Source: https://insight.rpxcorp.com/pat/US8942791B2
Timestamp: 2019-11-18 09:26:01
Document Index: 533728608

Matched Legal Cases: ['art105', 'art105', 'art105', 'art105', 'art105', 'art105', 'art105', 'art105', 'art1326']

Patent US 8,942,791 B2
Off-line sensing method and its applications in detecting undersensing, oversensing, and noise
US 8,942,791 B2
after retrieving the electrogram from the memory location, determining, by the external computing device, locations of heart beats on at least a first channel of the electrogram using a multi-pass process, resulting in a group of multi-pass beat locations, wherein the multi-pass process comprises;
Methods and Systems for Characterizing Cardiac Signal Morphology Using K-Fit Analysis
US 20110077541A1
US 20100204745A1
Method for automatically adjusting the sensitivity of cardiac sense amplifiers
US 6,112,119 A
US 20120271185A1
SYSTEM AND METHOD FOR OFF-LINE ANALYSIS OF CARDIAC DATA
US 20130138005A1
FIG. 1is an illustration of portions of a system that use an implantable medical device.
FIG. 2is an electrogram showing an example of far-field sensing on the right atrial (RA) channel.
FIG. 3is a flowchart showing an off-line method for analyzing cardiac electrogram data.
FIG. 4is a flowchart of an embodiment of a multi-pass method.
FIG. 5is a flowchart of an alternative embodiment of a multi-pass method for use with atrial and ventricular beats.
FIG. 6is a flowchart showing one embodiment of beat detection on one channel of an electrogram.
FIG. 7shows an electrogram where an example estimated beat region is labeled (above) and an electrogram where an example low amplitude region is labeled (below).
FIG. 8is a flowchart showing another embodiment of beat detection on one channel of an electrogram.
FIG. 9is a flowchart showing one embodiment of an independent, off-line evaluation of event sensing for collected electrograms.
FIG. 10is a flowchart of an alternative embodiment of a multi-pass method.
FIG. 11Ashows an electrogram with data sensed by a CRM device. FIG. 11Bshows an electrogram using the data fromFIG. 11Athat 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. 12Ashows an electrogram with data sensed by a CRM device. FIG. 12Bshows an electrogram using the data fromFIG. 12Athat 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. 13is a schematic diagram of an exemplary implementation of a cardiac rhythm management (CRM) system, including an implanted CRM device, an external interface device, and a patient management computer system, consistent with at least one embodiment of the invention.
FIG. 14is a schematic illustration of a patient management system consistent with at least one embodiment of the invention.
FIG. 15is a schematic diagram of an implementation of the components of an external interface device such as a programmer, in accordance with various embodiments.
FIG. 16is a block diagram of an implantable medical device.
FIG. 1is an illustration of portions of a system that uses an implanted medical device (IMD) 110. Other types of cardiac rhythm management (CRM) devices may also be used including external medical devices. Examples of IMDs110include, 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 device170that communicates wireless signals190with the IMD110, such as by using radio frequency (RF) or other telemetry signals.
The IMD110is coupled by one or more leads108A-C to the heart105. Cardiac leads108A-C include a proximal end that is coupled to IMD110and a distal end, coupled by an electrode or electrodes to one or more portions of a heart105. The electrodes typically deliver cardioversion, defibrillation, pacing, or resynchronization therapy, or combinations thereof to at least one chamber of the heart105. The electrodes may be electrically coupled to sense amplifiers to sense electrical cardiac signals.
The heart105includes a right atrium100A, a left atrium100B, a right ventricle105A, a left ventricle105B, and a coronary sinus extending from right atrium100A. The atrial lead108A includes electrodes (electrical contacts, such as ring electrode125and tip electrode130) disposed in the right atrium100A of heart105for sensing signals, or delivering pacing therapy, or both, to the right atrium100A.
The ventricular lead108B includes one or more electrodes, such as tip electrode135and ring electrode140, for sensing signals, delivering pacing therapy, or both sensing signals and delivering pacing therapy. The lead108B optionally also includes additional electrodes, such as for delivering atrial cardioversion, atrial defibrillation, ventricular cardioversion, ventricular defibrillation, or combinations thereof to the heart105. Such electrodes typically have larger surface areas than pacing electrodes in order to handle the larger energies involved in defibrillation. The lead108B optionally provides resynchronization therapy to the heart105.
