Method and system for estimating residual ECG noise level and adaptive noise threshold

Abstract

The present invention is entitled "Method and system for estimating residual ECG noise level and adaptive noise threshold". The present invention discloses a system comprising an apparatus for estimating the residual noise level in an electrocardiogram (ECG) signal. The disclosed system and method can be used in an electrocardiogram device. According to an exemplary embodiment of the present invention, a plurality of electrodes positioned near a cardiac structure can measure the electrical signal of the cardiac structure to generate an ECG signal. The system can segment the ECG signal into a plurality of segments. For each of the plurality of segments, linear trend energy and / or direct current (DC) energy can be removed from the segment, and an estimated noise energy of the segment can be calculated. A subset of the plurality of segments with the minimum estimated noise energy can be selected. The residual noise energy of the ECG signal can be estimated by calculating the average of the estimated noise energy over the subset of segments.

Description

Technical Field

[0001] The present invention generally relates to systems and methods for estimating noise levels in electrical signals, and more particularly to systems and methods for estimating noise levels in electrocardiogram (ECG) signals and adaptive noise thresholding algorithms. Background Art

[0002] Electrocardiography (ECG) is a test that measures and records the heart's electrical activity over a period of time using electrodes placed on the skin and / or electrodes placed inside the heart using a catheter. These electrodes detect small electrical changes caused by the electrophysiological pattern of depolarization of the heart muscle during each heartbeat and can therefore be used to detect abnormal cardiac conditions such as myocardial infarction, pulmonary embolism, structural heart disease (e.g., heart murmur), tachycardia, or arrhythmias (e.g., atrial fibrillation). Electrocardiography can be performed by an electrocardiograph, and the resulting test produces an electrocardiogram (equivalently abbreviated as EKG or ECG), which displays the electrical signals in the heart, typically as a graph of the voltage of the heart's electrical activity over time.

[0003] During each heartbeat, a healthy heart has an orderly depolarization process. This orderly depolarization pattern produces a characteristic ECG trace. For a well-trained clinician, the morphology of the ECG signal conveys a large amount of information about the structure of the heart and the function of its electrical conduction system. Among other things, the ECG can be used to measure the rate and rhythm of the heartbeat, the size and position of the ventricles, the presence of any damage to the heart's muscle cells or conduction system, the effects of cardiac drugs, and the function of implanted pacemakers. The interpretation of the ECG is fundamentally about understanding the heart's electrical conduction system. Normal conduction begins and propagates in a predictable pattern, and deviations from this pattern can be normal variations or pathological. Therefore, the presence of noise in the ECG signal can hinder the ability to effectively analyze cardiac activity. In addition, including cardiac 3D imaging and ablation systems (e.g., Many algorithms and systems (e.g., ECG systems) rely on ECG signals for reference, mapping, and analysis. To improve the way such systems and algorithms function, an accurate and real-time estimate of the actual residual noise level in the ECG signal is needed.

[0004] Electrocardiogram signals contain unwanted noise, which can include low-frequency noise (e.g., due to respiration and / or baseline drift) and higher-frequency noise, such as power noise and / or deflection noise. Filters (e.g., power filters) can be used to remove noise at certain frequencies. However, they cannot effectively remove all noise (e.g., noise at frequencies outside the filter bandwidth). Therefore, even after filtering, residual noise is typically present in the ECG signal. For various reasons, such as monitoring system performance and adjusting various algorithms, estimating residual noise is important. ECG noise estimation methods are challenged by the presence of concurrent signals and noise. Therefore, methods for noise estimation can include, as a first step, attempting to separate the desired signal from the noise signal. Existing methods for separating these signals are often very complex. Therefore, there is a need for a more simplified, accurate, and real-time method for noise estimation in ECG signals that can be used to assess and manage noise levels in systems that rely on ECG. Summary of the Invention

