For detected premature ventricular contractions (PVCS) trigger storage electrocardiogram

Abstract

Techniques for triggering storage or transmission of electrogram (EGM) signals associated with premature ventricular contractions (PVCs) include sensing cardiac EGM signals of a patient with a plurality of electrodes, detecting a premature ventricular contraction (PVC) within the cardiac EGM signals, determining whether PVC storage criteria are met, storing a portion of the cardiac EGM signals associated with the PVC in response to a determination that the PVC storage criteria are met, and refraining from storing the portion of the cardiac EGM signals associated with the PVC in response to a determination that the PVC storage criteria are not met.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to medical device systems, and more particularly, to medical device systems configured to detect premature ventricular contractions (PVCs). BACKGROUND

[0002] Medical devices can be used to monitor physiological signals of a patient. For example, some medical devices are configured to sense electrocardiogram (EGM) signals that are indicative of electrical activity of the heart via electrodes. Some medical devices can be configured to deliver therapy in conjunction with or independent of monitoring of physiological signals.

[0003] PVCs are premature beats. PVCs are premature because they occur before the regular beats. During a PVC event, the ventricles fire and contract prematurely before the normal discharge from the sinoatrial node arrives. PVCs can occur in healthy individuals. As an example, PVCs can be caused by caffeine, tobacco use, alcohol consumption, stress, fatigue, pharmacologic toxicity, electrolyte imbalance, hypoxia, and heart attack. Common symptoms associated with PVCs include palpitations, dizziness, fatigue, shortness of breath, chest pain, and lightheadedness. PVCs are generally considered benign, but can lead to cardiomyopathy, ventricular arrhythmias, and heart failure.

[0004] Management strategies for PVC-induced cardiomyopathy include medical therapy and catheter ablation, with the role of catheter ablation increasing in view of the potential for permanent suppression of PVCs. Ablation to suppress PVCs can lead to improvement in left ventricular systolic dysfunction (LVSD) and normalization of left ventricular ejection fraction (LVEF). PVC burden, a quantification of the amount of PVCs over a period of time, can be an independent predictor of PVC-induced cardiomyopathy. Currently, 24-hour Holter monitoring is the most commonly used method to determine PVC burden. SUMMARY

[0005] Generally, the present disclosure relates to techniques for detecting PVCs using a medical device. More specifically, the present disclosure relates to techniques for triggering storage or transmission of a cardiac EGM signal associated with a PVC in response to one or more PVC storage criteria being met. For example, processing circuitry of an implantable medical device (IMD) or another device can identify a PVC in a cardiac EGM signal, classify the PVC, and store or transmit the PVC signal to a server or remote computing device when a PVC burden is above a PVC burden threshold or a new PVC classification is detected. In this way, the processing circuitry can store or transmit only cardiac EGM signals that help a physician as needed, while conserving storage space and battery life of the device by not storing or transmitting every detected PVC. Further, storing or transmitting PVCs in response to a PVC burden of a given patient exceeding a PVC burden threshold can help determine that the patient is experiencing one or more patient conditions, such as a risk of sudden cardiac death, arrhythmia, or cardiomyopathy.

[0006] In some cases, an EGM signal can indicate one or more events of a cardiac cycle, such as ventricular depolarization and / or repolarization, atrial depolarization and / or repolarization, or any combination thereof. Such an EGM can be referred to as a cardiac EGM or a cardiac EGM signal. PVCs can be detected in a cardiac EGM signal. While PVCs are common and generally harmless, they can be dangerous for people with heart disease. Thus, this can help physicians detect and identify patterns of PVCs to better treat their patients, especially those with heart disease. For example, a physician can want to review and analyze EGM data regarding a PVC morphology detected in a particular patient. This data can include an exemplary PVC morphology, a class of PVC morphologies, or PVC morphologies occurring over a period of time (e.g., a day, a week, a month) or a particular time interval (e.g., every hour, every day, every week). By providing a physician with an exemplary morphology or a class of PVC morphologies detected in a patient, a system according to the present disclosure can help the physician locate the origin of the PVCs and / or determine whether multiple triggers are causing the PVCs. This can help more accurately determine cardiac health and a risk of sudden cardiac death and can lead to clinical interventions to suppress the PVCs, such as medication and PVC ablation of a target region of the heart.

[0007] In one example, a medical system includes a plurality of electrodes configured to sense electrocardiogram (EGM) signals of a patient; and processing circuitry configured to detect a premature ventricular contraction (PVC) within a cardiac EGM signal; determine whether a PVC storage criterion is met; in response to a determination that the PVC storage criterion is met, store a portion of the cardiac EGM signal associated with the PVC; and in response to a determination that the PVC storage criterion is not met, refrain from storing the portion of the cardiac EGM signal associated with the PVC.

[0008] In another example, a method includes sensing a patient's electrocardiogram (EGM) signal using multiple electrodes; detecting premature ventricular contractions (PVCs) within the cardiac EGM signal; determining whether PVC storage criteria are met; storing a portion of the cardiac EGM signal associated with the PVC in response to the determination that the PVC storage criteria are met; and avoiding storing a portion of the cardiac EGM signal associated with the PVC in response to the determination that the PVC storage criteria are not met.

[0009] In another example, a non-transitory computer-readable medium includes instructions for causing one or more processors to perform the following steps: sensing an electrocardiogram (EGM) signal of a patient; detecting premature ventricular contractions (PVCs) within the cardiac EGM signal; determining whether PVC storage criteria are met; storing a portion of the cardiac EGM signal associated with the PVC in response to the determination that the PVC storage criteria are met; and avoiding storing a portion of the cardiac EGM signal associated with the PVC in response to the determination that the PVC storage criteria are not met.

[0010] This summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the systems, apparatus, and methods described in detail in the following drawings and specification. Further details of one or more embodiments of this disclosure are set forth in the following drawings and specification. Other features, objectives, and advantages will become apparent from the description, drawings, and claims. Attached Figure Description

[0011] Figure 1 The patient's experience illustrates the environment of the example healthcare system.

[0012] Figure 2 This explains Figure 1 Functional block diagram of an example configuration of an implantable medical device (IMD) for a medical system.

[0013] Figure 3 This explains Figure 1 and Figure 2 A conceptual side view of an example configuration of an IMD.

[0014] Figure 4 This explains Figure 1 A functional block diagram of an example configuration of an external device.

[0015] Figure 5 This is a block diagram illustrating an example system, which includes an access point, a network, external computing devices such as a server, and one or more other computing devices that can interact with... Figures 1 to 4 The IMD is coupled to external devices.

[0016] Figure 6A FIG. 53 is a graph illustrating an example classification of a PVC cardiac EGM signal.

[0017] Figure 6B FIG. 56 is a graph illustrating an example classification of a PVC cardiac EGM signal.

[0018] Figure 7 FIG. 59 is a flow diagram illustrating example operations for triggering storage of a portion of a cardiac EGM signal associated with a PVC.

[0019] Figure 8 FIG. 59 is a flow diagram illustrating example operations for triggering storage of a portion of a cardiac EGM signal associated with a PVC.

[0020] Figure 9 FIG. 59 is a flow diagram illustrating example operations for triggering storage of a portion of a cardiac EGM signal associated with a PVC.

[0021] Figure 10 FIG. 59 is a flow diagram illustrating example operations for triggering storage of a portion of a cardiac EGM signal associated with a PVC.

[0022] Throughout the specification and drawings like reference numerals refer to like elements. DETAILED DESCRIPTION

[0023] A variety of types of medical devices sense cardiac EGMs. Some medical devices that sense cardiac EGMs are non-invasive, for example using a plurality of electrodes placed in contact with an external portion of a patient, such as at various locations on the patient’s skin. As an example, electrodes used in these non-invasive procedures for monitoring cardiac EGMs can be attached to a patient using adhesive, a belt, a waistband, or a vest, and electrically coupled to a monitoring device, such as an electrocardiograph, a Holter monitor, or other electronic device. The electrodes are configured to sense electrical signals associated with electrical activity of a patient’s heart or other cardiac tissue, and provide these sensed electrical signals to the electronic device for further processing and / or display of the electrical signals. Non-invasive devices and methods can be utilized on a temporary basis, for example to monitor a patient during a clinical visit, such as during a physician’s appointment, or for a predetermined period of time, such as a day (twenty-four hours), or a few day period.