The IMD110may include a third cardiac lead108C attached to the IMD110through the header155. The third cardiac lead108C includes ring electrodes160, 165placed in a coronary vein lying epicardially on the left ventricle (LV) 105B via the coronary vein120.
The lead108B may include a first defibrillation coil electrode175located proximal to tip and ring electrodes135, 140for placement in a right ventricle (RV), and a second defibrillation coil electrode180located proximal to the first defibrillation coil175, tip electrode135, and ring electrode140for placement in the superior vena cava (SVC). In some examples, high-energy shock therapy is delivered from the first or RV coil175to the second or SVC coil180. In some examples, the SVC coil180is electrically tied to an electrode formed on the hermetically-sealed IMD can150. This improves defibrillation by delivering current from the RV coil175more uniformly over the ventricular myocardium. In some examples, the therapy is delivered from the RV coil175only to the electrode formed on the IMD can150.
Other forms of electrodes include meshes and patches which may be applied to portions of heart105or which may be implanted in other areas of the body to help “steer” electrical currents produced by IMD110. The present methods and systems will work in a variety of configurations and with a variety of electrical contacts or “electrodes.” Sensing among different sets of electrodes often provides directional information regarding the propagation of cardiac signals and is often referred to as sensing among different vectors. For example, in a single chamber ICD, sensing from a right ventricular tip electrode135to a right ventricular ring electrode140would be a first vector, and sensing from an RV coil175to an electrode on the can150, or a header155, would be second vector. Various electrode configurations may be used, as described in co-pending and commonly-assigned U.S. Publication No. 2010/0204745, filed Jan. 26, 2010, the contents of which are herein incorporated by reference.
The sensing of signals by the IMD110may be susceptible to noise. The signal noise may be physiologic or non-physiologic in nature. Non-physiologic signal noise may be intracardiac in origin due to a separate electronic device providing electrical therapy. The IMD110may sense the therapy. Intracardiac non-physiologic signal noise may also be due to the sensing electrode or lead making electrical contact with an abandoned lead fragment.
Other factors are also of concern with cardiac devices. Generally, the larger the detected signal, the better. In certain devices, if the signal passed through the right ventricular (RV) lead (R wave) is too small, then a pacemaker or other IMD may not sense intrinsic rhythm and begin pacing inappropriately, creating a risk for arrhythmia. Furthermore, if the R wave is too small and a particular device is programmed to account for small signals, there is a possibility that the lead will detect a signal that is not ventricular activity and incorrectly not pace the ventricle when the patient is potentially asystolic. In the case of the right atrial (RA) lead, both the size of the signal sensed on the RA lead (P wave) and the presence of distant but detectable ventricular activity are significant. The distant ventricular activity that is still detected by the atrial lead is known as far-field sensing. The EGM200ofFIG. 2shows an example far-field signal210on the right atrial (RA) channel220. The ventricular channel230and shock channel240are also shown on the EGM200. If far-field activity is too large relative to an atrial signal, an IMD may sense both the far-field and the atrial signal and determine that both are atrial signals, counting two beats instead of one. The methods described herein seek to detect and remove far-field signals to improve the accuracy of beat detection.
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 “multi-pass” 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. One example of the multi-pass method300with two total passes is shown inFIG. 3. FIG. 3is flowchart showing an off-line method for analyzing cardiac electrogram data.
In step310, an electrogram is retrieved from a memory location305by a computer or other computing device. In step320, 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. Next, in step330, 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 and second channels are then used in a multi-pass process for the purpose of removing far-field sensing. In a first pass340, a first pre-determined range is used to remove first-channel beat candidates. During the first pass340, the amplitude of each 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 first pre-determined range from either the previous or the next beat and the first-channel beat candidate is near a second-channel beat candidate. In some embodiments, if the number of first-channel beat candidates which meet the criteria exceeds a first pre-defined beat count threshold, 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. In other embodiments, that time interval is 100 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 pre-defined 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.
