Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
  • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
  • A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/25—Bioelectric electrodes therefor
  • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
  • A61B5/28—Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
  • A61B5/283—Invasive
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
  • A61B5/346—Analysis of electrocardiograms
  • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N1/00—Electrotherapy; Circuits therefor
  • A61N1/18—Applying electric currents by contact electrodes
  • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
  • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
  • A61N1/362—Heart stimulators
  • A61N1/37—Monitoring; Protecting
  • A61N1/3702—Physiological parameters
  • A61N1/3704—Circuits specially adapted therefor, e.g. for sensitivity control
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
  • A61B5/7217—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise originating from a therapeutic or surgical apparatus, e.g. from a pacemaker
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
  • F04C—ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT PUMPS
  • F04C2270/00—Control; Monitoring or safety arrangements
  • F04C2270/04—Force
  • F04C2270/041—Controlled or regulated

Definitions

• FIG. 1 is an illustration of portions of a system that use an implantable medical device.
• FIG. 5 is a flowchart of an alternative embodiment of a multi-pass method for use with atrial and ventricular beats.
• FIG. 6 is a flowchart showing one embodiment of beat detection on one channel of an electrogram.
• FIG. 11A shows an electrogram with data sensed by a CRM device.
• FIG. 11B shows an electrogram using the data from FIG. 11A that is further analyzed to show where beats are detected using an off-line multi-pass algorithm to reduce undersensing in accordance with an embodiment of the invention.
• FIG. 12A shows an electrogram with data sensed by a CRM device.
• FIG. 12B shows an electrogram using the data from FIG. 12A that is further analyzed to show where beats are detected using an off-line multi-pass algorithm to reduce oversensing in accordance with an embodiment of the invention.
• FIG. 15 is a schematic diagram of an implementation of the components of an external interface device such as a programmer, in accordance with various embodiments.
• FIG.16 is a block diagram of an implantable medical device.
• Cardiac signals can be in the form of EGMs, heart sound signals, impedance signals, and pressure signals.
• Data-generating devices include IMDs, while in some embodiments the data generating devices are external or subcutaneous.
• the information gathered includes an intracardiac EGM.
• the science of morphology deals with interpretation of the shape characteristics of the cardiac signals, such as EGM signals, where such shape characteristics include amplitude, width and contour.
• the morphology of a cardiac waveform can be used to discriminate between different types of cardiac arrhythmias and other cardiac events, and morphology information can be very useful to clinicians treating the patient.
• FIG. 1 is an illustration of portions of a system that uses an implanted medical device (IMD) 110.
• IMD implanted medical device
• CCM cardiac rhythm management
• Examples of IMDs 110 include, without limitation, a pacer, a defibrillator, a cardiac resynchronization therapy (CRT) device, or a combination of such devices.
• the system also typically includes an IMD programmer or other external device 170 that communicates wireless signals 190 with the IMD 110, such as by using radio frequency (RF) or other telemetry signals.
• RF radio frequency
• the IMD 110 is coupled by one or more leads 108A-C to the heart 105.
• Cardiac leads 108A-C include a proximal end that is coupled to IMD 110 and a distal end, coupled by an electrode or electrodes to one or more portions of a heart 105.
• the electrodes typically deliver cardioversion, defibrillation, pacing, or resynchronization therapy, or combinations thereof to at least one chamber of the heart 105.
• the electrodes may be electrically coupled to sense amplifiers to sense electrical cardiac signals.
• the heart 105 includes a right atrium 100A, a left atrium 100B, a right ventricle 105 A, a left ventricle 105B, and a coronary sinus extending from right atrium 100A.
• the atrial lead 108A includes electrodes (electrical contacts, such as ring electrode 125 and tip electrode 130) disposed in the right atrium 100A of heart 105 for sensing signals, or delivering pacing therapy, or both, to the right atrium 100A.
• the ventricular lead 108B includes one or more electrodes, such as tip electrode 135 and ring electrode 140, for sensing signals, delivering pacing therapy, or both sensing signals and delivering pacing therapy.
• the lead 108B optionally also includes additional electrodes, such as for delivering atrial cardioversion, atrial defibrillation, ventricular cardioversion, ventricular defibrillation, or combinations thereof to the heart 105.
• Such electrodes typically have larger surface areas than pacing electrodes in order to handle the larger energies involved in
• the IMD 110 may include a third cardiac lead 108C attached to the IMD 110 through the header 155.
• the third cardiac lead 108C includes ring electrodes 160, 165 placed in a coronary vein lying epicardially on the left ventricle (LV) 105B via the coronary vein 120.
• LV left ventricle
• the lead 108B may include a first defibrillation coil electrode 175 located proximal to tip and ring electrodes 135, 140 for placement in a right ventricle (RV), and a second defibrillation coil electrode 180 located proximal to the first defibrillation coil 175, tip electrode 135, and ring electrode 140 for placement in the superior vena cava (SVC).
• high-energy shock therapy is delivered from the first or RV coil 175 to the second or SVC coil 180.
• the SVC coil 180 is electrically tied to an electrode formed on the hermetically-sealed IMD can 150. This improves defibrillation by delivering current from the RV coil 175 more uniformly over the ventricular myocardium.
• the therapy is delivered from the RV coil 175 only to the electrode formed on the IMD can 150.
• the electrode configuration used in the systems and methods described herein allow for the collection of EGMs on at least one channel, while multiple channels may be used.
• signals from a first and second channel of an EGM are analyzed.
• a signal from an atrial channel is provided.
• a ventricular EGM signal is recorded with electrodes implanted in or near a ventricle, called a ventricular channel.
• a ventricular channel or vector may include a tip electrode and ring electrode for the right ventricular channel or ring electrodes for the left ventricular channel.
• the atrial and ventricular channels are also sometimes referred to as rate channels because they can be used to sense the heart's depolarization rate.
• shock channel Another channel, known as the shock channel or shock vector, may be used.