[0024] External devices that can be used for non-invasive sensing and monitoring of cardiac EGMs include wearable devices, such as patches, watches, or necklaces, with electrodes configured to contact a patient’s skin. One example of a wearable physiological monitor configured to sense cardiac EGMs is the SEEQ® wearable cardiac monitor commercially available from Medtronic pic, Dublin, Ireland. TMMobile cardiac telemetry systems. These external devices facilitate relatively long-term monitoring of patients during normal daily activities and can periodically transmit collected data to network services, such as Medtronic's Carelink. TM network.

[0025] Some implantable medical devices (IMDs) also sense and monitor cardiac EGM. Electrodes used by the IMD to sense cardiac EGM are typically integrated into the IMD's housing and / or coupled to the IMD via one or more thin leads. Examples of IMDs that monitor cardiac EGM include pacemakers and implantable cardioverter defibrillators that can be coupled to intravascular or extravascular leads, and pacemakers with a housing configured for implantation within the heart, which may be leadless. An example of a pacemaker configured for intracardiac implantation is the Micra. TM Transcatheter pacing systems are available from Medtronic. Some IMDs (Intravascular Doppler Devices) do not provide a therapy, such as implantable patient monitors that sense cardiac EGM. An example of such an IMD is the subcutaneously insertable Reveal LINQ. TM Insertable cardiac monitors (IMDs) are available from Medtronic. These IMDs facilitate relatively long-term monitoring of patients during normal daily activities and can periodically transmit collected data to network services such as Medtronic's Carelink. TM network.

[0026] Any medical device configured to sense the cardiac EGM via implanted or external electrodes, including the instances identified herein, can implement the techniques of this disclosure for detecting PVCs in the cardiac EGM, classifying PVCs, and storing a portion of the cardiac EGM signal associated with the PVC when one or more PVC storage criteria are met. The techniques of this disclosure for triggering the storage of the cardiac EGM signal associated with the PVC can facilitate the determination of cardiac health and risk of sudden cardiac death, and may lead to clinical interventions to suppress PVCs, such as drugs and PVC ablation.

[0027] Figure 1 The example medical system 2, based on one or more technologies according to this disclosure, is illustrated with reference to patient 4. The example technologies can be used with IMD 10, which can be used with external device 12 and... Figure 1 At least one of the other devices not shown in the diagram communicates wirelessly. In some instances, the IMD 10 can be implanted outside the pleural cavity of patient 4 (e.g., subcutaneously). Figure 1 (As described in the pectoral muscle location). IMD 10 can be positioned near the sternum at or just below the patient's heart level, for example, at least partially within the heart contour. IMD 10 contains multiple electrodes ( Figure 1The IMD 10 can transmit data to the external device 12 (or any other device). The transmitted data can include values of physiological parameters measured by the IMD 10, indications of arrhythmia or other disease episodes detected by the IMD 10, and physiological signals recorded by the IMD 10. For example, the external device 12 can receive information related to a PVC detected by the IMD 10, such as at least a portion of a cardiac EGM signal, morphological information about the PVC, or a count or other quantification of PVCs (e.g., over a period of time). The external device 10 can also transmit a segment of a cardiac EGM, as the IMD 10 determines that an episode of an arrhythmia or other disease occurred during the segment, or in response to a request to record the segment from the patient 4 or another user. In some examples, the IMD 10 employs LINQ® TM ICM or a version or modification of the LINQ® TM ICM or another ICM.

[0028] The external device 12 can be a computing device having a display viewable by a user and an interface (i.e., user input mechanisms) for providing input to the external device 12. In some examples, the external device 12 can be a notebook computer, a tablet computer, a workstation, one or more servers, a cloud, a data center, a cellular phone, a personal digital assistant, or another computing device that can run an application that enables the computing device to interact with the IMD 10. The external device 12 is configured to communicate with the IMD 10 and, optionally, another computing device (not illustrated in FIG. 1) through wireless communication. For example, the external device 12 can communicate through near-field communication techniques (e.g., inductive coupling, NFC, or other communication techniques that can operate at ranges of less than 10-20 cm) and far-field communication techniques (e.g., RF telemetry according to the 802.11 or Figure 1 Bluetooth® specification sets, or other communication techniques that can operate at ranges greater than near-field communication techniques).

[0029] ​External device 12 can be used to configure operational parameters of IMD 10. External device 12 can be used to retrieve data from IMD 10. The retrieved data can include values of physiological parameters measured by IMD 10, indications of episodes of cardiac arrhythmia or other maladies detected by IMD 10, and physiological signals recorded by IMD 10. For example, external device 12 can retrieve information related to PVCs detected by IMD 10, such as at least portions of cardiac EGM signals, morphological information about PVCs, or counts or other quantifications of PVCs, such as over a period of time since external device last retrieved information. External device 12 can also retrieve cardiac EGM segments recorded by IMD 10, such as due to IMD 10 determining that an episode of cardiac arrhythmia or another malady occurred during the segment, or in response to a request to record a segment from patient 4 or another user. As described below with respect to FIG. 3, external device 12 can also retrieve information related to PVCs detected by IMD 10, such as at least portions of cardiac EGM signals, morphological information about PVCs, or counts or other quantifications of PVCs, such as over a period of time since external device last retrieved information. External device 12 can also retrieve cardiac EGM segments recorded by IMD 10, such as due to IMD 10 determining that an episode of cardiac arrhythmia or another malady occurred during the segment, or in response to a request to record a segment from patient 4 or another user. Figure 5 As discussed in more detail, one or more remote computing devices can interact with IMD 10 over a network in a manner similar to external device 12, such as to program IMD 10 and / or exchange data with IMD 10.

[0030] The processing circuitry of medical system 2, such as IMD 10, external device 12, and / or one or more other computing devices, can be configured to perform example techniques of the present disclosure: detecting PVCs, classifying PVCs, and, in response to one or more PVC storage criteria being met, triggering storage or transmission of cardiac EGM information associated with the PVCs. In some examples, the processing circuitry of medical system 2 analyzes a cardiac EGM signal sensed by IMD 10 to determine whether a PVC has occurred. The PVC storage criteria can include a PVC burden being above a PVC burden threshold or when a new PVC classification is detected, as described in more detail below. Although described in the context of examples in which IMD 10 that senses a cardiac EGM includes an insertable cardiac monitor, example systems including any type of one or more implantable or external devices configured to sense a cardiac EGM can be configured to implement techniques of the present disclosure.

[0031] Figure 2 is illustrated in accordance with one or more techniques described herein. Figure 1 is a functional block diagram of an example configuration of IMD 10 in accordance with one or more techniques described herein. In the illustrated example, IMD 10 includes electrodes 16A and 16B (collectively, “electrodes 16”), antenna 26, processing circuitry 50, sensing circuitry 52, communication circuitry 54, storage 56, switching circuitry 58, and sensor 62. Although the illustrated example includes two electrodes 16, in some examples, IMDs that include or are coupled to more than two electrodes 16 can implement techniques of the present disclosure.

[0032] Processing circuitry 50 can include fixed function circuitry and / or programmable processing circuitry. Processing circuitry 50 can include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry. In some examples, processing circuitry 50 can include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry. The functions attributed to processing circuitry 50 herein can be embodied as software, firmware, hardware or any combination thereof.

[0033] Sensing circuitry 52 can be selectively coupled to electrodes 16 through switching circuitry 58, e.g., to select electrodes 16 and polarities for sensing a cardiac EGM, referred to as a sensing vector, as controlled by processing circuitry 50. Sensing circuitry 52 can sense signals from electrodes 16, e.g., to produce a cardiac EGM, to facilitate monitoring electrical activity of the heart. As examples, sensing circuitry 52 can also monitor signals from sensors 62, which can include one or more accelerometers, pressure sensors, and / or optical sensors. In some examples, sensing circuitry 52 can include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 16 and / or sensors 62.

[0034] Sensing circuitry 52 and / or processing circuitry 50 can be configured to detect a cardiac depolarization (e.g., a P-wave of atrial depolarization or an R-wave of ventricular depolarization) when a cardiac EGM amplitude crosses a sensing threshold. In some examples, for cardiac depolarization detection, sensing circuitry 52 can include a rectifier, a filter, an amplifier, a comparator, and / or an analog-to-digital converter. In some examples, sensing circuitry 52 can output an indication to processing circuitry 50 in response to the sensing of a cardiac depolarization. In this way, processing circuitry 50 can receive indications of detected cardiac depolarizations corresponding to occurrences of detected R-waves and P-waves in respective chambers of the heart. Processing circuitry 50 can use the indications of detected R-waves and P-waves to determine intervals between depolarizations, heart rate, and detect cardiac arrhythmias, such as tachyarrhythmias and asystole.