In a second pass350of the multi-pass process, a second pre-determined range is used to further remove first-channel beat candidates. During the second pass350, 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. In one embodiment, the second pre-determined range is defined as beats with amplitudes that are between 40% to 180% of the amplitude of the previous candidate beat or the next candidate beat. In other embodiments, the second pre-determined range may be between 50% to 150% of the amplitude of the previous and next candidate beat. In one embodiment, the second pre-defined beat count threshold is 20% of the total number of beat candidates. In another embodiment, the second pre-defined beat count threshold is 30% of the total number of beat candidates.
Other embodiments of a multi-pass method for removal of far-field sensing are shown inFIGS. 4 and 5. In some embodiments, more than two passes are used in a multi-pass method, as shown inFIGS. 4 and 5. For example, in the multi-pass method400shown inFIG. 4, first410and second passes420are used followed by a third pass430. After each of steps410, 420, and430, the number of identified beats is compared to a first, second, or third beat count threshold in steps415, 425, and435, respectively. In step415, if the number of identified beats exceeds a first beat count threshold, then the identified beats are deleted. In step425, if the number of identified beats exceeds a second beat count threshold, then the identified beats are deleted. In step435, if the number of identified beats exceeds a third beat count threshold, then the identified beats are deleted.
In the multi-pass method400, a memory405storing electrograms, pacing markers, first-channel beat candidates, and second-channel beat candidates provides data to be analyzed. The data is analyzed through the first410and second420passes, similar to the multi-pass method300ofFIG. 3. In the third pass430, 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. In one embodiment, the third pre-determined range is defined as between 80% and 120% of amplitude from the previous or next candidate beat. In other embodiments, the third pre-determined range may be defined as 70% or 130% of amplitude from the previous or next candidate beat. In one embodiment, the third pre-defined 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.
In the multi-pass method500shown inFIG. 5, an atrial channel is used as a first-channel, and a ventricular channel is used as a second-channel. A memory505provides storage for electrograms, pacing markers, atrial beat candidates and ventricular beat candidates to be analyzed. Similar to the embodiment shown inFIG. 4, the first pass510inFIG. 5analyzes 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. In certain embodiments, 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 pass520and third530pass inFIG. 5. For example, 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 steps520and530. The first/second channel interval is defined as the time difference between a first channel beat and the closest second channel beat. For example, in an embodiment where the first channel is an atrial channel and the second channel is a ventricular channel, shown inFIG. 5, 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. In order for there to be a certain number of “similar” beats with similar AV intervals (or first/second channel intervals, generally), 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 pass520ofFIG. 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. WhileFIG. 5shows 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 pass530, but the number of required similar beats is different. Before a candidate beat is removed, it is required that there are more than NV/2 atrial beats with similar AV intervals, where NVis 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 milliseconds, it is required that more than NV/2 other atrial beats can be found, any of whose AV interval is within 10 milliseconds less than x and 10 milliseconds more than x. WhileFIG. 5shows 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 inFIG. 5.
After each of steps510, 520, and530, the number of identified beats is compared to a first, second, or third beat count threshold in steps515, 525, and535, respectively. In step515, if the number of identified beats exceeds a first beat count threshold, then the identified beats are deleted. In step525, if the number of identified beats exceeds a second beat count threshold, then the identified beats are deleted. In step535, if the number of identified beats exceeds a third beat count threshold, then the identified beats are deleted.
A flow chart showing an example of beat detection sensing on one channel of an EGM600is shown inFIG. 6. The method takes at least one channel from an EGM and pacing markers605from a memory storage location and analyzes the signal from the at least one channel to detect and collect a group of beats. First in step610, a first signal adaptive threshold is set. In one embodiment, the first signal adaptive threshold is set to 21 units. In another embodiment, 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. Next, at step620, 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 step624and 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, step630is performed at i to determine whether it is located within an estimated beat region. In one embodiment, the “estimated beat region” is defined as 0.8
An EGM700with three signal channels (atrial710, ventricular712, and shock714) is shown inFIG. 7. An example estimated beat region for a particular position i is shown at portion720of the EGM ventricular channel712. Now referring back toFIG. 6, if i is not located in the estimated beat region, the method moves to step626and 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 step624and S(i) is declared not to be a beat. If S(i) is not a small noise, the method moves to step628and i is analyzed to determine whether it is located 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 the first signal adaptive threshold. For example, 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. That is, if 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. In one embodiment, the low amplitude interval is 400 milliseconds.