[0005] The present invention discloses a system including a device for estimating the residual noise level in an electrocardiogram (ECG) signal. The disclosed system and method can be used in an ECG device. According to an exemplary embodiment of the present invention, a plurality of electrodes positioned near a cardiac structure can measure the electrical signal of the cardiac structure to generate an ECG signal. The system can segment the ECG signal into a plurality of segments. For each of the plurality of segments, a trend including a constant direct current (DC), a linear trend, and / or an interpolated low-frequency trend can be removed from the segment, and an estimated noise energy for the segment can be calculated. A subset of the plurality of segments with the minimum estimated noise energy can be selected. The residual noise energy of the ECG signal can be estimated by calculating the average of the estimated noise energy over the subset of segments. The estimated noise energy can be used in a variety of applications, for example, to compare and select diagnostic devices (e.g., catheters, electrodes) with the minimum noise, to detect and warn of errors in the device and perform fault diagnosis, and to utilize the estimated noise in the system to adjust system performance and system thresholds / algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The foregoing and other features and advantages of the disclosure will be apparent from the following more particular description of preferred embodiments of the disclosure, as illustrated in the accompanying drawings.

[0007] Figure 1 is a schematic diagram of an example electrocardiogram device 100 according to an exemplary embodiment;

[0008] Figure 2 is a flow chart of an example process for estimating the noise level in an electrocardiogram (ECG) signal according to an exemplary embodiment;

[0009] Figure 3Ashows example ECG noise levels at different levels of ECG signals produced using a first type of catheter in the CARTO system, wherein the ECG signal is shown in raw form (digital output), after power noise filtering, and after user-selectable filtering (by the CARTO system);

[0010] Figure 3B shows example ECG noise levels at different levels of ECG signals produced using a second type of catheter in the CARTO system, wherein the ECG signals are shown in raw form (digital output), after power noise filtering, and after user-selectable filtering (by the CARTO system);

[0011] Figure 3C An example half ECG signal in raw format generated using a first type of catheter in the CARTO system is shown, and a method for Figure 2 The process shown calculates an example segment of a residual ECG noise level estimate; and

[0012] Figure 3D An example half ECG signal in raw format generated using a second type of catheter in the CARTO system is shown, and a method for Figure 2 The process shown calculates an example segment of a residual ECG noise level estimate. DETAILED DESCRIPTION

[0013] An exemplary electrocardiogram system may include a plurality of leads (e.g., twelve leads, or fewer or more) and a plurality of electrodes (e.g., ten electrodes, or more or fewer) placed on the patient's limbs and chest surface. The overall magnitude of the heart's electrical potential is measured by the leads (each lead corresponds to a different measurement angle) and recorded over a period of time. Electrocardiography performed with intracardiac electrodes (which are, for example, mounted on a catheter placed within a chamber of the heart) produces an ECG known as an intracardiac electrocardiogram (ICEG), and may be used in conjunction with or instead of leads placed outside the patient. A lead consisting of two electrodes of opposite polarity is referred to as a bipolar lead. A lead consisting of a single positive electrode and a reference point is a unipolar lead.

[0014] In order to measure the electrical activity of the heart muscle, the ECG electrodes must be able to detect very small changes in potential energy on the patient's skin or heart tissue. For example, electrical changes can be detected by ECG electrodes as cardiac electrical signals measuring approximately 1 millivolt (mV) or less.

[0015] In a conventional intracardiac electrocardiogram (ECG) system, ECG electrodes in contact with the skin and / or heart tissue measure the cardiac signal current as positive charge flowing toward the electrodes, and as negative charge flowing away from the electrodes, to produce a voltage reading of the heart's electrical signal over time. The goal of an ECG system is to minimize artifacts and maximize the accuracy of the EKG signal in order to provide reliable information to physicians. The ECG signals generated by ECG systems are widely used to diagnose and monitor cardiac conditions, but they are sensitive to various intermixing noises, which can reduce diagnostic accuracy and hinder physicians' ability to effectively diagnose and treat cardiac conditions.