[0035] According to the techniques of this disclosure, sensing circuitry 52 can also provide one or more digitized cardiac EGM signals to processing circuitry 50 for analysis, e.g., for detecting PVCs, and / or for analysis to determine whether one or more PVC detection criteria are met. In some examples, processing circuitry 50 can store one or more portions of the digitized cardiac EGM or a digitized cardiac EGM associated with a PVC in storage device 56. According to the techniques of this disclosure, processing circuitry 50 of IMD 10 and / or processing circuitry of another device that retrieves or receives data from IMD 10 can analyze the cardiac EGM to determine whether one or more PVC storage criteria are met.

[0036] Communication circuitry 54 can include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as external device 12, another networked computing device, or another IMD or sensor. Communication circuitry 54 can receive downlink telemetry from and send uplink telemetry to external device 12 or another device with the assistance of an internal or external antenna, such as antenna 26, under the control of processing circuitry 50. In addition, processing circuitry 50 can communicate with external device 12 and the Medtronic Carelink® network via the internet or other computer network. Antenna 26 and communication circuitry 54 can be configured to transmit and / or receive signals via inductive coupling, electromagnetic coupling, near-field communication (NFC), radio frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes. Communication circuitry 54 can include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as external device 12, another networked computing device, or another IMD or sensor. Communication circuitry 54 can receive downlink telemetry from and send uplink telemetry to external device 12 or another device with the assistance of an internal or external antenna, such as antenna 26, under the control of processing circuitry 50. In addition, processing circuitry 50 can communicate with external device 12 and the Medtronic Carelink® network via the internet or other computer network. Antenna 26 and communication circuitry 54 can be configured to transmit and / or receive signals via inductive coupling, electromagnetic coupling, near-field communication (NFC), radio frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes.

[0037] In some examples, storage device 56 includes computer readable instructions that, when executed by processing circuitry 50, cause IMD 10 and processing circuitry 50 to perform various functions attributed to IMD 10 and processing circuitry 50 herein. Storage device 56 can include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media. As examples, storage device 56 can store programmed values for one or more operational parameters of IMD 10 and / or data collected by IMD 10 for transmission to another device using communication circuitry 54. Data stored by storage device 56 and transmitted by communication circuitry 54 to one or more other devices can include, for example, at least portions of a cardiac EGM signal associated with one or more PVCs, morphology information about a PVC, a count or other quantification of PVCs, and / or other cardiac EGM information.

[0038] Figure 3 is illustrated Figure 1 andFigure 2 A conceptual side view of an example configuration of IMD 10. Figure 3 In the example shown, the IMD10 may comprise a leadless subcutaneous implantable monitoring device having a housing 15 and an insulating cover 76. Electrodes 16A and 16B may be formed or placed on the outer surface of the cover 76. (The above refers to...) Figure 2 The described circuit systems 50-62 can be formed or placed on the inner surface of the cover 76 or within the housing 15. In the illustrated example, the antenna 26 is formed or placed on the inner surface of the cover 76, but in some examples, it can be formed or placed on the outer surface. In some examples, one or more sensors 62 can be formed or placed on the outer surface of the cover 76. In some examples, the insulating cover 76 can be positioned over the open housing 15 such that the housing 15 and the cover 76 surround the antenna 26 and the circuit systems 50-62, and protect the antenna and circuit systems from fluids (such as bodily fluids).

[0039] One or more of the antenna 26 or circuit systems 50-62 can be formed on the inside of the insulating cover 76, such as by using flip-chip technology. The insulating cover 76 can be flipped onto the housing 15. When flipped and placed onto the housing 15, the components of the IMD 10 formed on the inside of the insulating cover 76 can be positioned in the gap 78 defined by the housing 15. The electrode 16 can be electrically connected to the switching circuit system 58 through one or more through-holes (not shown) formed in the insulating cover 76. The insulating cover 76 can be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. The housing 15 can be formed of titanium or any other suitable material (e.g., a biocompatible material). The electrode 16 can be formed of any of stainless steel, titanium, platinum, iridium, or alloys thereof. Furthermore, the electrode 16 can be coated with a material such as titanium nitride or fractal titanium nitride, but other suitable materials and coatings for such electrodes can be used.

[0040] Figure 4 This is a block diagram illustrating an example configuration of the components of external device 12. Figure 4 In one example, the external device 12 includes a processing circuit system 80, a communication circuit system 82, a storage device 84, and a user interface 86.

[0041] The processing circuitry 80 can include one or more processors configured to execute the functions and / or process instructions for execution within the external device 12. For example, the processing circuitry 80 can be capable of processing instructions stored in the storage device 84. The processing circuitry 80 can include, among other things, a microprocessor, a DSP, an ASIC, a FPGA, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, the processing circuitry 80 can include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions of the processing circuitry 80 described herein.

[0042] The communication circuitry 82 can include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as the IMD 10. Under the control of the processing circuitry 80, the communication circuitry 82 can receive downlink telemetry from, as well as send uplink telemetry to, the IMD 10 or another device. The communication circuitry 82 can be configured to transmit or receive signals through inductive coupling, electromagnetic coupling, near-field communication (NFC), radio frequency (RF) communication, Bluetooth, Wi-Fi, or other proprietary or non-proprietary wireless communication schemes. The communication circuitry 82 can also be configured to communicate with devices other than the IMD 10 through any of a variety of forms of wired and / or wireless communication and / or network protocols.

[0043] The storage device 84 can be configured to store information within the external device 12 during operation. The storage device 84 can include a computer-readable storage medium or computer-readable storage device. In some examples, the storage device 84 includes one or more of a short-term memory or a long-term memory. The storage device 84 can include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memory, or various forms of EPROM or EEPROM. The storage device can be used to store at least portions of cardiac EGM signals associated with one or more PVCs, morphology information about the PVCs, counts or other quantifications of PVCs, and / or other cardiac EGM information received from the IMD 10. In some examples, the storage device 84 is used to store data indicative of instructions executed by the processing circuitry 80. The storage device 84 can be used by software or applications running on the external device 12 to temporarily store information during program execution.

[0044] Data exchanged between external device 12 and IMD 10 can include operational parameters. External device 12 can transmit data including computer-readable instructions that, when implemented by IMD 10, can control IMD 10 to change one or more operational parameters and / or to derive collected data. For example, external device can receive data from IMD 10, such as at least portions of cardiac EGM signals associated with one or more PVCs, morphology information about PVCs, a count or other quantification of PVCs (e.g., total number and / or by classification), and / or other cardiac EGM information. In some examples, processing circuitry 80 can transmit instructions to IMD 10 requesting that IMD 10 derive collected data (e.g., at least portions of cardiac EGM signals associated with one or more PVCs, morphology information about PVCs, a count or other quantification of PVCs, and / or other cardiac EGM information) to external device 12. In turn, external device 12 can receive the collected data from IMD 10 and store the collected data in storage 84. Processing circuitry 80 can implement any of the techniques described herein to analyze cardiac EGMs received from IMD 10, such as determining whether PVC storage criteria are met to store data collected from IMD 10 or to transmit data collected from IMD 10 and / or other PVC data (e.g., at least portions of cardiac EGM signals associated with one or more PVCs, morphology information about PVCs, a count or other quantification of PVCs, and / or other cardiac EGM information) over a network to another device (e.g., a server, a cloud, a data center).

[0045] A user, such as a clinician or patient 4, can interact with external device 12 through user interface 86. User interface 86 includes a display (not shown), such as a liquid crystal display (LCD) or light emitting diode (LED) display or other type of screen, by which the processing circuitry 80 can present information related to IMD 10, such as cardiac EGM signals, indications of PVC detections, PVC morphology information, and quantification of detected PVCs, such as quantification of PVC burden. As described in further detail below, Figure 10 Exemplary PVC information that can be presented to a user is illustrated. In addition, user interface 86 can include input mechanisms for receiving input from a user. Input mechanisms can include, for example, buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, or a touch screen, or another input mechanism that permits a user to navigate a user interface presented by processing circuitry 80 of external device 12 and provide input. In other examples, user interface 86 also includes audio circuitry for providing audible notifications, instructions, or other sounds to a user, receiving voice commands from a user, or both.

[0046] Figure 5 is a block diagram illustrating an example system including an access point 90, a network 92, an external computing device such as a server 94, and one or more other computing devices 100A-100N (collectively, "computing devices 100") that can be coupled with IMD 10 and external device 12 through network 92 in accordance with one or more techniques described herein. In this example, IMD 10 can communicate with external device 12 through a first wireless connection using communication circuitry 54 and with access point 90 through a second wireless connection. In Figure 5 In examples, access point 90, external device 12, server 94, and computing devices 100 are connected to one another and can communicate with one another through network 92.