FIG. 7also shows a portion of an EGM730with three signal channels (atrial740, ventricular742, and shock744). An example low amplitude region is shown at portion750of the ventricular channel742of the EGM. In this embodiment, the low amplitude region is a 400 millisecond portion of the EGM730where i is at the center of the low amplitude region.
Again referring back toFIG. 6, if S(i) is a local maxima and in the estimated beat region, the method moves to step640and the first signal adaptive threshold is reset. For example, the first signal adaptive threshold may be reset to 33.3% of the amplitude of the previous beat. If the signal S(i) is not small noise and i is located in a low amplitude region, then the method moves to step632and the first signal adaptive threshold is reset. In one embodiment, in step632, the first 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 95% of the maximal amplitude of the low amplitude region. In one embodiment, the low amplitude region may be 400 milliseconds. In either case, regardless of whether i is located in a low amplitude region, the absolute value of S(i) is compared to the reset first signal adaptive threshold at step650. If the absolute value of S(i) is not greater than the threshold, the method moves to step624and 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 step660and 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 step624. If S(i) is not paced, then S(i) is determined to be an intrinsic beat at step670.
One alternative embodiment of the method used for initial beat detection800is shown inFIG. 8. The method takes at least one channel from an EGM and pacing markers805from a memory storage location and analyzes the signals from the at least one channel to detect and collect a group of beats. First, in step810a signal adaptive threshold is set. In one embodiment, the threshold is set to 21 units. Then in step820, 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 step870.
If S(i) is a local maxima, step830tests 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. In one embodiment 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 step835. In one embodiment, in step835, 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.
In either case, the absolute value of S(i) is tested to determine whether its value is greater than the adaptive threshold840. If not, S(i) is not a beat870. If the absolute value of S(i) is greater than the adaptive threshold, the algorithm determines whether the signal at S(i) is paced850and therefore caused by an artificial or device-induced shock. If yes, S(i) is not an intrinsic beat870. If S(i) is not paced, then S(i) is an intrinsic beat860and is added to the group of candidate beats880.
FIG. 9shows a method for performing independent, off-line evaluation of event sensing for collected electrograms900. An IMD902is used to sense EGM data910. The IMD902determines locations of heart beats within the EGM data, resulting in a group of device-identified beat locations920. 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 stored930in a memory location904. Next, 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 location940. The computing device determines the locations of heart beats on at least one channel of EGM data using a multi-pass process950. 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 locations960. Based on the comparison, the presence of oversensing, undersensing, or noise from the device can potentially be identified970.
One embodiment of the multi-pass process of step950is shown in the flow chart ofFIG. 10. In step1010of the multi-pass process1000, first a preliminary group of beat location candidates within the EGM is identified by the computing device. In a first pass1020, 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. In a second pass1030, 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. For example, the second algorithm may implement the same formulas but utilize different parameters. Alternatively, 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 inFIGS. 3, 4, and5and described in the accompany text, may also be used in the method for performing independent, off-line evaluation of event sensing for collected electrograms900.
A comparison showing undersensing sensed by a CRM device versus a multi-pass algorithm is shown inFIGS. 11A and 11B. FIG. 11Ashows an EGM1100from a CRM device. FIG. 11Bshows output1120of the off-line multi-pass algorithm, where the same EGM1100ofFIG. 11Awas used as input to the off-line multi-pass algorithm. The CRM device EGM1100shows signals from three channels: atrial1102, ventricular1104, and shock1106. Detected beats as determined by the CRM device are indicated along the bottom of the EGM using upward pointing arrows, such as arrow1108. The signal output1120from the off-line multi-pass algorithm also shows signals from the three channels: atrial1122, ventricular1124, and shock1126. Detected beats are indicated on the EGM with a cross mark, such as cross mark1128on each of the three channels.
