RR interval of the cardiac signal based on the determined R wave; determine a current RR interval of the cardiac signal based on the determined R wave; determine a search window based on one or more of the current RR interval or the previous RR interval; determine a T wave of the cardiac signal in the search window; and determine a QT interval based on the determined T wave and the determined R wave.

[0189] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

1. An apparatus comprising: One or more electrodes; A sensing circuit system configured to sense cardiac signals via the one or more electrodes; as well as Processing circuitry system, the processing circuitry system being configured to: Determine the R wave of the cardiac signal; The preceding RR interval of the cardiac signal is determined based on the determined R wave; The current RR interval of the cardiac signal is determined based on the determined R wave; Determine a search window based on one or more of the current RR interval or the previous RR interval, including determining the start sample and end sample of the search window based on one or more of the current RR interval or the previous RR interval; Identify the T wave of the cardiac signal in the search window; as well as The QT interval is determined based on the determined T wave and the determined R wave.

2. The apparatus of claim 1, wherein the processing circuitry is further configured to: determine the QTc interval.

3. The apparatus of claim 2, wherein the processing circuitry is configured to determine the QTc interval by applying at least one of the Framingham formula, the Friderica formula, the Bazett formula, or other formulas or techniques to the QT interval.

4. The apparatus according to any one of claims 1 to 3, wherein the processing circuit system is further configured to: Determine whether the difference between the current RR interval and the previous RR interval is greater than a predetermined time period or greater than a predetermined number of samples; and The search window is determined based on the current RR interval if the difference between the current RR interval and the previous RR interval is greater than the predetermined time period or greater than the predetermined number of samples.

5. The apparatus according to any one of claims 1 to 3, wherein the processing circuit system is further configured to: Determine if there was any noise in the previous heartbeat; and The search window is determined based on the previous noisy heartbeat and the current RR interval.

6. The apparatus according to any one of claims 1 to 3, wherein the processing circuitry is configured to determine the T-wave by determining the maximum median of the samples in the search window.

7. The apparatus of claim 1, wherein the processing circuitry is further configured to: Based on the QT interval or the preceding QTc interval, the amplitudes of samples in the search window that are more likely to be located by the T wave are weighted more heavily than those samples that are unlikely to be located by the T wave.

8. The apparatus according to any one of claims 1 to 3, wherein the processing circuit system is further configured to: Determine whether the amplitude of the samples in the search window is less than a threshold; and Based on the fact that the amplitude of the sample is less than the threshold, it is determined that the sample is not the T wave.

9. The apparatus of claim 1, wherein the processing circuitry is further configured to: The confidence level of the T wave is determined based on one or more of a predetermined number of previous T wave amplitudes, QT intervals, or QTc intervals.

10. The apparatus of claim 1, wherein the processing circuitry is further configured to: Determine the time or count by which the QT interval or QTc interval is longer than a predetermined threshold.

11. A non-transitory computer-readable storage medium storing an instruction set, which, when executed, causes the system to: Identify the R wave of cardiac signals; The preceding RR interval of the cardiac signal is determined based on the determined R wave; The current RR interval of the cardiac signal is determined based on the determined R wave; Determine a search window based on one or more of the current RR interval or the previous RR interval, including determining the start sample and end sample of the search window based on one or more of the current RR interval or the previous RR interval; Identify the T wave of the cardiac signal in the search window; as well as The QT interval is determined based on the determined T wave and the determined R wave.

Images