[0047] Access point 90 can include a device connected to network 92 through any of a variety of connections including, for example, a telephone dial-up, digital subscriber line (DSL), or cable modem connection. In other examples, access point 90 can be coupled to network 92 through different forms of connections including wired or wireless connections. In some examples, access point 90 can be a user device such as a tablet computer or smartphone that can be co-located with the patient. IMD 10 or external device 12 can be configured to transmit data, such as PVC detection information, PVC morphology information, PVC quantification (e.g., PVC burden), and / or cardiac EGM signals, to access point 90 in response to satisfying PVC storage criteria. Access point 90 can then communicate the received data to server 94 through network 92.

[0048] In some cases, server 94 can be configured to provide a secure storage site for data that has been collected from IMD 10 and / or external device 12. In some cases, server 94 can assemble data in web pages or other documents for viewing by trained professionals, such as clinicians, through computing devices 100. Figure 5 One or more aspects of the illustrated system can be implemented with general network technology and functionality that can be similar to that provided by Medtronic CareLink® Network Hub. In some examples, server 94 can include one or more servers, clouds, one or more databases, and / or data centers.

[0049] In some examples, one or more of the computing devices 100 can be a tablet computer or other smart device positioned with a clinician through which the clinician can program, receive alerts from, and / or interrogate the IMD 10. For example, the clinician can access data collected by the IMD 10 through the computing devices 100, such as when the patient 4 is between clinician visits, to check the status of the medical condition. In some examples, the clinician can enter instructions for a medical intervention for the patient 4 into an application executed by the computing devices 100, such as based on the status of the patient condition determined by the IMD 10, the external device 12, the server 94, or any combination thereof, or based on other patient data known to the clinician. The devices 100 can then transmit the instructions for the medical intervention to another one of the computing devices 100 positioned with the patient 4 or a caregiver of the patient 4. For example, such instructions for the medical intervention can include instructions to change a medication dosage, timing, or selection, to schedule a visit with a clinician, or to seek medical attention. In further examples, the computing devices 100 can generate an alert to the patient 4 based on the status of the medical condition of the patient 4, which can enable the patient 4 to proactively seek medical attention prior to receiving instructions for a medical intervention. In this way, the patient 4 can be empowered to take action as needed to address his or her medical condition, which can help improve clinical outcomes for the patient 4.

[0050] In the examples described by Figure 5 The server 94 includes, for example, a storage device 96 for storing data retrieved from the IMD 10 and processing circuitry 98, although Figure 5 The computing devices 100 can similarly include storage devices and processing circuitry, although not illustrated. The processing circuitry 98 can include one or more processors configured to implement functionality and / or process instructions for execution within the server 94. For example, the processing circuitry 98 can be capable of processing instructions stored in the memory 96. The processing circuitry 98 can include, among other things, a microprocessor, a DSP, an ASIC, an FPGA, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, the processing circuitry 98 can include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to

[0051] Storage 96 can include computer-readable storage media or computer-readable storage devices. In some examples, memory 96 includes one or more of short-term memory or long-term memory. Storage 96 can include, for example, RAM, DRAM, SRAM, magnetic disks, optical disks, flash memory, or various forms of EPROM or EEPROM. In some examples, storage 96 is used to store data indicative of instructions executed by processing circuitry 98.

[0052] Figure 6A FIG. 16 is a graph illustrating a cardiac EGM signal 120 and example techniques for detecting and classifying PVCs based on the cardiac EGM signal. For example, the techniques of the present disclosure can use different features, such as the inter-depolarization (e.g., R-R) intervals and morphology features, to distinguish PVC depolarizations from normal ventricular depolarizations. IMD 10 senses cardiac EGM signal 120 and detects the timing of ventricular depolarizations 122A, 122B, 122C, and 122D (collectively, “ventricular depolarizations 122”) using ventricular depolarization (e.g., R-wave) detection techniques as described with respect to FIGS. 1-15. Figure 2

[0053] In some examples, IMD 10 uses two or more, e.g., a primary and a secondary sensing channel, to sense ventricular depolarizations 122. The different sensing channels can have different hardware, different firmware settings, and / or different software settings for processing cardiac EGM 120 signal to detect ventricular depolarizations 122. For example, the primary sensing channel can implement a relatively short blanking, e.g., 150 milliseconds (ms), automatic adjustment threshold, with a relatively high depolarization detection amplitude. For the primary sensing channel, some examples can implement the techniques described in U.S. Patent No. 7,027,858 to Cao et al.

[0054] However, because the ventricular depolarization wave of a PVC depolarization, e.g., a QRS complex, is generally wider and has a relatively lower frequency content than a normal depolarization, the primary sensing channel can be able to sense PVC depolarizations. The secondary sensing channel can include a relatively long blanking, e.g., a 520 ms fixed threshold, which can help to detect PVC depolarizations that can not be detected by the primary sensing channel. Processing circuitry 50 and / or sensing circuitry 52 can determine the fixed threshold that the secondary sensing channel uses to detect depolarizations in a given cardiac cycle based on the amplitude of one or more prior ventricular depolarizations.

[0055] ​Characteristics that distinguish PVC depolarizations from normal ventricular depolarizations include: a shorter interval between a PVC depolarization and a preceding (temporally) adjacent depolarization; a longer interval between a PVC depolarization and a subsequent adjacent depolarization; and different depolarization and repolarization wave morphologies between a PVC depolarization and a normal ventricular depolarization. To determine whether a current ventricular depolarization 122C is a PVC depolarization, processing circuitry 50 of IMD 10 or other processing circuitry of system 2 can consider interval and morphology information for current ventricular depolarization 122C, a preceding (temporally) adjacent depolarization 122B, and a subsequent (temporally) adjacent depolarization 122D. Processing circuitry can determine whether each ventricular depolarization 122 is a PVC depolarization in this manner by performing a next depolarization iteration, e.g., depolarization 122C becomes the preceding adjacent depolarization, depolarization 122D becomes the current depolarization, and the next (temporally) depolarization after depolarization 122D becomes the subsequent adjacent depolarization. Although techniques for determining whether a ventricular depolarization is a PVC depolarization are primarily described herein (e.g., with respect to FIGS. 6-8) as being performed by processing circuitry 50 of IMD 10, such techniques can be performed, in whole or in part, by processing circuitry of any one or more devices of system 2, such as processing circuitry 80 of external device 12, processing circuitry 98 of server 94, or processing circuitry of one or more computing devices 100. Figure 10 ) as being performed by processing circuitry 50 of IMD 10, such techniques can be performed, in whole or in part, by processing circuitry of any one or more devices of system 2, such as processing circuitry 80 of external device 12, processing circuitry 98 of server 94, or processing circuitry of one or more computing devices 100.

[0056] In some examples, processing circuitry 50 determines, for each depolarization 122, a respective inter-depolarization interval 124A-124C (collectively, "inter-depolarization intervals 124"), such as an R-R interval. For example, processing circuitry 50 can determine an inter-depolarization interval 124A for preceding adjacent depolarization 122B as the interval between the detection time of ventricular depolarization 122A and the detection time of ventricular depolarization 122B. Similarly, processing circuitry 50 can determine an inter-depolarization interval 124B for current depolarization 122C as the interval between the detection time of ventricular depolarization 122B and the detection time of ventricular depolarization 122C, and determine an inter-depolarization interval 124C for subsequent adjacent depolarization 122D as the interval between the detection time of ventricular depolarization 122C and the detection time of ventricular depolarization 122D.

[0057] Processing circuitry 50 can also identify, for each ventricular depolarization 122B-122D within each window 126A-126C (collectively, "windows 126"), a respective segment of the digitized version of cardiac EGM signal 120. Each of the windows 126 can contain a predetermined number of samples of cardiac EGM signal 120, such as sixteen samples sampled at 64 Hz. The location of the windows 126, and thus which samples of cardiac EGM signal 120 are within a given window 126, can be set relative to the point in time at which processing circuitry 50 detects the corresponding ventricular depolarization 122 or another fiducial marker of cardiac EGM 120. In some examples, each of the windows 126 contains sixteen samples of cardiac EGM signal 120 that begin four samples before the detected point of the corresponding depolarization 122.

[0058] To determine whether a current ventricular depolarization 122C is a PVC depolarization, processing circuitry 50 can determine, based on the segments within the respective windows 126, whether the ventricular depolarizations 122B-122D satisfy one or more morphological criteria. For each of the depolarizations 122B-122D, processing circuitry 50 can determine, as an example, one or more of a maximum amplitude, a minimum amplitude, a maximum slope, and a minimum slope within the respective windows 126A-126C. Processing circuitry 50 can determine a time interval, e.g., a number of samples, between the maximum slope point and the minimum slope point of each depolarization 122B-122D, also referred to herein as a slope interval. Processing circuitry 50 can determine the slope of cardiac EGM signal 120 using any known technique, such as by determining a derivative or differential signal of cardiac EGM signal 120.