[0016] According to an exemplary embodiment of the present invention, the disclosed electrocardiogram system and method employs a dynamic and real-time residual noise estimation process to estimate the residual noise level in ECG signals (including surface or intracardiac ECG signals). Furthermore, the residual noise estimation process disclosed herein can be used with other forms of signals, such as neural recordings, electrical signals on communication lines, and any other electrical signals where the desired signal and noise signal need to be separated in a timely manner, including signals generated in non-biomedical applications. According to an exemplary embodiment of the present invention, the residual noise estimation process may assume that the ECG signal contains short intervals and does not contain local or far-field activity. According to the exemplary noise estimation process, the ECG signal is segmented into short segments (e.g., 40-50 millisecond windows). The direct current (DC) component, linear trend component, and / or any low-pass component are removed from the ECG segments, and the energy of each ECG segment is calculated (e.g., by calculating the root mean square (RMS) or peak-to-peak amplitude). The non-DC, non-trend energy of the RMS of the ECG segment is then determined and used as an estimate of the noise level of the ECG signal. The present invention exploits the fact that the energy level of noise generally does not change abruptly (except perhaps in cases where communication noise is transient). More details of the present invention are described below.

[0017] Figure 1is a schematic diagram of an example electrocardiogram device 100 according to an exemplary embodiment. The electrocardiogram device 100 may include, but is not limited to, any of the following components: a console system 101; an intracardiac lead 107 connected to a catheter 120 having a distal end 114 inserted into a heart 126 of a patient 105; a non-contact electrode 116 located at the distal end 114 of the catheter 120; and leads 110 connected to electrodes 112 positioned in various locations on the skin of the patient 105. The console system 101 may include, but is not limited to, any of the following components: an analog-to-digital converter (ADC or A / D converter) 125; a processor 130; a data storage device 155; a data port printer 160; an input / output (I / O) device 165; a visual display device 170; and / or an energy source device 175. Processor 130 may include, but is not limited to, including any one or more of the following components: a video controller 135 ; a digital signal processor (DSP) 140 ; a microprocessor 145 ; and / or a microcontroller 150 .

[0018] Catheter 120, leads 107 and 110, electrodes 112 and 116, and / or other components (not shown) of electrocardiography device 100 (e.g., additional catheters, sensors, transformers, etc.) can be used directly on, in, and / or near patient 105 to collect information for visualization, diagnosis, and treatment (e.g., ablation therapy). This information can be provided to console system 101 for processing, visualization, and operator control and guidance, some of which are described below.

[0019] A series of leads 110 and intracardiac leads 107 connect electrodes 112 on the surface of the patient's 105 skin and electrodes 116 on a catheter 120 within the heart 126, respectively, to the main console 101 of the electrocardiogram device 100. In an example, the intracardiac catheter 120 may be used for diagnostic and / or therapeutic treatments, such as for mapping electrical potentials in the heart 126 of the patient 105. In an example, the leads may be bipolar or monopolar. In an example, the catheter 120 may be inserted into the vascular system of the patient 105 such that the distal end 114 of the catheter 120 enters a chamber of the patient's heart 126. Although Figure 1 A single catheter 120 and intracardiac lead 107 are shown, but additional catheters and leads (not shown) having one or more electrodes and / or sensors may similarly be used. In addition, the electrocardiogram device 100 may use only the surface electrodes 112, only the intracardiac electrodes 116, or both the surface electrodes 112 and the intracardiac electrodes 116 for ECG readings.

[0020] A raw ECG signal 115 (i.e., an analog input signal) is acquired from any one (or more) of the electrodes 112 and / or 116 and converted from an analog format to a digital format by an adjustable gain ADC 125. The ADC 125 generates and provides a digital output 117 of the ECG signal 115 by sampling the analog input signal 115 at a sampling rate. The resolution of the ADC 125 indicates the number of discrete values that the ADC 125 can generate within the analog value range and can be defined electrically in volts. The number of voltage intervals that the ADC 125 can generate is determined by 2 M is given by, where M is the bit resolution of the ADC.