As shown in the CRM device EGM1100, the CRM sensing misses a beat at EGM portion1110 (no upward arrow is present to indicate a beat is detected) on the ventricular channel1104. In the off-line multi-pass algorithm output1120ofFIG. 11B, output portion1130corresponds to the same timeframe as EGM portion1110inFIG. 11A. The off-line multi-pass algorithm detects the beat at portion1130on the ventricular channel1124as indicated by the cross mark in portion1130. In some algorithms, a certain number of beats of a certain type must be sensed before an episode is declared. As a result of missing the beat at1110, the device delayed declaring a ventricular fibrillation episode until a later beat was detected. The EGM1100inFIG. 11Ashows a declaration of a ventricular fibrillation episode at label1109at the bottom right corner ofFIG. 11A. However, if the beat at portion1110had been correctly sensed, an episode would have been properly declared at an earlier time. FIG. 11Bshows the beat that was missed at portion1110inFIG. 11Aproperly sensed at portion1130and marked with a cross mark. As undersensing is avoided, episodes are less likely to be declared late.
If the number of device-identified beats is more than the number of beat candidates by a certain threshold, device oversensing is possible. A comparison showing oversensing and noise sensed by a device versus the presently disclosed algorithm is shown inFIGS. 12A and 12B. The CRM device EGM1200shows signals from three channels: atrial1202, ventricular1204, and shock1206. As withFIG. 11A, detected beats inFIG. 12Aare indicated along the bottom of the EGM1200using upward pointing arrows. FIG. 12Bshows output1220from a multi-pass off-line detection algorithm, where EGM1200fromFIG. 12Awas the input to the algorithm. Beats that are declared by the algorithm onFIG. 12Bare indicated on the output1220with a cross mark.
FIG. 12Ashows a number of detected beats in a portion1210of the EGM1200that are not in fact beats but rather the result of oversensing by the device. Portion1230on the algorithm output1220generally corresponds to the same timeframe as the position1210on the EGM1200. In portion1230ofFIG. 12B, there are no beats as determined by the algorithm. Portion1230ofFIG. 12Btherefore shows oversensing by the device and shows no detected beats at portion1230. This evaluation can be performed over a complete EGM or for part of an EGM.
One embodiment of a data-generating device is a CRM device, as will now be described with reference toFIG. 13, which is a schematic of an exemplary CRM system1300. The system1300can include an implantable medical device1314disposed within a patient1312. The implantable medical device1314can include pacing functionality. The implantable medical device1314can 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. In some embodiments, the implantable medical device1314can include one or more leads1322disposed in or near the patient's heart1326.
The implantable medical device1314can be in communication with an external interface system1316. In some embodiments, communication between the implantable medical device1314and the external interface system1316can be via inductive communication through a wand1310held on the outside of the patient1312near the implantable medical device1314. However, in other embodiments, communication can be carried out via radiofrequency transmission, acoustically, or the like.
The implantable medical device1314can include one or more implantable sensors in order to gather data regarding the patient1312. For example, the implantable medical device1314can 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 device1314may be any type of patient data. In one embodiment, the implantable medical device1314collects 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 device1314can be configured to store data over a period of time, and periodically communicate with the external interface system1316in order to transmit some or all of the stored data.
The external interface system1316can be for example, a programmer, a programmer/recorder/monitor device, a computer, a patient management system, a personal digital assistant (PDA), or the like. As used herein, the term programmer refers to a device that programs implanted devices, records data from implanted devices, and allows monitoring of the implanted device. Exemplary programmer/recorder/monitor devices include the Model 3120 Programmer, available from Boston Scientific Corporation, Natick, Mass. The external interface system1316can include a user input device, such as a keyboard1320and/or a mouse1328. The external interface system1316can include a video output channel and video output device, such as a video display1318for displaying video output. The displayed video output can include a user interface screen. In addition, the video display1318can also be equipped with a touch screen, making it into a user input device as well.
The external interface device1316can 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 device1316can present textual information to a user along with several response options. The external interface device1316can also input and store a user's response to a question, and can store a user's text response in some embodiments.
In one embodiment, the external interface device1316, which can also be referred to as a user interface, is in communication with a patient management computer system1332. The communication link between the user interface1316and the patient management computer system1332may be via phone lines, the Internet1330, or any other data connection. The user interface1316can also be used when it is not in communication with a device, but is only in communication with the patient management computer system1332.
In one embodiment, the external interface device1316is capable of changing the operational parameters of the implantable medical device1314, and is therefore referred to as a programmer. Typically, programmers are used to interface with CRM devices in a clinic or hospital setting. In this context, the user of the external interface device is a physician or trained technician.