[0059] The morphological criteria can include criteria related to the degree of correlation between various possible pairings of depolarizations 122B-122D. Processing circuitry 50 can determine a correlation value for a pair of depolarizations by performing a correlation operation on the segments of cardiac EGM signal 120 within the respective windows 126 of the depolarizations. Example correlation operations include any known cross-correlation, wavelet-based comparison, feature set comparison, or sum of differences technique.

[0060] An example formula for computing a cross-correlation is:

[0061]

[0062] where x and y are two segments of the cardiac EGM signal 120 to be compared, and different values of L are different lags at which the cross-correlation is computed. This equation represents shifting one of the segments by a lag (L), multiplying it point-by-point with the other segment, and adding the multiplication results point-by-point. The same procedure is followed for different lags. In some examples, the lags are + / - four samples. The maximum value of C(L) will occur at the lag at which the two segments x and y best match each other. In such examples, the processing circuitry 50 can determine the maximum value of C(L) as the correlation value for a given comparison between two ventricular depolarizations 122.

[0063] To conserve processing and power resources of the IMD 10, the processing circuitry 50 can implement a sum-of-differences technique for determining correlation values that represent the degree of correlation between various pairings of depolarizations 122B-D. The processing circuitry 50 can determine the point-by-point difference between segments of the cardiac EGM signal 120 for two depolarizations 122 at various lags (e.g., + / - 4 samples), and the lag with the lowest sum-of-differences will have the highest correlation between the depolarizations 122. An example formula for computing the sum-of-differences is:

[0064]

[0065] where x and y are two segments of the cardiac EGM signal 120 to be compared, and different values of L are different lags at which the cross-correlation is computed. This equation represents shifting one of the segments by a lag (L), multiplying it point-by-point with the other segment, and adding the multiplication results point-by-point. The same procedure is followed for different lags. In some examples, the lags are + / - four samples. The maximum value of C(L) will occur at the lag at which the two segments x and y best match each other. In such examples, the processing circuitry 50 can determine the maximum value of C(L) as the correlation value for a given comparison between two ventricular depolarizations 122.

[0066] In some examples, to determine whether the current ventricular depolarization 122C is a PVC depolarization (e.g., for detecting a PVC), the processing circuitry 50 determines a correlation value between the current ventricular depolarization 122C and each of the preceding adjacent ventricular depolarization 122B and the subsequent adjacent depolarization 122D. In the example illustrated in FIG. 6, the current ventricular depolarization 122C is a PVC depolarization, and both of the adjacent ventricular depolarizations 122B and 122D are normal ventricular depolarizations. Because the ventricular depolarization 122C has a different morphology than both of the adjacent ventricular depolarizations 122B and 122D, the processing circuitry 50 expects the correlation values determined for both comparisons to indicate a relatively low degree of correlation, e.g., a relatively high difference sum. The processing circuitry 50 can also determine a correlation value between the adjacent ventricular depolarizations 122B and 122D. Because both of the ventricular depolarizations 122B and 122D are normal ventricular depolarizations expected to have similar morphologies, the correlation value between them is expected to indicate a relatively high degree of correlation, e.g., a relatively low difference sum. The processing circuitry 50 can apply any combination of one or more of the morphology criteria described herein.

[0067] To determine whether the current ventricular depolarization 122C is a PVC depolarization, the processing circuitry 50 can also evaluate the respective inter-depolarization intervals 124A-124C of the ventricular depolarizations 122B-122D. Because the current ventricular depolarization 122C is a PVC depolarization, the inter-depolarization interval 124B is expected to be shorter than the inter-depolarization interval 124A, and the inter-depolarization interval 124C is expected to be longer than the inter-depolarization interval 124A due to the compensatory pause following the PVC depolarization. The processing circuitry 50 can also evaluate the maximum and minimum amplitudes and the slope intervals of the ventricular depolarizations 122B-122D to determine whether the depolarization 122C is a PVC depolarization. Because the depolarization 122C is a PVC depolarization and is expected to have a wide QRS complex, the interval (e.g., number of samples) between the maximum slope and the minimum slope of the depolarization 122C is expected to be greater than that of a normal depolarization (such as the adjacent depolarizations 122B and 122D). For PVC detection, some examples can implement the techniques described in Sarkar, U.S. Patent No. 9,675,270; Sarkar, U.S. Patent Publication Nos. 2016 / 0310029 and 2016 / 0310031; and Rajagopal et al., U.S. Patent Application No. 16 / 436,012.

[0068] After determining that the current ventricular depolarization 122C is a PVC depolarization, the processing circuitry 50 can classify the current ventricular depolarization 122C. For example, the processing circuitry 50 can store a plurality of classifications in the storage device 56. In some examples, the processing circuitry 50 can classify the current ventricular depolarization 122C as a PVC based on the correlation values determined for the current ventricular depolarization 122C and the adjacent ventricular depolarizations 122B and 122D. In some examples, the processing circuitry 50 can classify the current ventricular depolarization 122C as a PVC based on the correlation value determined for the current ventricular depolarization 122C and the adjacent ventricular depolarization 122B. In some examples, the processing circuitry 50 can classify the current ventricular depolarization 122C as a PVC based on the correlation value determined for the current ventricular depolarization 122C and the adjacent ventricular depolarization 122D. Figure 6AIn the illustrated example, the storage 56 can contain a classification 1, a classification 2, and a classification 3 represented by PVC morphologies 127A, 128A, and 129A, respectively. In some examples, each of the PVC morphologies 127A, 128A, and 129A can include an average or mean morphology of the PVC signals detected under the respective classification. In other examples, each of the PVC morphologies 127A, 128A, and 129A can include the last PVC signal classified under the respective classification.

[0069] To classify the ventricular depolarization 122C or a portion of the EGM signal 120 associated with the ventricular depolarization 122C (e.g., the portion of the EGM signal 120 contained within the window 126B) (referred to herein as “PVC 122C”), the processing circuitry 50 can determine a difference between the PVC 122C and each of the PVC morphologies 127A, 128A, and 129A to identify the classification closest to the PVC 122C. For example, the processing circuitry 50 can determine a correlation value between the PVC 122C and each of the PVC morphologies 127A, 128A, and 129A. To determine these correlation values, the processing circuitry 50 can perform any of the correlation operations described above (e.g., cross-correlation, wavelet-based comparison, feature set comparison, or sum of differences technique). For example, the processing circuitry 50 can use the sum of differences technique to determine a correlation value between the PVC 122C and each of the PVC morphologies 127A, 128A, and 129A to identify the closest classification (e.g., the classification with the lowest sum of differences). In some examples, the processing circuitry 50 can determine a Euclidean distance between the PVC 122C and each of the PVC morphologies to identify the closest classification (e.g., the PVC morphology with the lowest Euclidean distance to the PVC 122C). An example formula to calculate the Euclidean distance is:

[0070]

[0071] where x and y are two points of the PVC 122C and a stored PVC morphology. In some examples, the processing circuitry 50 can determine N Euclidean distances between N different points on the PVC 122C and each stored PVC morphology and either add the N Euclidean distances or calculate an average Euclidean distance to determine a correlation value between the PVC 122C and each stored PVC morphology. Either way, the PVC morphology with the lowest Euclidean distance will represent the stored classification closest to the PVC 122C. In some examples, the processing circuitry 50 can classify the PVC 122C using a clustering algorithm such as K-Means clustering.

[0072] In Figure 6AIn the illustrated example, processing circuitry 50 can determine PVC morphology 128A as the classification closest to PVC 122C after determining the correlation values (e.g., difference sums) between PVC 122C and each of PVC morphologies 127A, 128A, and 129A, and finding that the correlation value for PVC morphology 128A is the lowest of the three correlation values. Although only three classifications are shown in Figure 6A FIG. 6, according to the present disclosure, processing circuitry 50 can store fewer or more classifications. For example, processing circuitry 50 can store any number (N) of classifications, and processing circuitry 50 will determine N correlation values between PVC 122C and each of the N classifications to identify the closest classification (e.g., the classification with the lowest difference sum). Processing circuitry 50 can then determine whether the correlation value between PVC 122C and PVC morphology 128A is below a threshold value. If the correlation value is equal to or above the threshold value, then processing circuitry 50 classifies PVC 122C as a new classification (e.g., “Classification 4”) and optionally stores PVC 122C as a PVC morphology for the new classification. If the correlation value is below the threshold value, then processing circuitry 50 classifies PVC 122C as “Classification 2”. In some examples, if PVC 122C is classified as “Classification 2”, when PVC morphology 128A represents an average or mean morphology of PVC signals detected under “Classification 2”, processing circuitry 50 will update PVC morphology 128A (e.g., recalculate the average or mean PVC morphology) to include PVC 122C (e.g., as shown by PVC morphology 128AA of Figure 6B FIG. 7).