[0021] Once the analog signal is converted, the ADC 125 transmits the digital ECG signal to the processor 130 for generating an ECG graph and / or performing other ECG analysis. The processor 130 may be coupled to a data storage device 155, a data port and printer 160, other I / O devices 165, and a visual display device 170 that may be used to display the ECG generated by the electrocardiographic device 100. The electrocardiographic device 100 and / or any of its components may be powered by one or more energy sources 175.

[0022] Data storage device 155 is any device that records information. The data storage device may provide a storage medium for the signals included within apparatus 100 and a location for computations of processor 130 to be stored.

[0023] The microprocessor 145 may be a computer processor that combines the functionality of a central processing unit (CPU) of a computer onto a single integrated circuit (IC) or onto several integrated circuits. The microprocessor 145 may be a multi-purpose, clock-driven, register-based programmable electronic device that accepts digital or binary data as input, processes the digital or binary data according to instructions stored in its memory or data storage device 155, and provides results as output. The microprocessor 145 includes both combinational logic and sequential digital logic.

[0024] Microcontroller 150 can be one or more small computers on a single integrated circuit. Microcontroller 150 can contain one or more CPUs as well as memory and programmable input / output peripherals. Program memory in the form of ferroelectric RAM, NOR flash memory, or OTP ROM, as well as a small amount of RAM, is also often included on the chip. Microcontrollers are designed for embedded applications, in contrast to microprocessors used in personal computers or other general-purpose applications, which are composed of various discrete chips.

[0025] The DSP 140 can perform digital signal processing to perform a variety of signal processing operations. Signals processed in this manner are digital sequences of samples representing continuous variables in domains such as time, space, or frequency. Digital signal processing can involve linear or nonlinear operations. Nonlinear signal processing is closely related to nonlinear system identification and can be implemented in the time, frequency, and space-time domains. Applying digital computing to signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission and data compression. DSP is applicable to both streaming data and static (stored) data.

[0026] Figure 2 is a flow chart of an example residual ECG noise level estimation process 200 according to an exemplary embodiment of the present invention. The example residual ECG noise level estimation process 200 may be used in an electrocardiogram system such as Figure 1 The residual ECG noise level estimation process 200 may be implemented in the example electrocardiography device 100 of FIG.

[0027] exist Figure 2 At step 202 of the residual ECG noise level estimation process 200 shown, ECG data are segmented into multiple overlapping sections or windows. According to an exemplary embodiment, ECG data can be the original ECG signal (e.g., unipolar or bipolar ECG signal) generated by one or more electrodes located in or near the patient's heart and before any noise filtering (e.g., power filtering). Process 200 can be performed to various types of ECG signals (i.e., before / after each processing stage). According to an exemplary embodiment, ECG data can be the original analog ECG signal before or after power filtering, or ECG data can be processed (e.g., CARTO) ECG signal after power filtering, high-pass filtering and / or low-pass filtering. For example, before power filtering, the original analog signal is performed to process 200 so that the actual noise level introduced by catheter / electrode can be detected, which can be masked by filtering. In addition, in clinical settings, other noise sources that the power filter is not designed to eliminate can be introduced, and / or the power filter can introduce other problems or cause errors. In another example, process 200 may be performed after power filtering, for example, to estimate the effectiveness of the filtering, and / or to use the resulting residual noise level estimate as a dynamic (time-varying) noise level estimate in systems and algorithms that take noise levels into account, and / or to report the resulting residual noise level estimate to a monitoring station (user) to be used, for example, for calibration, analysis, and / or big data.