FIG. 14is 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. Of note, 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 system1400generally includes one or more devices1402, 1404, and1406, one or more external interface devices1408, a communication system1410, one or more remote peripheral devices1409, and a host1412. The host1412may be a single computing device, such as a programmer or other patient management device. In some embodiments, the host1412is an external device that communicates directly with the one or more devices1402, 1404, and1406and does not require the use of separate external interface devices1408. In some embodiments, the host is an external device and receives data, such as EGM data, from an external database1480.
Each component of the patient management system1400can communicate using the communication system1410. Some components may also communicate directly with one another. The various components of the example patient management system1400illustrated herein are described below. The patient management system1400may be a single device or comprise multiple devices. In one embodiment, the patient management system1400is a single external computing device.
Data-generating devices1402, 1404, and1406can 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. For example, in one embodiment, devices1402, 1404, and1406are 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 devices1402, 1404, and1406can be configured to automatically gather data or can require manual intervention by the patient or another person. The devices1402, 1404, and1406can be configured to store data related to the physiological and/or subjective measurements and/or transmit the data to the communication system1410using a variety of methods, described in detail below. Although three devices1402, 1404, and1406are illustrated in the example embodiment shown, many more devices can be coupled to the patient management system. In one embodiment, each of the devices1402, 1404and1406is serving a different patient. In one embodiment, two or more devices are serving a single patient.
The devices1402, 1404, and1406can be configured to analyze the measured data and act upon the analyzed data. For example, the devices1402, 1404, and1406can be configured to modify therapy or provide an alarm based on the analysis of the data.
In one embodiment, devices1402, 1404, and1406provide therapy. Therapy can be provided automatically or in response to an external communication. Devices1402, 1404, and1406are programmable in that the characteristics of their sensing, therapy (e.g., duration and interval), or communication can be altered by communication between the devices1402, 1404, and1406and other components of the patient management system1400. Devices1402, 1404, and1406can also perform self-checks or be interrogated by the communication system1410to verify that the devices are functioning properly. Examples of different embodiments of the devices1402, 1404, and1406are provided herein.
Devices1402, 1404, and1406can 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 system1400may also include one or more remote peripheral devices1409 (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 system1410and/or the host1412.
The database module1414comprises 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 database1480. The example database module1414includes a patient database1444and an episode database1442, which are described further below. The patient database1444includes patient specific data, including data acquired by the devices1402, 1404, and1406, such as electrogram data, as well as a patient's medical records and historical information. The episode database1442has episode data regarding a plurality of different episodes generated from those of devices1402, 1404, and1406that provide episode data. The episode database1442may also store data analyzed by the analysis module1416.
Information can also be provided from an external source, such as external database1480. For example, the external database1480could 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 database1480may also store patient data that was previously acquired by an implantable or external medical device. One example of stored patient data on an external database1480is electrogram data.
The example analysis module1416includes a patient analysis module1450and a device analysis module1452. Patient analysis module1450may utilize information collected by the patient management system1400, 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 module1452analyzes data from the devices1402, 1404, and1406and external interface devices1408to predict and determine device issues or failures. For example, the device analysis module1452may analyze electrogram data to determine locations of heart beats on one or more channels using the multi-pass methods described above. The device analysis module1452can further compare device-identified beats and beat locations to beats and beat locations determined using the multi-pass method. The device analysis module1452can then perform comparisons to find the presence of noise, oversensing, and undersensing by the device, as described above.
The analysis module1416further includes an adjudication processor1458, and episode processor1460and an overwrite processor1462. In one embodiment, the adjudication processor is operatively connected to at least the episode database1442and is configured to receive as input episode data regarding one of the different episodes.
The episode processor1460performs processing of the adjudication database such as in order to provide reports, patient alerts, or programming recommendations. The overwrite processor1462can analyze data provided from the episode database1442, and other portions of the patient management system1400to determine what particular portion of episode data for one of the episodes from the episode database should be displayed to a user. Overwrite processor1462can, through the delivery module1418described 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 processor1462also 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 processor1462may also store the overwrite history for individual users.
One or more portions of the analysis module1416, such as the adjudication processor1458and episode processor1460may be located remotely from other parts of the patient management system1400. A microprocessor of a data-generating device may also serve as an adjudication processor in some embodiments.