[0073] Figure 6B Exemplary classifications of PVC cardiac EGM signals are illustrated. In particular, Figure 6B Classifications 1, 2, and 3 represented by PVC morphologies 127A, 128AA, and 129A, respectively, are shown. Although only three classifications are shown in Figure 6B FIG. 6, according to the present disclosure, processing circuitry 50 can store fewer or more classifications. In this example, each of PVC morphologies 127A, 128AA, and 129A includes an average or mean morphology of PVC signals detected under the respective classification. For example, PVC morphology 127A represents an average or mean of PVC signals 127B, 127C, and 127D; PVC morphology 128AA represents an average or mean of PVC signals 128B, 128C, 128D, and PVC 122C; and PVC morphology 129A represents an average or mean of PVC signals 129B and 129C. In some examples, PVC morphology 128AA can include Figure 6A PVC 122C, as shown by the dashed line in FIG. 6. Figure 6APVC 122C. In some examples, each of the PVC signals 127B, 127C, 127D, 128B, 128C, 128D, 129B, and 129C includes a portion of the cardiac EGM signal corresponding to a PVC (e.g., the P, QRS, and T waves of the PVC).

[0074] In some examples, the processing circuitry 50 stores each of the PVC signals 127A, 127B, 127C, 127D, 128AA, 128B, 128C, 128D, 129A, 129B, and 129C in the storage device 56. For example, the processing circuitry 50 can store each of the PVC signals 127A, 128AA, and 129A in a buffer in the storage device 56. In some examples, each buffer contains a reference to a data structure (e.g., a stack, queue, array, linked list, tree, or table) containing the PVC signals detected under the corresponding classification. For example, a buffer entry containing the PVC morphology 127A can contain a reference or link to a data structure containing the PVC signals 127B, 127C, and 127D; a buffer entry containing the PVC morphology 128AA can include a reference or link to a data structure containing the PVC signals 128B, 128C, and 128D; and a buffer entry containing the PVC morphology 129A can include a reference or link to a data structure containing the PVC signals 129B and 129C. In some examples, the processing circuitry 50 can store all or the last N (e.g., 10, 20) PVC signals detected under the corresponding classification. In examples where the processing circuitry 50 stores only the last N (e.g., 10, 20) PVC signals and stores N PVC signals for a particular classification, the processing circuitry removes the oldest stored PVC signal before storing another PVC signal. In some examples, the stored PVC signals are automatically purged after a certain period of time (e.g., after a week, a month, a year, or any other period of time) or manually purged by a user, physician, or administrator.

[0075] In some examples, the processing circuitry 50 stores each of the PVC signals 127A, 127B, 127C, 127D, 128AA, 128B, 128C, 128D, 129A, 129B, and 129C in the cloud (e.g., the external device 12 and / or 94). In some examples, the cloud can periodically (e.g., every week, every month, every year, or any other period of time) update the average or mean morphology of the last N (e.g., 10, 20) PVC signals stored. In some examples, the cloud can not purge any stored PVC signals.

[0076] In some examples, if a particular classification can not match any detected PVCs for a sufficiently long period of time (e.g., 3 months, 6 months, a year, or any other period of time), processing circuitry 50 can“retire” or“archive” the classification (e.g., processing circuitry 50 can no longer compare future detected PVCs to the classification) as it can no longer be applicable to the current clinical situation. For example, processing circuitry 50 can track how many matches are being generated for a given classification and when PVCs are detected.

[0077] Figure 7 is a flowchart illustrating example operations for triggering storage of a cardiac EGM signal associated with a PVC. While Figure 7 the example operations of are described as being performed by processing circuitry 50 of IMD 10 and with respect to cardiac EGM signal 120 of FIG. 6, in other examples, some or all of the example operations can be performed by processing circuitry of another device and with respect to any cardiac EGM.

[0078] According to Figure 7 an example, processing circuitry 50 senses a cardiac EGM signal of a patient (e.g., by electrodes 16) (702). Next, processing circuitry 50 detects a PVC within the cardiac EGM signal (e.g., as described above with reference to Figure 6A or any other known method of detecting a PVC within a cardiac EGM signal) (704). In some examples, processing circuitry 50 maintains a count of detected PVCs for a period of time (e.g., 6 hours, 24 hours, a week, a month).

[0079] Processing circuitry 50 then classifies the detected PVC (706). As described above with reference to Figure 6A to classify PVC 122C, processing circuitry 50 can compare the morphology of PVC 122 to each of PVC morphologies 127A, 128A, and 129A corresponding to the classifications stored in storage 56 to identify the closest classification to PVC 122C. If the difference between PVC 122C and the closest classification in storage 56 is equal to or above a threshold difference, processing circuitry 50 will create a new classification and store PVC 122C as the PVC morphology of the new classification. If the difference between PVC 122C and the closest classification in storage 56 is less than the threshold difference, processing circuitry 50 will classify PVC 122C as the closest classification. In some examples, processing circuitry 50 can determine the Euclidean distance between PVC 122C and each PVC morphology to identify the closest classification (e.g., the PVC morphology with the lowest Euclidean distance to PVC 122C). InFigure 6A In the illustrated example, PVC morphology 128A is the closest classification to PVC 112C and the difference (e.g., correlation value) between PVC morphology 128A and PVC 112C is below a threshold and processing circuitry 50 classifies PVC 122C as “Classification 2.” In some examples, when PVC morphology 128A represents an average or mean morphology of PVC signals detected under “Classification 2,” processing circuitry 50 will update PVC morphology 128A (e.g., recalculate the average or mean PVC morphology) to include PVC 122C (e.g., as shown by PVC morphology 128AA of FIG. 1C). In some examples, processing circuitry 50 can classify PVC 122C using a clustering algorithm such as K-Means clustering. In some examples, processing circuitry 50 maintains a detection count or other quantification of PVCs detected for a given classification (e.g., determines a PVC burden by classification). Figure 6B

[0080] Processing circuitry 50 further determines whether a PVC storage criterion is met (708). In some examples, the PVC storage criterion is met if the PVC burden exceeds a PVC burden threshold, if a new PVC classification is detected, if a pair or triplet event is detected, or if a R-on-T phenomenon is detected (e.g., as described in further detail below with reference to FIG. 1D). Figure 8 Processing circuitry 50 further determines whether a PVC storage criterion is met (708). In some examples, the PVC storage criterion is met if the PVC burden exceeds a PVC burden threshold, if a new PVC classification is detected, if a pair or triplet event is detected, or if a R-on-T phenomenon is detected (e.g., as described in further detail below with reference to FIG. 1D).

[0081] ​Based on a determination that the PVC storage criteria is not met (the "No" branch of 708), processing circuitry 50 can refrain from storing the portion of cardiac EGM signal 120 associated with PVC 122C (710). Based on a determination that the PVC storage criteria is met (the "Yes" branch of 708), processing circuitry 50 can store the portion of cardiac EGM signal 120 associated with PVC 122C (712). For example, processing circuitry 50 can store the portion of cardiac EGM signal 120 associated with PVC 122C in storage 56. In some examples, processing circuitry 50 stores PVC 122C with other PVCs that occurred within a first time period (e.g., 24 hours, a week, a month). In some examples, in response to the determination that the PVC storage criteria is met (the "Yes" branch of 708), processing circuitry 50 can transmit the portion of cardiac EGM signal 120 associated with PVC 122C over a network to another device. For example, processing circuitry 50 can transmit the portion of cardiac EGM signal 120 associated with PVC 122C to external device 12, server 94, any computing device 100, or any other device. In other examples, the portion of cardiac EGM signal 120 associated with PVC 122C can include one or more other beats surrounding PVC 122C. For example, processing circuitry 50 can store or transmit the portion of cardiac EGM signal including PVC 122C for a duration of between 2 and 14 minutes. In some examples, processing circuitry 50 can store or transmit other PVC information including PVC burden information (e.g., total and / or by classification) for a given duration (e.g., 24 hours, a week, a month) or timing information (e.g., start and end times).