[0028] The duration of the segment can be selected to isolate energy fluctuations due to unwanted noise from non-DC energy or trend energy (e.g., slope effects in the signal) due to the atrial / ventricular signal of interest. Specifically, the segment can be selected to be short enough so that the non-DC trend energy is minimized. For example, most tachycardias (except for possible atrial fibrillation) include long periods without atrial / ventricular signals, so short segments within these periods can include minimal to no trend energy. In an example, a segment with a duration of approximately 40ms-50ms can be selected for a unipolar ECG signal of 1 second duration. Figure 3C and Figure 3D An example segment for a 1 second ECG signal is shown in FIG. According to an exemplary embodiment, adjacent segments in the plurality of segments may partially overlap in time. For example, the overlap between adjacent segments may be approximately 10% or less of the segment duration (e.g., a 5 ms overlap of a 50 ms segment). According to other exemplary embodiments, the segments may not overlap and may be adjacent or may be selected to be regularly spaced.

[0029] For each of the plurality of ECG segments, one or more of the following steps (ie, steps 204, 206, and / or 208) may be performed. Figure 2 At step 204 of the residual ECG noise level estimation process 200 shown, linear trend energy is determined and removed from the ECG segment. The linear trend energy corresponding to the increasing or decreasing slope of the ECG segment can be determined using, for example, a linear regression technique. Examples of trend energy include sharp slopes or spikes in the ECG signal, such as Figure 3C and Figure 3D There are spikes near or around 0.1 seconds and 0.6 seconds.

[0030] In an example, a linear trend energy can be determined using regression analysis to find a formula that fits a straight line (or linear) trend to the data in the ECG segment (e.g., in the form of Y=μX+β). Once the linear trend energy is determined, it can be subtracted from the ECG segment to remove the linear trend energy. In some cases, the linear trend energy can be minimal or non-existent within a short ECG segment (even if a trend is present within a time window larger than the segment (e.g., the entire duration of the signal being measured)). Therefore, in such cases, step 204 can be omitted.

[0031] exist Figure 2At step 206 of the illustrated residual ECG noise level estimation process 200, DC energy and / or any low-pass energy components are removed from the ECG segment. The DC energy (and any low-pass energy components), also referred to as a DC bias, DC component, DC offset, or DC coefficient, is determined and calculated as the average amplitude of the waveform of the ECG segment, which can then be subtracted from the ECG segment to remove the DC component.

[0032] exist Figure 2 At step 208 of the illustrated residual ECG noise level estimation process 200, an estimated magnitude of the noise level (i.e., estimated noise energy) is calculated, for example, by taking the root mean square (RMS) amplitude (RMS of the alternating current (AC) voltage) or peak-to-peak (PP) amplitude of the ECG segment (where the linear trend / low-pass / DC energy is removed). When calculating the estimated noise energy, the estimated noise energy value (e.g., RMS or PP amplitude value) can be normalized by the duration (or energy window) of the segment. According to other exemplary embodiments, the estimated noise level of the segment can be calculated using any known estimation technique, including but not limited to spectral density estimation techniques.

[0033] Once the estimated noise level for each of the plurality of ECG segments of the ECG data is calculated, Figure 2 At step 210 of the illustrated residual ECG noise level estimation process 200, a subset (one or more) of the multiple ECG segments is selected based on the ECG segment with the minimum estimated noise level (e.g., the minimum RMS or PP amplitude), and the estimated average noise level of the ECG data is calculated by taking the average of the estimated noise levels of the selected subset of ECG segments. The noise estimate is preferably averaged over multiple segments to provide a more reliable estimate. The average of the estimated noise levels of the selected subset of ECG segments can be a simple average or a weighted average. For example, the weighted average can take into account previous noise estimates to determine weights and / or the consistency of the noise estimate over time (e.g., if the noise estimate changes abruptly from one second of signal to the next, there may be noise or error in the estimate). According to an exemplary embodiment, a subset (e.g., three segments) of the multiple segments is used. In another exemplary embodiment, the noise estimate can be averaged over all segments of the original ECG data signal. In an alternative exemplary embodiment, only one estimated noise level (eg, the minimum noise level) is used, and no averaging is performed.

[0034] According to an exemplary embodiment of the present invention, the noise energy of the ECG segment with the minimum noise energy is preferably used to estimate the noise energy of the ECG data. However, according to an alternative exemplary embodiment, other noise energy estimates other than the lowest value may be used, such as sampling the noise energy estimate in the lowest 10% percentile.