• the shock channel is sensed using electrodes that are also used to delivery high-energy shock therapy.
• the shock channel includes an electrode placed at the superior vena cava (SVC).
• the shock channel includes an electrode placed in the RV. Description of Noise, Undersensing, and Oversensing
• Physiologic noise can also be intracardiac or extracardiac in origin.
• intracardiac physiologic noise includes a low amplitude R-wave or a prolonged Q-T segment of a sensed cardiac activation signal that complicates identification of a T-wave, and dislodgement of a ventricular lead that complicates the sensing and identifying of a P-wave or causes double- counting of an R-wave.
• Extracardiac physiologic noise includes oversensing of abdominal or diaphragmatic myopotentials (DMPs).
• DMPs are electrical activation signals related to contractions of the diaphragm. DMPs may be sensed by the IMD due to the position of implanted leads used to sense cardiac depolarization or due to failure of the insulation of the implanted leads. In the absence of a lead abnormality, oversensing of noise that leads to inappropriate delivery of cardio verting or defibrillating shock therapy is most commonly due to DMPs.
• the DMPs are incorrectly identified by an IMD as ventricular tachyarrhythmia, such as ventricular fibrillation (VF) or VT for example. Consequently, accurately discriminating DMPs from actual
• arrhythmias reduces delivery of inappropriate shocks from devices with cardioverter/defibrillator capability.
• Undersensing is an intrinsic depolarization that is present but not seen or sensed by the device. In a pacemaker, undersensing may be caused by inappropriately programmed sensitivity, lead dislodgment, lead failure (i.e. insulation break, conductor fracture), lead maturation, or change in the native signal. Oversensing is the sensing of an inappropriate signal, which can be either physiologic or non-physiological. Where oversensing occurs, the channel signal may not correspond with the electrogram pattern of the other channel from the same chamber.
• Oversensing may be caused by lead insulation failure, lead fracture, or a lead connection problem.
• An implantable cardiac device configured with one or more sensing channels and associated circuitry for recording and storing electrograms is programmed to generate representative electrograms with respect to time and/or with respect to heart rate.
• the method described herein is used to analyze the electrograms to improve beat detection and to detect undersensing, oversensing, and noise.
• Independent Event Sensing Algorithm is used to analyze the electrograms to improve beat detection and to detect undersensing, oversensing, and noise.
• electrograms from multiple channels are gathered. While data from many different channels can be used and compared, the following example discusses an algorithm using data gathered from the RV and shock channels. Furthermore, while this method may be performed in real-time, electrograms may also be analyzed off-line.
• Removal of far-field sensing from a group of candidate beats is provided in one embodiment.
• the far-field removal is performed on cardiac electrogram data off- line rather than real-time. Performing the far-field removal off-line allows for the electrogram data to be run through and analyzed multiple times. Hence, the method is known as a "multipass" method. In each pass or data analysis run, each candidate beat is examined and different criteria are applied to remove candidate beats that should not be considered heart beats.
• FIG. 3 is flowchart showing an off-line method for analyzing cardiac electrogram data.
• an electrogram is retrieved from a memory location 305 by a computer or other computing device.
• at least one channel from the electrogram is analyzed to identify a first-channel group of candidate beats. In a first embodiment, this channel is the atrial channel. In other embodiments, the ventricular or shock channels may be used.
• at least one other channel from the electrogram is analyzed to identify a second-channel group of candidate beats. In the first embodiment, this channel is the ventricular channel. In other embodiments, this channel may be the atrial or shock channel. Processes for identifying candidate beats will be further described herein.
• the first-channel beat candidates that meet the criteria are determined not to be a beat and removed.
• the pre-determined range is used to define acceptable thresholds for beat detection. In one embodiment, the first pre-determined range is between 20% to 200% of the amplitude from the previous beat or the next beat. In other embodiments, the first pre-determined range may be 10% to 250% of the amplitude from the previous or the next beat.
• a first-channel beat candidate is considered to be "near" a second- channel beat candidate if it occurs within a first time interval from the second-channel beat candidate. In one embodiment, that first time interval is 50 milliseconds.
• the first pre-defined beat count threshold is used to define minimum number of beats that meet the pre-defined criteria. In one embodiment, the first predefined beat count threshold is 10% of the total number of beat candidates. In another embodiment, the first pre-defined beat count threshold is 5% of the total number of beat candidates.
• a second pre-determined range is used to further remove first-channel beat candidates.
• the amplitude of each remaining first-channel beat candidate is compared to the amplitude of both the previous and next first-channel beat candidate.
• Each first-channel beat candidate is evaluated to see if it meets the following criteria: the amplitude of a first-channel beat candidate is outside of a second pre-determined range from either the previous or next beat and the first-channel beat candidate is near a second-channel beat candidate. If the number of first-channel beat candidates which meet the criteria exceeds a second pre-defined beat count threshold, the first-channel beat candidates which meet the criteria are determined not to be a beat and removed.
• FIGS. 4 and 5 Other embodiments of a multi-pass method for removal of far-field sensing are shown in FIGS. 4 and 5. In some embodiments, more than two passes are used in a multi-pass method, as shown in FIGS. 4 and 5. For example, in the multi-pass method 400 shown in FIG. 4, first 410 and second passes 420 are used followed by a third pass 430. After each of steps 410, 420, and 430, the number of identified beats is compared to a first, second, or third beat count threshold in steps 415, 425, and 435, respectively. In step 415, if the number of identified beats exceeds a first beat count threshold, then the identified beats are deleted. In step 425, if the number of identified beats exceeds a second beat count threshold, then the identified beats are deleted. In step 435, if the number of identified beats exceeds a third beat count threshold, then the identified beats are deleted.
• a memory 405 storing electrograms, pacing markers, first- channel beat candidates, and second-channel beat candidates provides data to be analyzed.
• the data is analyzed through the first 410 and second 420 passes, similar to the multi-pass method 300 of FIG. 3.