Delivery module1418coordinates the delivery of reports, patient alerts or programming recommendations based on the analysis performed by the host1412. For example, based on the data collected from the devices and analyzed by the host1412, the delivery module1418can deliver information to the caregiver, user, or to the patient using, for example, a display provided on the external interface device1408. A user interface device1490that is independent of a data-generating device may also be used to deliver information. The external interface device1408and user interface device1490are 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 processor1460, overwrite processor1462and delivery module1418.
External interface devices1408to display information, such as programmer/recorder/monitors, can include components common to many computing devices. User interface devices1490to display and received information from users can also include components common to many computing devices. Referring now toFIG. 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 inFIG. 15.
In one embodiment, the external interface device includes a central processing unit (CPU) 1505or processor, which may include a conventional microprocessor, random access memory (RAM) 1510for temporary storage of information, and read only memory (ROM) 1515for permanent storage of information. A memory controller1520is provided for controlling system RAM1510. A bus controller1525is provided for controlling data bus1530, and an interrupt controller1535is used for receiving and processing various interrupt signals from the other system components.
Mass storage can be provided by diskette drive1541, which is connected to bus1530by controller1540, CD-ROM drive1546, which is connected to bus1530by controller1545, and hard disk drive1551, which is connected to bus1530by controller1550. User input to the programmer system may be provided by a number of devices. For example, a keyboard and mouse can connected to bus1530by keyboard and mouse controller1555. DMA controller1560is provided for performing direct memory access to system RAM1510. A visual display is generated by a video controller1565or video output, which controls video display1570. The external system can also include a telemetry interface1590or 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 inFIG. 15.
Referring now toFIG. 16, some components of an exemplary implantable medical device1600are schematically illustrated. The implantable medical device1600can include a controller made up of a microprocessor1610communicating with a memory1612, where the memory1612may 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 device1600in 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 interface1680is provided for communicating with an external programmer1675. The external programmer is a computerized device with a controller1677that can interrogate the implantable medical device1600and receive stored data as well as adjust the operating parameters of the pacemaker.
The implantable medical device1600has an atrial sensing/pacing channel comprising ring electrode1633A tip electrode1633B sense amplifier1631, pulse generator1632, and an atrial channel interface1630which communicates bi-directionally with a port of microprocessor1610. The device also has two ventricular sensing/pacing channels that similarly include ring electrodes1643A and1653A tip electrodes1643B and1653B sense amplifiers1641and1651, pulse generators1642and1652, and ventricular channel interfaces1640and1650. For each channel, the electrodes are connected to the implantable medical device1600by a lead and used for both sensing and pacing. A MOS switching network1670controlled 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 generator1690and shock electrodes1691A and1691B that enables the device to deliver a defibrillation shock to the heart when fibrillation or other tachyarrhythmia is detected. The implantable medical device1600also has an evoked response sensing channel that comprises an evoked response channel interface1620and a sense amplifier1621that has its differential inputs connected to a unipolar electrode1623and to the device housing or can1660through the switching network1670. 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 microprocessor1610controls the overall operation of the device in accordance with programmed instructions stored in memory. The sensing circuitry of the implantable medical device1600generates atrial and ventricular sense signals when voltages sensed by the electrodes exceed a specified threshold. The controller then interprets sense signals from the sensing channels and controls the delivery of paces in accordance with a programmed pacing mode. The sense signals from any of the sensing channels of the implantable medical device1600inFIG. 16can be digitized and recorded by the controller to constitute an electrogram that can either be transmitted via the telemetry link1680to the external programmer1675or stored for later transmission. The patient's cardiac activity may thus be observed in real-time or over a selected historical period.
Dong, Yanting, Zhang, Shijie, Mahajan, Deepa, Liu, Chenguang, Li, Dan, Lin, Yayun, Bohn, Derek D.
US 20130138004A1
600/509, 600515-519, 600/521
A61B 5/7217 : of noise originating from a...
A61N 1/3704 : Circuits specially adapted ...
A61N 1/3937 : Monitoring output parameters
Off Line Sensing Method And Its Applications In Detecting Undersensing, Oversensing, And Noise
System And Method For Off Line Analysis Of Cardiac Data
US 9,008,760 B2