[0035] Optionally, in Figure 2 At step 212 of the residual ECG noise level estimation process 200 shown, the steps for calculating the estimated average noise level (e.g., steps 202-210) can be performed for multiple ECG data (e.g., X ECG signals each having a duration of 1 second, or equivalently, X seconds of longer ECG signals divided into 1 second signals), and noise statistics such as the mean and standard deviation of the noise can be generated over the multiple ECG signals. According to an exemplary embodiment, ECG segments of electrical activity where noise should not be measured can be marked or identified. For example, if the system knows that a specific condition occurs at a specific time, the system can temporarily ignore or suspend the noise level estimation process calculation (e.g., during ablation or during specific signal periods, such as during detection of the ventricular far field during atrial mapping).

[0036] In various applications, the estimated noise energy of the ECG data generated at step 210 can be provided to and used by a system or algorithm, or directly provided to a user for analysis or troubleshooting. Figure 2 Some examples of applications and uses of the estimated noise level of an ECG signal generated by the residual ECG noise level estimation process 200 are shown.

[0037] According to an exemplary embodiment of the present invention, the electrocardiogram system can be combined with other medical systems, such as a real-time 3D cardiac imaging system for visualizing cardiac activity and defects, and / or a cardiac ablation system for correcting cardiac rhythm defects known as arrhythmias by creating ablation lesions to destroy the tissue in the heart that causes the rhythm defects. An example of a real-time 3D imaging system for cardiac ablation is the Biosense Imaging System, a subsidiary of Johnson & Johnson. The company's production 3 systems. The 3D system uses electromagnetic technology to create a 3D map of the patient's heart structure, showing the exact location and orientation of the catheter in the heart, and provides an ECG of the electrical signals in the heart at the corresponding location. In some applications, different catheters can be used with 3 systems are used together, and Figure 2 The residual ECG noise level estimation process 200 shown can be used to compare different catheter 3. Noise performance between systems. The following example compares several points collected at the same location in the ventricle and under similar arrhythmia conditions using different catheters. The signal is extracted to visualize the ECG signal in its original form (without any filtering), in its original form after power filtering (but without high-pass and / or low-pass filtering), and in its processed form (with power filtering, high-pass filtering, and low-pass filtering). Additional examples of filters (in addition to power filters) that can be used to process the ECG signal prior to ECG noise energy estimation include, but are not limited to, a linear regression median filter, a derivative filter, and / or a high-pass filter (e.g., to remove baseline wander prior to ECG noise energy estimation).

[0038] Figure 3A and Figure 3B Examples of different levels of ECG signals generated using a first type of catheter and a second type of catheter, respectively, in a CARTO system are shown.

[0039] Figure 3A shows an example distal unipolar ECG signal acquired from a low voltage point (0.2 mV) of the inferior posterior view of the right atrium (RA) ventricle using a first type of catheter, and Figure 3B An example distal unipolar ECG signal acquired from a low voltage point (0.2 mV) of the inferior posterior view of the RA ventricle using a second type of catheter is shown. Figure 3A and Figure 3B The ECG signal is shown in its original form (without any filtering), in its original form after power filtering and in its processed form (CARTO signal with power filtering, high-pass filtering and low-pass filtering). Figure 2 The residual ECG noise level estimation process 200 described in

[15] can be applied to Figure 3A and Figure 3B To compare the noise performance of two different types of catheters. Figure 3A and Figure 3B In the example of , it can be observed that the ECG noise level in the CARTO system in the processed (CARTO) signals using either catheter appears to be almost the same (i.e., Figure 3A and Figure 3B The differences in signal patterns in the two catheters were minimal. Therefore, no meaningful analysis of the noise level could be performed based on the processed CARTO signals. However, the ECG noise level observed in the raw ECG signals was different between the two catheters.