• a first-channel beat candidate is removed if it is near a second-channel beat and outside of a third pre-determined range from either the previous or next beat and near a second channel beat and the number of beat candidates which meet the criteria exceeds a third pre-determined beat count threshold.
• the third pre- determined range is defined as between 80% and 120% of amplitude from the previous or next candidate beat.
• the third pre-determined range may be defined as 70% or 130% of amplitude from the previous or next candidate beat.
• the third predefined beat count threshold may be 40% of total number of beats. In another embodiment, the third pre-defined beat count threshold may be 50% of the total number of beats.
• an atrial channel is used as a first- channel
• a ventricular channel is used as a second-channel.
• a memory 505 provides storage for electrograms, pacing markers, atrial beat candidates and ventricular beat candidates to be analyzed.
• the first pass 510 in FIG. 5 analyzes each sensed atrial candidate beat and removes sensed atrial candidate beats that are a) not within a first-predetermined range of the previous and next atrial candidate beats and b) near a ventricular beat.
• certain passes may have one or more additional requirements that must be met before first-channel candidate beats are removed, as shown in the second pass 520 and third 530 pass in FIG. 5.
• one pass may have a further requirement that there are at least a predetermined number of similar beats with similar first/second channel intervals in order for first-channel beat candidates to be removed, as with steps 520 and 530.
• the first/second channel interval is defined as the time difference between a first channel beat and the closest second channel beat.
• the first/second channel interval is the AV interval, which is defined as the time difference between the atrial beat to the closest ventricular beat.
• a similarity threshold is defined.
• the similarity threshold may be, for example, 5 milliseconds. In another embodiment, the similarity threshold may be 10 milliseconds. In such an embodiment, it may be required that there be, for example, three or more other atrial beats with similar AV intervals. As shown in the second pass 520 of FIG. 5, assuming the AV interval of the beat is x milliseconds, it is required then that more than three other atrial beats can be found, any of whose AV interval is within 10 milliseconds less than x and 10 milliseconds more than x. While FIG. 5 shows this requirement in the second pass, it may be a requirement of any or all of the passes of the multi-pass method.
• a similar criterion is applied in the third pass 530, but the number of required similar beats is different. Before a candidate beat is removed, it is required that there are more than ⁇ /2 atrial beats with similar AV intervals, where Ny is defined as the total number of ventricular beats (or the total number of beats on the second channel, if another type of channel is used). Where the similarity threshold is 10 milliseconds and the AV interval of the beat is x
• FIG. 5 shows this requirement in the third pass, it may be a requirement of any or all of the passes of the multi-pass method. Any combination of requirements may also be used, as shown in FIG. 5.
• a fourth pass is used (not shown), similar to the first, second, and third passes, wherein the first-channel beat candidate is removed if it is near a second-channel beat and outside of a fourth pre-determined range from either the previous or next beat.
• Other requirements may be used, as described above.
• the number of identified beats is compared to a first, second, or third beat count threshold in steps 515, 525, and 535, respectively.
• step 515 if the number of identified beats exceeds a first beat count threshold, then the identified beats are deleted.
• step 525 if the number of identified beats exceeds a second beat count threshold, then the identified beats are deleted.
• step 535 if the number of identified beats exceeds a third beat count threshold, then the identified beats are deleted.
• multiple passes may be performed to improve the accuracy of the method.
• various pre-determined thresholds are used with varying degrees of restriction.
• the pre-determined thresholds are stricter in later passes than in early passes.
• the second pre-determined range employs a threshold of the amplitude of the neighboring beats that is higher than a threshold of the amplitude of the neighboring beats that is employed in the first pre-determined range.
• the second pre-determined beat count threshold is greater than the first pre-determined beat count.
• the multi-pass method for removing far-field sensing may also be used in conjunction with other steps, including initial beat detection, noise removal, and detecting device oversensing or undersensing. Beat Detection on an EGM Channel
• the groups of candidate beats mentioned in the multi-pass methods described herein may be acquired using beat detection algorithms described herein. These groups of candidate beats originate from any EGM channel (i.e. right or left atrial, right or left ventricular, shock).
• the groups of candidate beats can be used for the first-channel group of candidate beats or second- channel group of candidate beats or any other channel group of candidate beats. In one embodiment, a particular group of candidate beats will originate from a single channel of an EGM.
• a flow chart showing an example of beat detection sensing on one channel of an EGM 600 is shown in FIG. 6.
• the method takes at least one channel from an EGM and pacing markers 605 from a memory storage location and analyzes the signal from the at least one channel to detect and collect a group of beats.
• a first signal adaptive threshold is set.
• the first signal adaptive threshold is set to 21 units.
• the first signal adaptive threshold is set to 0.2 mV.
• the EGM channel data will be referred to as the signal S, where S(i) is the signal value at a position i on the EGM channel signal.
• step 620 at a position i, the absolute value of the signal S(i) is tested to determine if it is a local maxima. If not, the method moves to step 624 and the signal at location i is determined not to be a beat. If the absolute value of the signal S(i) is a local maxima, step 630 is performed at i to determine whether it is located within an estimated beat region.
• the "estimated beat region" is defined as 0.8 ⁇ (i-p)/(p-q) ⁇ 1.2, where p is the position of the previous beat, and q is the position of the beat occurring before p.
• every data sample in the EGM is examined to determine whether it could be a beat location. In another embodiment, beat detection is performed on small (i.e. 100 ms), non- overlapping segments of the EGM.
• An EGM 700 with three signal channels is shown in FIG. 7.
• An example estimated beat region for a particular position i is shown at portion720 of the EGM ventricular channel 712.
• S(i) is analyzed to determine whether it is small noise.
• "Small noise” is identified if there is another signal whose absolute amplitude is larger than twice the absolute value of S(i) within a small noise interval. In one embodiment, the small noise interval is 80 milliseconds. If S(i) is a small noise, the method moves to step 624 and S(i) is declared not to be a beat.