[0082] Figure 8 is a flowchart illustrating example operations for triggering storage of a portion of a cardiac EGM signal associated with a PVC. Figure 8 Example operations of FIG. 7 can be performed as part of another method for triggering storage of a portion of a cardiac EGM signal associated with a PVC. In some examples, elements 702-708 can be performed in any order. In some examples, one or more of elements 702-708 need not be performed. Figure 7 Example operations of element 708 of FIG. 7 can be performed as part of another method for triggering storage of a portion of a cardiac EGM signal associated with a PVC. In some examples, elements 802-808 can be performed in any order. In some examples, one or more of elements 802-808 need not be performed. Figure 8

[0083] According to some examples, the method of FIG. 7 can be performed by a medical device, such as external device 12, server 94, any computing device 100, or any other device. Figure 8 ​In instances, processing circuitry 50 determines whether the PVC burden threshold is exceeded (802). In some instances, processing circuitry 50 can determine that the PVC burden threshold is exceeded if 10% is exceeded over a period of time (e.g., 6 hours, 12 hours, 24 hours, one week, one month). In some instances, processing circuitry 50 will determine a PVC burden for all detected PVCs. In some instances, processing circuitry 50 will determine a PVC burden for each PVC classification stored in storage 56. For example, processing circuitry 50 will determine a total PVC burden, a first PVC burden for classification 1 (PVC morphology 127A), a second PVC burden for classification 2 (PVC morphology 128A or 128AA), and a third PVC burden for classification 3 (PVC morphology 129A). As such, processing circuitry 50 will determine whether any of the total, first, second, or third PVC burdens exceed the PVC burden threshold (802). If the PVC burden threshold is exceeded (YES branch of 802), processing circuitry 50 will store the portion of cardiac EGM signal 120 associated with the detected PVC (712), regardless.

[0084] If processing circuitry 50 determines that the PVC burden threshold is not exceeded (NO branch of 802), processing circuitry 50 determines whether a new PVC classification is detected (804). As described above with reference to FIG. 7, processing circuitry 50 will detect a new classification if the existing classification that is closest to the detected PVC is substantially different. For example, if the correlation value (e.g., difference and) between PVC 122C and PVC morphology 128A is equal to or above the threshold in instances of FIG. 7, processing circuitry 50 will classify PVC 122C as a new classification (e.g., “classification 4”). If a new PVC classification is detected (YES branch of 804, e.g., because the correlation value between the detected PVC and the closest classification stored in storage 56 is too large), processing circuitry 50 will store the portion of cardiac EGM signal 120 associated with the detected PVC (712). Figure 6A Figure 6A

[0085] ​​If the processing circuitry 50 determines that no new PVC classification has been detected (the "No" branch of 804), then the processing circuitry 50 determines whether a binary or triadic event has been detected in the cardiac EGM signal (806). If each detected normal beat is followed by a PVC or any other abnormal beat, then the processing circuitry 50 will detect a binary event in the cardiac EGM signal. For example, a heartbeat pattern in the cardiac EGM signal that includes normal heartbeat, PVC, normal heartbeat, PVC, etc., will constitute a binary event (regardless of whether the detected PVCs belong to the same or different classifications). If the processing circuitry 50 detects two normal heartbeats followed by a PVC, or if the processing circuitry 50 detects two normal heartbeats followed by two PVCs (the "Yes" branch of 806), then the processing circuitry 50 will detect a triadic event in the cardiac EGM signal. For example, to detect a binary event in the cardiac EGM signal 120, the processing circuitry system 50 can determine that the current depolarization 122C is a PVC and determine whether the previous two depolarizations (e.g., depolarizations 122A and 122B) were a PVC and a normal beat, respectively (regardless of whether the detected PVCs belong to the same or different categories). Figure 6A In the example shown, the processing circuitry 50 can detect binary events in the cardiac EGM signal 120 because ventricular depolarization 122A is PVC depolarization, ventricular depolarization 122B is normal depolarization, and ventricular depolarization 122C is PVC. In some instances, the processing circuitry 50 can determine whether a quadruple event has occurred at element 806. The processing circuitry 50 will detect a quadruple event in the cardiac EGM signal if each detected normal beat is followed by three consecutive PVCs, or if every four beats are PVCs (regardless of whether the detected PVCs belong to the same or different categories). In some instances, the processing circuitry 50 can determine whether a double event has occurred at element 806 if it detects two consecutive PVCs in the cardiac EGM signal (regardless of whether the detected PVCs belong to the same or different categories). In some instances, the processing circuitry 50 can determine whether a triple event has occurred at element 806 if it detects three consecutive PVCs in the cardiac EGM signal (regardless of whether the detected PVCs belong to the same or different categories). If a binomial, triad, quadruple, dual, or triple event is detected in the cardiac EGM signal (the "yes" branch of 806), then the processing circuit system 50 will store a portion (712) of the cardiac EGM signal 120 associated with the detected PVC.

[0086] If processing circuitry 50 determines that a pair or a triplet is not detected in the cardiac EGM signal ("NO" branch of 806), processing circuitry 50 determines whether an R-on-T phenomenon is detected (808). Processing circuitry 50 will detect an R-on-T phenomenon when processing circuitry 50 detects a PVC (e.g., PVC depolarization) on a T-wave of a previous beat in the cardiac EGM signal. R-on-T phenomenon is a particularly dangerous event because ventricular fibrillation and death can occur. During the T-wave (repolarization), the myocardium is very sensitive to external stimuli, and a strong PVC can fibrillate the myocardium. If an R-on-T phenomenon is detected in the cardiac EGM signal ("YES" branch of 808), processing circuitry 50 will store the portion of the cardiac EGM signal 120 associated with the detected PVC (712). In some examples, processing circuitry 50 will generate an automatic clinician alert in response to detecting an R-on-T phenomenon. If processing circuitry 50 determines that an R-on-T phenomenon is not detected in the cardiac EGM signal ("NO" branch of 808), processing circuitry 50 can refrain from storing the portion of the cardiac EGM signal 120 associated with the detected PVC (710).

[0087] In some examples, processing circuitry 50 can determine whether one or more of criteria 802-808 are met. For example, processing circuitry 50 can perform elements 802-808 for each detected PVC. In such examples, in addition to storing the portion of the cardiac EGM signal 120 associated with the detected PVC, processing circuitry 50 will also store which of criteria 802-808 are met. For example, if the PVC burden threshold is exceeded ("YES" branch of 802), processing circuitry 50 can store an indication that the PVC burden is exceeded and determine whether a new PVC classification is also detected (804). If processing circuitry 50 determines that a new PVC classification is detected ("YES" branch of 804), processing circuitry 50 can store an indication that the new PVC classification is detected and also determine whether a pair or triplet event is detected in the cardiac EGM signal (806). If processing circuitry 50 determines that a pair, triplet, quartet, double, or triple event is detected in the cardiac EGM signal ("YES" branch of 806), processing circuitry 50 can store an indication that the detected PVC and pair, triplet, quartet, double, or triple event was detected and also determine whether an R-on-T phenomenon is detected (808). In this example, elements 802-808 can be performed in series, in parallel, or in any order. Figure 9 is a flowchart illustrating example operations for classifying PVCs. Figure 9 Example operations of Figure 7of element 706. In other instances, Figure 9 Example operations of element 706 can be performed as part of another method for triggering storage of a portion of a PVC-associated cardiac EGM signal.

[0088] According to Figure 9 In instances of element 702, processing circuitry 50 determines a difference between the detected PVC and each stored PVC classification in storage 56 (902). As shown in the example of FIG. 7B, processing circuitry 50 can determine a difference between PVC 122C and each of PVC morphologies 127A, 128A, and 129A by determining a correlation value between PVC 122C and each of PVC morphologies 127A, 128A, and 129A. To determine these correlation values, processing circuitry 50 can perform any of the correlation operations described above with respect to element 706 (e.g., cross-correlation, wavelet-based comparison, feature set comparison, sum of differences, or Euclidean distance techniques). For example, processing circuitry 50 can use the sum of differences technique to determine a correlation value between PVC 122C and each of the PVC morphologies. Based on the correlation values, processing circuitry 50 then identifies the stored classification that is closest to PVC 122C (904). In the example shown in FIG. 7B, processing circuitry 50 can identify PVC morphology 128A as the classification that is closest to PVC 122C (e.g., the classification with the lowest sum of differences). Figure 6A Figure 6A Figure 6A

[0089] Processing circuitry 50 can then determine whether the difference between the detected PVC and the closest classification is below a threshold (906). For example, processing circuitry 50 can determine whether the correlation value (e.g., sum of differences) between PVC 122C and PVC morphology 128A is below a threshold. Based on a determination that the correlation value is equal to or above the threshold (NO branch of 906), processing circuitry 50 classifies PVC 122C as a new classification (e.g., “Classification 4”) and optionally stores PVC 122C as a PVC morphology for the new classification (910). Based on a determination that the correlation value is below the threshold (YES branch of 906), processing circuitry 50 classifies PVC 122C as “Classification 2”. In some instances, when PVC morphology 128A represents an average or mean morphology of PVC signals detected under “Classification 2”, processing circuitry 50 will update PVC morphology 128A (e.g., recalculate the average or mean PVC morphology) to include PVC 122C (e.g., as shown by PVC morphology 128AA of FIG. 7C). Figure 6B ​​​​

[0090] Figure 10 This is a diagram illustrating example PVC information that can be presented to a user. For example, diagram 1000 may include a user interface for physicians, clinical technicians, or any other user to view PVC information categorized and stored according to the techniques disclosed herein.