[0040] Figure 3C An example half (monopolar) ECG signal in raw format produced using a first type of catheter in the CARTO system is shown, and a method for Figure 2 The process shown computes an example segment of the residual ECG noise level estimate. Similarly, Figure 3D An example half (monopolar) ECG signal in raw format produced using a second type of catheter in the CARTO system is shown, and a method for Figure 2 The process shown computes an example segment of the residual ECG noise level estimate. Figure 3C and Figure 3D The quieter segments are shown to have less trend / DC / low-pass energy and are approximately 50ms in duration. Figure 2 The residual ECG noise level estimation process 200 shown can be used to remove any trend / DC / low-pass energy using any short duration period to isolate the noise energy by applying the techniques described herein.

[0041] In the Figure 2 The residual ECG noise level estimation process 200 is shown as an example for application to Figure 3C and Figure 3D When a segment of the raw ECG signal in , which may be averaged over a plurality of half ECG signals (eg, 300 half ECG signals), is determined, the catheter of the first type ( Figure 3C ) produces a noise level of approximately 57 μV in the raw ECG signal, which is caused by the second type of catheter ( Figure 3D ) generated more than double the noise level in the original ECG signal, which was approximately 21 μV. Spectral analysis comparing the two original signals also supported this finding, indicating that the higher noise level was due to power harmonics.

[0042] Therefore, a comparison of ECG signals of different catheters based on residual ECG noise level estimation shows that although the processed signals appear to be equivalent, Figure 3A The original signal ratio shown Figure 3B The raw signal in the is noisier. This type of analysis can be used, for example, in process control to determine that a first type of catheter has worse noise performance than a second type of catheter, and to determine which type of catheter to use in different applications. As described above, analysis based on ECG noise level estimation can be performed on various levels of the signal, including the signal in its raw form, after power filtering, or after any user-selectable filtering.

[0043] According to an exemplary embodiment, the residual ECG noise level estimation process of the present invention can be used to estimate the noise energy level in a system involving one or more intracardiac unipolar ECG signals that are sensitive to noise at different frequencies, and an algorithm designed to analyze unipolar ECG signals. According to an exemplary embodiment, the noise level can be estimated for each catheter (wherein more than one catheter is used), and the catheters can be compared based on their corresponding estimated noise levels. According to another exemplary embodiment, noise levels can be estimated and compared for different levels of ECG output (e.g., in raw form, power filtered form, or after a user-selectable filter).

[0044] According to another exemplary embodiment, using Figure 2 The estimated noise level of the ECG signal generated by the illustrated residual ECG noise level estimation process 200 can be used to dynamically modify specific algorithms. For example, a mapping algorithm (e.g., a wavefront algorithm and a finder algorithm) for signals detected in the presence of noise can utilize the real-time estimated noise level to dynamically change the detection threshold of the mapped signal, thereby enabling detection of low (amplitude) signals when the noise level is low and increasing the detection threshold of the signal when the noise is high.

[0045] According to another exemplary embodiment, using Figure 2 The estimated noise level of the ECG signal generated by the illustrated residual ECG noise level estimation process 200 can be used to compare the instantaneous noise level estimate with a history of noise level estimates collected for a given system (e.g., a CARTO system). For example, the comparison of the estimated noise level over time can be used to monitor power filter algorithm performance and provide an alert to the system or user when a power filter malfunctions. More generally, the comparison of the estimated noise level over time can be used to alert the system or user to identified problems and aid in troubleshooting (e.g., comparing noise levels using different cables to identify a faulty cable).

[0046] According to another exemplary embodiment, using Figure 2 The estimated noise level of the ECG signal generated by the illustrated residual ECG noise level estimation process 200 can be used during the production of a device (e.g., a catheter) to test the device during production to ensure that the design and connectivity are appropriate. Figure 2 The estimated noise level of the ECG signal generated by the illustrated residual ECG noise level estimation process 200 can be used to determine which components, systems and / or conditions generate noise when collecting big data from a group of systems and / or to verify that there is no performance degradation due to system changes including software upgrades.