• a low amplitude region is identified when the magnitude of the maximum value (or the absolute value of the minimum value) within a certain interval, known as a low amplitude interval, is smaller than the first signal adaptive threshold.
• a "low amplitude region” may be defined when the magnitude of the maximum value (or the absolute value of the minimum value) within the low amplitude interval is less than the signal adaptive threshold for the channel.
• the first signal adaptive threshold is 21 units
• a low amplitude region is present if the magnitude of the maximum value (or the absolute value of the minimum value) within the low amplitude interval is less than 21.
• the low amplitude interval is 400 milliseconds.
• FIG. 7 also shows a portion of an EGM 730 with three signal channels (atrial 740, ventricular 742, and shock 744).
• An example low amplitude region is shown at portion 750 of the ventricular channel 742 of the EGM.
• the low amplitude region is a 400 millisecond portion of the EGM 730 where i is at the center of the low amplitude region.
• the method moves to step 640 and the first signal adaptive threshold is reset.
• the first signal adaptive threshold may be reset to 33.3% of the amplitude of the previous beat.
• the method moves to step 632 and the first signal adaptive threshold is reset.
• the first signal adaptive threshold is reset based on the maximal amplitude in the low amplitude region.
• the first signal adaptive threshold may be reset to 95% of the maximal amplitude of the low amplitude region.
• the low amplitude region may be 400
• the absolute value of S(i) is compared to the reset first signal adaptive threshold at step 650. If the absolute value of S(i) is not greater than the threshold, the method moves to step 624 and S(i) is declared not to be a beat. If the absolute value of S(i) is greater than the threshold, the algorithm moves to step 660 and determines whether the signal at S(i) is paced and therefore caused by an artificial or device-induced shock. The determination of whether the signal at S(i) is paced is based on the input pacing markers. If the signal at S(i) is paced, S(i) is not an intrinsic beat at step 624. If S(i) is not paced, then S(i) is determined to be an intrinsic beat at step 670.
• the method takes at least one channel from an EGM and pacing markers 805 from a memory storage location and analyzes the signals from the at least one channel to detect and collect a group of beats.
• a signal adaptive threshold is set. In one embodiment, the threshold is set to 21 units.
• the absolute value of the signal S(i) at position i is tested to determine whether it is a local maxima. If it is not a local maximal, the signal is determined not to be a beat at step 870.
• step 830 tests the signal to determine if it is in a low amplitude region.
• a "low amplitude region” is identified when the magnitude of the maximum value (or the absolute value of the minimum value) within a certain interval, known as a low amplitude interval, is smaller than a signal adaptive threshold for a second rate channel.
• the second channel is the atrial channel. If the signal adaptive threshold is 21, a low amplitude region present if the magnitude of the maximum value (or the absolute value of the minimum value) within the low amplitude interval is less than 21. In one embodiment, the low amplitude interval is 400 milliseconds. If S(i) is in a low amplitude region, the adaptive threshold is reset at step 835. In one embodiment, in step 835, the signal adaptive threshold is reset based on the maximal amplitude in the low amplitude region. For example, the first signal adaptive threshold may be reset to 66% of the maximal amplitude of the low amplitude region. In one embodiment, the low amplitude region may be 400 milliseconds.
• the absolute value of S(i) is tested to determine whether its value is greater than the adaptive threshold 840. If not, S(i) is not a beat 870. If the absolute value of S(i) is greater than the adaptive threshold, the algorithm determines whether the signal at S(i) is paced 850 and therefore caused by an artificial or device-induced shock. If yes, S(i) is not an intrinsic beat 870. If S(i) is not paced, then S(i) is an intrinsic beat 860 and is added to the group of candidate beats 870.
• the adaptive threshold can also be adjusted to a lower value as described above. In one embodiment, it could be 70% of the threshold described above.
• these methods of detecting heart beats on at least one channel output of a group of candidate beats can then be used in combination with the far- field sensing removal techniques described above.
• the beat detection is performed first on the RA rate channel, and then the far-field sensing is removed from this channel.
• FIG. 9 shows a method for performing independent, off-line evaluation of event sensing for collected electrograms 900.
• An IMD 902 is used to sense EGM data 910.
• the IMD 902 determines locations of heart beats within the EGM data, resulting in a group of device-identified beat locations 920. These may also be known as device markers. Pacing markers may also be present within the EGM data, indicating when the IMD provided a pacing pulse to the patient's heart. For purposes of this discussion, pacing markers are not considered a type of device- identified beat locations.
• the EGM data and device-identified beat locations are then stored 930 in a memory location 904.
• a computer or other computing device such as a programmer is used to retrieve the EGM data and device-identified beat locations from the memory location 904.
• the computing device determines the locations of heart beats on at least one channel of EGM data using a multi-pass process 950.
• the group of beat locations determined using this multi-pass process is referred to as the multi-pass group of beat locations.
• the computing device then analyzes and compares the device-identified group of beat locations with the multi-pass group of beat locations 960. Based on the comparison, the presence of oversensing,
• undersensing, or noise from the device can potentially be identified 970.
• step 1010 of the multi-pass process 1000 first a preliminary group of beat location candidates within the EGM is identified by the computing device.
• a first pass 1020 a portion of the preliminary group of beat location candidates is eliminated using a first algorithm, resulting in a refined group of beat location candidates.
• a second pass 1030 a portion of the refined group of beat location candidates is eliminated using a second algorithm, resulting in the group of multi-pass beat locations.
• the second algorithm is different than the first algorithm.
• the second algorithm may implement the same formulas but utilize different parameters.
• the second algorithm may utilize the same parameters as the first algorithm but implement different steps.
• the multi-pass methods described above, such as those shown in FIGS. 3, 4, and 5 and described in the accompany text, may also be used in the method for performing independent, off-line evaluation of event sensing for collected electrograms 900.
• the number of detected beats is determined using the multi-pass methods described above, algorithms for detecting undersensing, oversensing, or CRM device noise may be implemented. These algorithms may be implemented off-line with previously recorded data.