[0091] exist Figure 10 The example shown presents the daily detected amount of PVC (e.g., PVC load). For example, Figure 10 The total daily PVC load is illustrated in line graph 1008. Figure 10 It also illustrates a bar chart visually breaking down the daily PVC load by category. For example, the daily PVC load for category 1 is shown by bars 1002A, 1002B, and 1002C (collectively referred to as "Bar 1002"); the daily PVC load for category 2 is shown by bars 1004A, 1004B, and 1004C (collectively referred to as "Bar 1004"); and the daily PVC load for category 3 is shown by bars 1006A, 1006B, and 1006C (collectively referred to as "Bar 1006"). Although Figure 10 The columns 1002, 1004, and 1006 are stacked on top of each other in the order of the highest daily PVC load at the bottom and the lowest daily PVC load at the top. It is understood that each of columns 1002, 1004, and 1006 can be displayed adjacent to each other and / or in any order. In some instances, a physician, clinical technician, or any other user can select any of columns 1002, 1004, and 1006, and the system will display additional PVC information (either on the same screen or in a pop-up screen). For example, the system can display a stored portion of the cardiac EGM signal corresponding to a PVC detected on a specific date. For example, a user can select column 1002A, and the system can display a stored portion of the cardiac EGM signal corresponding to a Class 1 PVC detected on October 1st. In this way, the system can help physicians detect and identify patterns of PVCs to better treat their patients, especially those with heart disease. For example, a physician might want to view and analyze EGM data related to the PVC morphology detected in a particular patient. This can help doctors pinpoint the origin of PVC and / or determine if multiple triggers contribute to PVC, which may help to more accurately determine the risk of heart health and sudden cardiac death, and may lead to clinical interventions to suppress PVC, such as medications.

[0092] As described above, processing circuitry, such as processing circuitry 50 of IMD 10, can include any combination of one or more of hardware, firmware, and software configured to implement the techniques described herein. In some examples, implementation of certain aspects of the techniques in hardware can improve computing and power performance of an implementation device, such as IMD 10. As an example, processing circuitry can include hardware configured to compute differences and or other correlation values, and firmware for other functionality described herein.

[0093] The techniques described in this disclosure can be implemented, at least in part, in hardware, software, firmware or any combination thereof. For example, various aspects of the techniques can be implemented within one or more processors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic QRS circuitry as well as any combinations of such components, embodied in external devices, such as physician or patient programmers, simulators, or other devices. The terms "processor" and "processing circuitry" can generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent electrical circuitry, alone or in combination with other digital or analog circuitry.

[0094] For aspects implemented in software, at least some of the functionality attributed to the systems and devices described in this disclosure can be embodied as instructions on a computer-readable storage medium, such as RAM, DRAM, SRAM, magnetic disks, optical disks, flash memory, or various forms of EPROM or EEPROM. The instructions can be executed to support one or more aspects of the functionality described in this disclosure.

[0095] Further, in some aspects, the functionality described herein can be provided within dedicated hardware and / or software modules. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units can be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Also, the techniques could be fully implemented in one or more circuits or logic elements. The techniques of the present disclosure can be implemented in a wide variety of devices or apparatuses, including an IMD, an external programmer, a combination of an IMD and external programmer, an integrated circuit (IC) or a set of ICs and / or discrete circuitry in residence within an IMD and / or external programmer.

Claims

1. A medical system comprising: a plurality of electrodes configured to sense a cardiac EGM signal of a patient; and processing circuitry configured to: detect a premature ventricular contraction (PVC) within the cardiac EGM signal; determine whether a PVC storage criterion is met based on PVC information stored in a memory and in response to detecting the PVC within the cardiac EGM signal; in response to a determination that the PVC storage criterion is met, store a portion of the cardiac EGM signal associated with the PVC; and in response to a determination that the PVC storage criterion is not met, refrain from storing the portion of the cardiac EGM signal associated with the PVC.

2. The medical system of claim 1, wherein the processing circuitry is configured to determine that the PVC storage criterion is met when a PVC burden associated with the cardiac EGM signal is above a PVC burden threshold; and wherein the processing circuitry is configured to determine that the PVC storage criterion is not met when the PVC burden associated with the cardiac EGM signal is at or below the PVC burden threshold.

3. The medical system of claim 1, wherein the processing circuitry is configured to classify the PVC; wherein the processing circuitry is configured to determine that the PVC storage criterion is met when the PVC is classified as a new classification that was not previously stored by the medical system; and wherein the processing circuitry is configured to determine that the PVC storage criterion is not met when the PVC is classified as an existing classification stored by the medical system.

4. The medical system of claim 3, wherein the processing circuitry is configured to: analyze a morphology of the PVC; and classify the PVC according to the analysis of the morphology of the PVC.

5. The medical system of claim 1, wherein the processing circuitry is configured to classify the PVC as one of a plurality of classifications stored by the medical system; and wherein the processing circuitry is further configured to determine a PVC burden for each of the plurality of classifications stored by the medical system.

6. The medical system of claim 1, wherein the processing circuitry is configured to determine that the PVC storage criterion is met when a PVC burden associated with the cardiac EGM signal is above a PVC burden threshold; and wherein the processing circuitry is further configured to set a PVC burden threshold to the PVC burden.

7. The medical system of claim 1, wherein the portion of the cardiac EGM signal associated with the PVC includes two or more beats surrounding the PVC.

8. The medical system of claim 1, wherein the portion of the cardiac EGM signal associated with the PVC comprises a portion of the cardiac EGM signal of the PVC having a duration between two and fourteen minutes.

9. The medical system of claim 1, wherein the processing circuitry is configured to: ​ compare the morphology of the PVC to each of a plurality of classifications stored by the medical system, wherein each of the plurality of classifications comprises a mean morphology of PVCs detected from the classification; determine a first classification of which a first mean morphology is closest to the morphology of the PVC; in response to determining that a difference between the first mean morphology and the morphology of the PVC is below a threshold, classify the PVC as the first classification and update the first mean morphology to include the morphology of the PVC; and in response to determining that the difference between the first mean morphology and the morphology of the PVC is at or above the threshold, classify the PVC as a new classification and set a new mean morphology to the morphology of the PVC.

10. The medical system of claim 9, further comprising a display system to display PVC information to a physician, wherein the detected up to N PVCs for each of the plurality of classifications are displayed to the physician.

11. The medical system of claim 1, wherein storing the portion of the cardiac EGM signal associated with the PVC comprises storing the portion of the cardiac EGM signal associated with other PVCs occurring within a first time period.

12. The medical system of claim 1, wherein the processing circuitry is configured to: compare the morphology of the PVC to each of a plurality of classifications stored in a plurality of buffers by the medical system, wherein each of the plurality of classifications comprises a representative morphology of PVCs detected from the classification; determine a first classification of which a first morphology is closest to the morphology of the PVC; in response to determining that a difference between the first morphology and the morphology of the PVC is below a threshold, set the first morphology as the morphology of the PVC; and in response to determining that the difference between the first morphology and the morphology of the PVC is at or above the threshold, classify the PVC as a new classification and store the morphology of the PVC in another buffer as a new representative morphology of the new classification.

13. The medical system of claim 1, wherein the processing circuitry morphology is configured to determine that the PVC storage criteria are satisfied when the PVC is part of a bi-, tri-, quad-, double, or triple event.

14. The medical system of claim 1, wherein the processing circuitry morphology is configured to determine that the PVC storage criteria are satisfied when a R-on-T phenomenon is detected.

15. The medical system of claim 1, wherein storing the portion of the cardiac EGM signal associated with the PVC comprises transmitting the portion of the cardiac EGM signal associated with the PVC to a server.

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