[0047] Based on the disclosure herein, many variations are possible. Although features and elements are described above in particular combinations, each feature or element can be used alone without the other features and elements, or each feature or element can be used in various combinations with or without the other features and elements.

[0048] The systems and processes described herein can be implemented in hardware and / or software. A computer-based system for performing electrocardiography may be capable of running software modules that incorporate additional features including the processes described herein. The processes described herein can enable advanced cardiac visualization and diagnostic capabilities to enhance the ability of clinicians to diagnose and treat cardiac rhythm disorders. Although the processes disclosed herein are described with respect to electrocardiography processes within the heart, these devices and processes can be similarly used for electrophysiological processes in other parts of the body, such as, but not limited to, electroencephalography in the brain, electrooculography in the eye, and electrorespirography in the lungs. In addition, the processes disclosed herein can be used to estimate the noise energy in any electrical signal, including non-biomedical electrical signals.

[0049] The method provided by the present invention may be included in a specific implementation in a general-purpose computer, a processor or a processor core. By way of example, suitable processors include general-purpose processors, special-purpose processors, conventional processors, digital signal processors (DSPs), multiple microprocessors, one or more microprocessors associated with a DSP core, controllers, microcontrollers, application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), any other type of integrated circuit (IC) and / or state machines. Such processors can be manufactured by configuring a manufacturing process using the results of processed hardware description language (HDL) instructions and other intermediate data including a network table (such instructions can be stored on a computer-readable medium). The result of such processing can be a mask work, which is then used in a semiconductor manufacturing process to manufacture a processor that can implement the method described herein.

[0050] The methods or flow charts provided herein may be implemented in a computer program, software, or firmware incorporated into a non-transitory computer-readable storage medium for execution by a general-purpose computer or processor. Examples of non-transitory computer-readable storage media include ROM, random access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks, and digital versatile disks (DVDs).

[0051] ***

Claims

1. A system for estimating residual noise energy of an electrocardiogram (ECG) signal, the system comprising: a plurality of electrodes positioned proximate to a cardiac structure and configured to measure electrical signals of the cardiac structure to generate the ECG signal; as well as a processor configured to: segmenting the ECG signal into a plurality of segments; For each of the plurality of segments: removing linear trend energy and direct current (DC) energy from the segment and calculating an estimated noise energy for the segment, wherein the linear trend energy corresponds to an increasing or decreasing slope of the segment and the DC energy is calculated as an average amplitude of the waveform of the segment; selecting a subset of the plurality of segments having a minimum estimated noise energy; as well as The residual noise energy of the ECG signal is estimated by averaging the estimated noise energy over a subset of the segments.

2. The system of claim 1 , wherein the plurality of electrodes comprises at least one of an intracardiac electrode and a surface electrode, the intracardiac electrode being mounted on a catheter, the surface electrode being located on a body surface external to the cardiac structure, the catheter being configured for insertion into a chamber of the cardiac structure. The system of claim 1 , wherein adjacent segments among the plurality of segments partially overlap in time. 4 . The system of claim 1 , wherein the ECG signal is a raw unipolar or bipolar ECG signal before any filtering.

5. The system according to claim 1, further comprising: At least one power filter is configured to filter the ECG signal.

6. The system of claim 1 , further comprising: At least one of a near-regression median filter, a derivative filter, or a high-pass filter is used to filter the ECG signal.

7. The system of claim 1 , wherein: The processor is further configured to select a duration of the segment such that the trend energy is minimized.

8. The system of claim 1 , wherein: The processor is configured to calculate the estimated noise energy of the segment by taking the root mean square (RMS) amplitude or the peak-to-peak (PP) amplitude of the segment.

9. The system of claim 1 , wherein: The processor is further configured to normalize the estimated noise energy of the segment by a duration of the segment.

10. The system of claim 1, wherein: The plurality of electrodes are further configured to generate a plurality of ECG signals; and The processor is configured to estimate the residual noise energy of each of the plurality of ECG signals and generate noise statistics across the plurality of ECG signals.

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