• a comparison is made between the number of beats detected by the CRM device ("device- identified group of beats") and the number of sensed beats using the multi-pass methods described above ("multi-pass group of beats").
• the multi-pass group of beats can be from any channel of an EGM. If the number of device-identified beats is less than the number of beat candidates by a certain threshold, device undersensing is present.
• the CRM device may be undersensing. Similarly, if the CRM device senses more than 120% of the number of beats detected using the multi-pass method, the CRM device may be oversensing.
• FIGS. 11A and 11B A comparison showing undersensing sensed by a CRM device versus a multi-pass algorithm is shown in FIGS. 11A and 11B.
• FIG. 11A shows an EGM 1100 from a CRM device.
• FIG. 11B shows output 1120 of the off-line multi-pass algorithm, where the same EGM 1100 of FIG. 11A was used as input to the off-line multi-pass algorithm.
• the CRM device EGM 1100 shows signals from three channels: atrial 1102, ventricular 1104, and shock 1106. Detected beats as determined by the CRM device are indicated along the bottom of the EGM using upward pointing arrows, such as arrow 1108.
• the signal output 1120 from the off-line multipass algorithm also shows signals from the three channels: atrial 1122, ventricular 1124, and shock 1126. Detected beats are indicated on the EGM with a cross mark, such as cross mark 1128 on each of the three channels.
• the CRM sensing misses a beat at EGM portion 1110 (no upward arrow is present to indicate a beat is detected) on the ventricular channel 1104.
• output portion 1130 corresponds to the same timeframe as EGM portion 1110 in FIG. 11 A.
• the off-line multi-pass algorithm detects the beat at portion 1130 on the ventricular channel 1124 as indicated by the cross mark in portion 1130.
• a certain number of beats of a certain type must be sensed before an episode is declared.
• the device delayed declaring a ventricular fibrillation episode until a later beat was detected.
• FIG. 11A shows a declaration of a ventricular fibrillation episode at label 1109 at the bottom right corner of FIG. 11 A. However, if the beat at portion 1110 had been correctly sensed, an episode would have been properly declared at an earlier time.
• FIG. 11B shows the beat that was missed at portion 1110 in FIG. 11A properly sensed at portion 1130 and marked with a cross mark. As undersensing is avoided, episodes are less likely to be declared late.
• FIGS. 12A and 12B A comparison showing oversensing and noise sensed by a device versus the presently disclosed algorithm is shown in FIGS. 12A and 12B.
• the CRM device EGM 1200 shows signals from three channels: atrial 1202, ventricular 1204, and shock 1206.
• detected beats in FIG. 12A are indicated along the bottom of the EGM 1200 using upward pointing arrows.
• FIG. 12B shows output 1220 from a multipass off-line detection algorithm, where EGM 1200 from FIG. 12A was the input to the algorithm. Beats that are declared by the algorithm on FIG. 12B are indicated on the output 1220 with a cross mark.
• FIG. 12A shows a number of detected beats in a portion 1210 of the EGM 1200 that are not in fact beats but rather the result of oversensing by the device.
• Portion 1230 on the algorithm output 1220 generally corresponds to the same timeframe as the position 1210 on the EGM 1200.
• portion 1230 of FIG. 12B there are no beats as determined by the algorithm.
• Portion 1230 of FIG. 12B therefore shows oversensing by the device and shows no detected beats at portion 1230. This evaluation can be performed over a complete EGM or for part of an EGM.
• Noise in the CRM can be detected by checking the discrepancy in the number of beats detected by the CRM device and the number of sensed beats using the multi-pass methods described above. Discrepancies in the morphology, as described in common-assigned
• noise detection algorithm e.g. prior to cross-channel/rate-shock comparison.
• noise detection based on zero- crossing count may be used, as described in U.S. Patent No. 6,917,830, the content of which is herein incorporated by reference.
• a signal saturation check based on determining the occurrence and number of max AD count value reached can be used to detect noise.
• Sources of Cardiac Signal Data Different cardiac rhythm management devices may be used to obtain EGM data from a patient.
• One example of a data-generating device is an implantable cardiac rhythm management device.
• Implantable cardiac rhythm management devices include a pacemaker, a cardioverter-defibrillator, a cardiac resynchronization device, a heart rhythm monitoring device, or the like.
• Other implantable data-generating devices include pressure sensors, heart sound sensors and impedance sensors.
• a data-generating device is one that is capable of providing cardiac signal information about an episode or time period experienced by a particular patient.
• CRM devices communicate with devices located outside of the body, which can receive information from the implanted device including sensor information and information about events, such as when the implanted device has provided therapy.
• the external interface device can also transmit operational parameters to an implanted CRM device, that is, program the device.
• External interface devices can be provided to a patient, often in a patient's home, and can collect information from the implanted device, and provide that information to a computer system designed to monitor the patient's status.
• An exemplary remote patient management system is the LATITUDE ® patient management system, available from Boston Scientific Corporation, Natick, MA. Aspects of exemplary remote patient management and monitoring systems are described in U.S. Pat. No. 6,978,182, the content of which is herein incorporated by reference in its entirety.
• remote patient management systems such as the LATITUDE ® patient management system has provided a large amount of data about patients with implanted medical devices.
• these systems store patient sensor readings including EGM, pressure sensor signals, impedance signals and heart sound signals.
• the sensor readings can include information associated with arrhythmia episodes and other episodes experienced by the patient.
• These systems also store information about patient characteristics, device settings and delivery of therapy by the device.
• a system and method uses this storehouse of patient-related data to analyze the device performance, understand a particular patient, understand a patient population group or improve therapy provided by the device.
• Such a system may operate outside of the device itself, such as on a server that is not at the same location as any of the data-gathering devices. As a result, a large amount of computer processing resources and memory can be devoted to utilizing the patient-related data.
• Episode is defined to mean activity of a patient's body within a time period of particular interest. The time period can be a time when there is abnormal activity, for example, abnormal cardiac activity.
• Episode data is defined to include sensor readings from a medical data-generating device before, during and after the episode, and can also include device settings, actions that were taken by the device and other information. According to the system described herein, an episode database stores episode data about episodes that have occurred.
• One or more data-generating devices can generate episode data.
• the episode database may have episode data about a plurality of episodes generated by one device, or generated by multiple devices.
• the episode database is external to any of the data- generating devices.
• the episode database is located within one of the data generating devices.
• the episode data or part of the episode data for a particular episode can be analyzed using a detection algorithm to detect undersensing, oversensing, or noise.
• Stored episodes comprising cardiac data are analyzed to collect potential beats as beat data.
• Beat data from different channels can be compared and analyzed to detect undersensing, oversensing, and noise.
• the beat data may be stored and associated with their respective episode data.
• the beat data may also be stored in an output database. In some embodiments, the beat data is sent to the data- generating device to be stored. Once undersensing, oversensing, or noise is detected, then it is possible to provide patients and clinicians with many different types of reports related to the episode data.
• the system may detect undersensing, oversensing, or noise that could be indicative of issues that need to be addressed by a health care provider. Alerts may be provided when an issue is detected. Programming recommendations may also be provided, for example, to adjust sensitivity, in response to detected oversensing or undersensing.
• the above-described method can be implemented on various hardware systems, such as on a programmer or in a patient management system. Alternatively, the method can be implemented on a computer or other computing device. Due to its low computational complexity, the method can also be implemented on implantable medical devices. Further detailed embodiments of the hardware of the system will now be described with respect to the attached figures. The method may be applied to all device stored episodes and/or EGMs collected during regular monitoring sessions.
• FIG. 13 is a schematic of an exemplary CRM system 1300.
• the system 1300 can include an implantable medical device 1314 disposed within a patient 1312.
• the implantable medical device 1314 can include pacing functionality.
• the implantable medical device 1314 can be of various types such as, for example, a pacemaker, a cardioverter- defibrillator, a cardiac resynchronization device, a heart rhythm monitoring device, or the like.
• the implantable medical device 1314 can include one or more leads 1322 disposed in or near the patient's heart 1326.
• the implantable medical device 1314 can be in communication with an external interface system 1316.
• communication between the implantable medical device 1314 and the external interface system 1316 can be via inductive communication through a wand 1310 held on the outside of the patient 1312 near the implantable medical device 1314.
• communication can be carried out via radiofrequency transmission, acoustically, or the like.
• the implantable medical device 1314 can include one or more implantable sensors in order to gather data regarding the patient 1312.
• the implantable medical device 1314 can include an activity level sensor, a respiration sensor, a heart sounds sensor, a blood pressure sensor, an impedance sensor, or other sensors.
• the data gathered using the implantable medical device 1314 may be any type of patient data.
• the implantable medical device 1314 collects electrograms from a patient.
• the patient data can further comprise data regarding the locations of heart beats within the electrograms. This data can be collected into groups of device-identified beat locations for each collected electrogram.
• the implantable medical device 1314 can be configured to store data over a period of time, and periodically communicate with the external interface system 1316 in order to transmit some or all of the stored data.
• the external interface system 1316 can be for example, a programmer, a
• the external interface system 1316 can include a user input device, such as a keyboard 1320 and/or a mouse 1328.
• the external interface system 1316 can include a video output channel and video output device, such as a video display 1318 for displaying video output.
• the displayed video output can include a user interface screen.
• the video display 1318 can also be equipped with a touch screen, making it into a user input device as well.
• the external interface device 1316 can display real-time data and/or stored data graphically, such as in charts or graphs, and textually through the user interface screen. In addition, the external interface device 1316 can present textual information to a user along with several response options. The external interface device 1316 can also input and store a user's response to a question, and can store a user's text response in some embodiments.
• the external interface device 1316 which can also be referred to as a user interface, is in communication with a patient management computer system 1332.
• the communication link between the user interface 1316 and the patient management computer system 1332 may be via phone lines, the Internet 1330, or any other data connection.
• the user interface 1316 can also be used when it is not in communication with a device, but is only in communication with the patient management computer system 1332.
• the external interface device 1316 is capable of changing the operational parameters of the implantable medical device 1314, and is therefore referred to as a programmer.
• programmers are used to interface with CRM devices in a clinic or hospital setting.
• the user of the external interface device is a physician or trained technician.
• FIG. 14 is a schematic illustration of a patient management system consistent with at least one embodiment of the invention.
• the patient management system is capable of maintaining an episode database using computer storage medium.
• the episode database may also be present in an implantable or implanted device as discussed further herein.
• a computer storage medium is any technology, including devices and materials, used to place, keep and retrieve data. Examples of computer storage medium include random-access memory (RAM), a network-attached storage device, magnetic storage such as hard disk drives, optical discs, and a redundant array of independent discs (RAID).
• Patient management system 1400 generally includes one or more devices 1402, 1404, and 1406, one or more external interface devices 1408, a communication system 1410, one or more remote peripheral devices 1409, and a host 1412.
• the host 1412 may be a single computing device, such as a programmer or other patient management device.
• the host 1412 is an external device that communicates directly with the one or more devices 1402, 1404, and 1406 and does not require the use of separate external interface devices 1408.
• the host is an external device and receives data, such as EGM data, from an external database 1480.
• the patient management system 1400 can communicate using the communication system 1410. Some components may also communicate directly with one another.
• the various components of the example patient management system 1400 illustrated herein are described below.
• the patient management system 1400 may be a single device or comprise multiple devices. In one embodiment, the patient management system 1400 is a single external computing device.
• Data-generating devices 1402, 1404, and 1406 can be implantable devices or external devices that may provide one or more of the following functions with respect to a patient: (1) sensing, (2) data analysis, and (3) therapy.
• devices 1402, 1404, and 1406 are either implanted or external devices used to measure a variety of physiological, subjective, and environmental conditions of a patient using electrical, mechanical, and/or chemical means.
• the devices 1402, 1404, and 1406 can be configured to automatically gather data or can require manual intervention by the patient or another person.
• the devices 1402, 1404, and 1406 can be configured to store data related to the physiological and/or subjective measurements and/or transmit the data to the communication system 1410 using a variety of methods, described in detail below.
• each of the devices 1402, 1404 and 1406 is serving a different patient. In one embodiment, two or more devices are serving a single patient.
• the devices 1402, 1404, and 1406 can be configured to analyze the measured data and act upon the analyzed data.
• the devices 1402, 1404, and 1406 can be configured to modify therapy or provide an alarm based on the analysis of the data.
• devices 1402, 1404, and 1406 provide therapy. Therapy can be provided automatically or in response to an external communication. Devices 1402, 1404, and 1406 are programmable in that the characteristics of their sensing, therapy (e.g., duration and interval), or communication can be altered by communication between the devices 1402, 1404, and 1406 and other components of the patient management system 1400. Devices 1402, 1404, and 1406 can also perform self-checks or be interrogated by the communication system 1410 to verify that the devices are functioning properly. Examples of different embodiments of the devices 1402, 1404, and 1406 are provided herein.
• Implantable devices implanted within the body have the ability to sense and communicate as well as to provide therapy.
• Implantable devices can provide direct measurement of characteristics of the body, including, without limitation, electrical cardiac activity (e.g., a pacemaker, cardiac resynchronization management device, defibrillator, etc.), physical motion, temperature, heart rate, activity, blood pressure, breathing patterns, ejection fractions, blood viscosity, blood chemistry, blood glucose levels, and other patient-specific clinical physiological parameters, while minimizing the need for patient compliance.
• electrical cardiac activity e.g., a pacemaker, cardiac resynchronization management device, defibrillator, etc.
• physical motion e.g., temperature, heart rate, activity, blood pressure, breathing patterns, ejection fractions, blood viscosity, blood chemistry, blood glucose levels, and other patient-specific clinical physiological parameters, while minimizing the need for patient compliance.
• Derived measurements can also be determined from the implantable device sensors (e.g., a sleep sensor, functional capacity indicator, autonomic tone indicator, sleep quality indicator, cough indicator, anxiety indicator, and cardiovascular wellness indicator for calculating a quality of life indicator quantifying a patient's overall health and well-being).
• implantable device sensors e.g., a sleep sensor, functional capacity indicator, autonomic tone indicator, sleep quality indicator, cough indicator, anxiety indicator, and cardiovascular wellness indicator for calculating a quality of life indicator quantifying a patient's overall health and well-being.
• Devices 1402, 1404, and 1406 can also be external devices, or devices that are not implanted in the human body, that are used to measure physiological data (e.g., a thermometer, sphygmomanometer, or external devices used to measure blood characteristics, body weight, physical strength, mental acuity, diet, heart characteristics, and relative geographic position).
• the patient management system 1400 may also include one or more remote peripheral devices 1409 (e.g., cellular telephones, pagers, PDA devices, facsimiles, remote computers, printers, video and/or audio devices) that use wired or wireless technologies to communicate with the communication system 1410 and/or the host 1412.
• remote peripheral devices 1409 e.g., cellular telephones, pagers, PDA devices, facsimiles, remote computers, printers, video and/or audio devices
• the database module 1414 comprises memory for storing patient data.
• the patient data can include electrogram data, which comprises groups of device-identified beat locations for the electrogram data. This data may be received from a patient device, such as an implantable medical device, or it may be retrieved from another database 1480.
• the example database module 1414 includes a patient database 1440 and an episode database 1442, which are described further below.
• the patient database 1440 includes patient specific data, including data acquired by the devices 1402, 1404, and 1406, such as electrogram data, as well as a patient's medical records and historical information.
• the episode database 1442 has episode data regarding a plurality of different episodes generated from those of devices 1402, 1404, and 1406 that provide episode data.
• the episode database 1442 may also store data analyzed by the analysis module 1416.
• Information can also be provided from an external source, such as external database 1480.
• the external database 1480 could include external medical records maintained by a third party, such as drug prescription records maintained by a pharmacy, providing information regarding the type of drugs that have been prescribed for a patient or, in another example, authorization data from patient groups that have authorized users to view arrhythmia episode data.
• the external database 1480 may also store patient data that was previously acquired by an implantable or external medical device.
• One example of stored patient data on an external database 1480 is electrogram data.
• the example analysis module 1416 includes a patient analysis module 1450 and a device analysis module 1452.
• Patient analysis module 1450 may utilize information collected by the patient management system 1400, as well as information for other relevant sources, to analyze data related to a patient and provide timely and predictive assessments of the patient's well- being.
• Device analysis module 1452 analyzes data from the devices 1402, 1404, and 1406 and external interface devices 1408 to predict and determine device issues or failures.
• the device analysis module 1452 may analyze electrogram data to determine locations of heart beats on one or more channels using the multi-pass methods described above.
• the device analysis module 1452 can further compare device-identified beats and beat locations to beats and beat locations determined using the multi-pass method.
• the device analysis module 1452 can then perform comparisons to find the presence of noise, oversensing, and undersensing by the device, as described above.
• the analysis module 1416 further includes an adjudication processor 1458, and episode processor 1460 and an overwrite processor 1462.
• the adjudication processor is operatively connected to at least the episode database 1442 and is configured to receive as input episode data regarding one of the different episodes.
• the episode processor 1460 performs processing of the adjudication database such as in order to provide reports, patient alerts, or programming recommendations.
• the overwrite processor 1462 can analyze data provided from the episode database 1442, and other portions of the patient management system 1400 to determine what particular portion of episode data for one of the episodes from the episode database should be displayed to a user. Overwrite processor 1462 can, through the delivery module 1418 described below, provide the means for graphically displaying a portion of data selected from arrhythmia episode data related to an episode of a patient, such as data generated by a data-generating device and stored in the episode database.
• Overwrite processor 1462 also requests from a user any changes in the characterization data determined by the adjudication processor, and can articulate the request for user input characterizing an episode.
• the request may be a direct question to a user, a series of choices provided to the user, or simply a blank space on the user interface configured to accommodate the user input.
• the overwrite processor 1462 may also store the overwrite history for individual users.
• One or more portions of the analysis module 1416 may be located remotely from other parts of the patient management system 1400.
• a microprocessor of a data-generating device may also serve as an adjudication processor in some embodiments.
• Delivery module 1418 coordinates the delivery of reports, patient alerts or programming recommendations based on the analysis performed by the host 1412. For example, based on the data collected from the devices and analyzed by the host 1412, the delivery module 1418 can deliver information to the caregiver, user, or to the patient using, for example, a display provided on the external interface device 1408.
• a user interface device 1490 that is independent of a data- generating device may also be used to deliver information.
• the external interface device 1408 and user interface device 1490 are also configured, according to multiple embodiments, to display a report, alert, or programming recommendation, receive overwrite information from a user, and receive other data from the user. Displayed data, as described above, can be determined by the episode processor 1460, overwrite processor 1462 and delivery module 1418.
• External interface devices 1408 to display information such as
• programmer/recorder/monitors can include components common to many computing devices.
• User interface devices 1490 to display and received information from users can also include components common to many computing devices.
• FIG. 15 a diagram of various components is shown in accordance with some embodiments of the invention. However, it is not required that an external interface device have all of the components illustrated in FIG. 15.
• the external interface device includes a central processing unit (CPU) 1505 or processor, which may include a conventional microprocessor, random access memory (RAM) 1510 for temporary storage of information, and read only memory (ROM) 1515 for permanent storage of information.
• CPU central processing unit
• RAM random access memory
• ROM read only memory
• a memory controller 1520 is provided for controlling system RAM 1510.
• a bus controller 1525 is provided for controlling data bus 1530, and an interrupt controller 1535 is used for receiving and processing various interrupt signals from the other system components.
• Mass storage can be provided by diskette drive 1541, which is connected to bus 1530 by controller 1540, CD-ROM drive 1546, which is connected to bus 1530 by controller 1545, and hard disk drive 1551, which is connected to bus 1530 by controller 1550.
• User input to the programmer system may be provided by a number of devices.
• a keyboard and mouse can connected to bus 1530 by keyboard and mouse controller 1555.
• DMA controller 1560 is provided for performing direct memory access to system RAM 1510.
• a visual display is generated by a video controller 1565 or video output, which controls video display 1570.
• the external system can also include a telemetry interface 1590 or telemetry circuit which allows the external system to interface and exchange data with an implantable medical device. It will be appreciated that some embodiments may lack various elements illustrated in FIG. 15.
• the implantable medical device 1600 can include a controller made up of a microprocessor 1610 communicating with a memory 1612, where the memory 1612 may comprise a ROM (read-only memory) for program storage and a RAM (random-access memory) for data storage.
• the controller could be implemented by other types of logic circuitry (e.g., discrete components or programmable logic arrays) using a state machine type of design, but a microprocessor-based system is preferable.
• the controller is capable of operating the implantable medical device 1600 in a number of programmed modes where a programmed mode defines how pacing pulses are output in response to sensed events and expiration of time intervals.
• a telemetry interface 1680 is provided for communicating with an external programmer 1675.
• the external programmer is a computerized device with a controller 1677 that can interrogate the implantable medical device 1600 and receive stored data as well as adjust the operating parameters of the pacemaker.
• the implantable medical device 1600 has an atrial sensing/pacing channel comprising ring electrode 1633 A tip electrode 1633B sense amplifier 1631, pulse generator 1632, and an atrial channel interface 1630 which communicates bi-directionally with a port of microprocessor 1610.
• the device also has two ventricular sensing/pacing channels that similarly include ring electrodes 1643 A and 1653 A tip electrodes 1643B and 1653B sense amplifiers 1641 and 1651, pulse generators 1642 and 1652, and ventricular channel interfaces 1640 and 1650.
• the electrodes are connected to the implantable medical device 1600 by a lead and used for both sensing and pacing.
• a MOS switching network 1670 controlled by the microprocessor is used to switch the electrodes from the input of a sense amplifier to the output of a pulse generator.
• a shock channel is also provided comprising a shock pulse generator 1690 and shock electrodes 1691 A and 169 IB that enables the device to deliver a defibrillation shock to the heart when fibrillation or other tachyarrhythmia is detected.
• the implantable medical device 1600 also has an evoked response sensing channel that comprises an evoked response channel interface 1620 and a sense amplifier 1621 that has its differential inputs connected to a unipolar electrode 1623 and to the device housing or can 1660 through the switching network 1670.
• the evoked response sensing channel may be used to verify that a pacing pulse has achieved capture of the heart in a conventional manner or, as explained below, used to record an evoked response electrogram.
• the channel interfaces include analog-to-digital converters for digitizing sensing signal inputs from the sensing amplifiers, registers that can be written to for adjusting the gain and threshold values of the sensing amplifiers, and, in the case of the ventricular and atrial channel interfaces, registers for controlling the output of pacing pulses and/or adjusting the pacing pulse energy by changing the pulse amplitude or pulse width.
• the microprocessor 1610 controls the overall operation of the device in accordance with programmed instructions stored in memory.
• the above-described method can be regularly initiated to evaluate the sensing
• the method can also be triggered by events, such as when ventricular arrhythmia episodes are detected, when mode switch due to atrial arrhythmias occurs, and when atrial arrhythmia episodes are detected.
• the device may store the electrogram and provide to physician for review. Programming recommendations may also be made.
• arrhythmia therapy may be withheld due to detection of issues. Gathered data may be used as input for other device functionality, such as arrhythmia adjudication.

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