Patent Description:
Medical devices may be used to monitor physiological signals of a patient. For example, some medical devices are configured to sense cardiac electrogram (EGM) signals indicative of the electrical activity of the heart via electrodes. Some medical devices may be configured to deliver a therapy in conjunction with or separate from the monitoring of physiological signals.

PVCs are premature heartbeats. PVCs are premature because they occur before the regular heartbeat. During a PVC event, the ventricles electrically discharge and contract prematurely before the normal electrical discharge arrives from the sinoatrial node. PVCs may occur in healthy individuals. PVCs may be caused by caffeine, smoking, alcohol consumption, stress, exhaustion, pharmacological toxicity, electrolyte imbalance, lack of oxygen, and heart attack as examples. Common symptoms associated with PVCs include palpitations, dizziness, fatigue, dyspnea, chest pain, and lightheadedness. PVCs are normally considered benign, but may potentially cause cardiomyopathy, ventricular arrythmias, and heart failure. <CIT> discloses detection and storage of PVCs.

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

In general, this disclosure is directed to techniques for detecting PVCs using a medical device. More particularly, the disclosure is directed to techniques for triggering the storage or transmission of cardiac EGM signals 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 may 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 when a new PVC classification is detected. In this way, the processing circuitry may only store or transmit the cardiac EGM signals necessary to aid a physician while conserving storage space and battery life of the device by not storing or transmitting every single detected PVC. Moreover, storing or transmitting PVCs in response to the PVC burden for a given patient exceeding the PVC burden threshold may help determine that the patient is experiencing one or more patient conditions such as such as risk of sudden cardiac death, arrhythmias, or cardiomyopathy.

EGM signals, in some cases, may indicate one or more events of a heart cycle such as ventricular depolarizations and/or repolarizations, atrial depolarizations and/or repolarizations, or any combination thereof. Such EGMs may be referred to as cardiac EGMs or cardiac EGM signals. PVCs can be detected in cardiac EGM signals. While PVCs are common and usually harmless, they can be dangerous for persons with existing heart problems. Therefore, it may be helpful for physicians to detect and identify patterns of PVCs to better treat their patients, particularly patients with existing heart problems. For example, physicians may want to view and analyze EGM data about the morphologies of the PVCs detected in a particular patient. This data can include exemplary PVC morphologies, classes of PVC morphologies, or morphologies of PVCs occurring within a period of time (e.g., a day, a week, a month) or in particular intervals of time (e.g., hourly, daily, weekly). By providing exemplary morphologies or classes of PVC morphologies detected in a patient to a physician, a system in accordance with this disclosure may assist a physician to localize the origin of the PVCs and/or determine whether there are multiple triggers causing the PVCs. This may facilitate more accurate determinations of cardiac wellness and risk of sudden cardiac death, and may lead to clinical interventions to suppress PVCs such as medications and PVC ablations of targeted areas of the heart.

In one example, a medical system comprises a plurality of electrodes configured to sense a cardiac electrogram (EGM) signal of a patient; and processing circuitry configured to detect a premature ventricular contraction (PVC) within the cardiac EGM signal; determine whether PVC storage criteria is met; in response to a determination that the PVC storage criteria is met, store a portion of the cardiac EGM signal associated with the PVC; and in response to a determination that the PVC storage criteria is not met, eschew storing the portion of the cardiac EGM signal associated with the PVC.

In another example, a method comprises sensing a cardiac electrogram (EGM) signal of a patient via a plurality of electrodes; detecting a premature ventricular contraction (PVC) within the cardiac EGM signal; determining whether PVC storage criteria is met; in response to a determination that the PVC storage criteria is met, storing a portion of the cardiac EGM signal associated with the PVC; and in response to a determination that the PVC storage criteria is not met, eschewing storing the portion of the cardiac EGM signal associated with the PVC.

In another example, a non-transitory computer-readable medium comprising instructions for causing one or more processors to sense a cardiac electrogram (EGM) signal of a patient; detect a premature ventricular contraction (PVC) within the cardiac EGM signal; determine whether PVC storage criteria is met; in response to a determination that the PVC storage criteria is met, store a portion of the cardiac EGM signal associated with the PVC; and in response to a determination that the PVC storage criteria is not met, eschew storing the portion of the cardiac EGM signal associated with the PVC.

Like reference characters denote like elements throughout the description and figures.

A variety of types of medical devices sense cardiac EGMs. Some medical devices that sense cardiac EGMs are non-invasive, e.g., using a plurality of electrodes placed in contact with external portions of the patient, such as at various locations on the skin of the patient. The electrodes used to monitor the cardiac EGM in these non-invasive processes may be attached to the patient using an adhesive, strap, belt, or vest, as examples, and electrically coupled to a monitoring device, such as an electrocardiograph, Holter monitor, or other electronic device. The electrodes are configured to sense electrical signals associated with the electrical activity of the heart or other cardiac tissue of the patient, and to provide these sensed electrical signals to the electronic device for further processing and/or display of the electrical signals. The non-invasive devices and methods may be utilized on a temporary basis, for example to monitor a patient during a clinical visit, such as during a doctor's appointment, or for example for a predetermined period of time, for example for one day (twenty-four hours), or for a period of several days.

External devices that may be used to non-invasively sense and monitor cardiac EGMs include wearable devices with electrodes configured to contact the skin of the patient, such as patches, watches, or necklaces. One example of a wearable physiological monitor configured to sense a cardiac EGM is the SEEQ™ Mobile Cardiac Telemetry System, available from Medtronic plc, of Dublin, Ireland. Such external devices may facilitate relatively longer-term monitoring of patients during normal daily activities, and may periodically transmit collected data to a network service, such as the Medtronic Carelink™ Network.

Some implantable medical devices (IMDs) also sense and monitor cardiac EGMs. The electrodes used by IMDs to sense cardiac EGMs are typically integrated with a housing of the IMD and/or coupled to the IMD via one or more elongated leads. Example IMDs that monitor cardiac EGMs include pacemakers and implantable cardioverter-defibrillators, which may be coupled to intravascular or extravascular leads, as well as pacemakers with housings configured for implantation within the heart, which may be leadless. An example of pacemaker configured for intracardiac implantation is the Micra™ Transcatheter Pacing System, available from Medtronic plc. Some IMDs that do not provide therapy, e.g., implantable patient monitors, sense cardiac EGMs. One example of such an IMD is the Reveal LINQ™ Insertable Cardiac Monitor, available from Medtronic plc, which may be inserted subcutaneously. Such IMDs may facilitate relatively longer-term monitoring of patients during normal daily activities, and may periodically transmit collected data to a network service, such as the Medtronic Carelink™ Network.

Any medical device configured to sense a cardiac EGM via implanted or external electrodes, including the examples identified herein, may implement the techniques of this disclosure for detecting a PVC in a cardiac EGM, classifying the PVC, and storing a portion of the cardiac EGM signal associated with PVC when one or more PVC storage criteria is met. The techniques of this disclosure for triggering storage of cardiac EGM signals associated with a PVC may facilitate determinations of cardiac wellness and risk of sudden cardiac death, and may lead to clinical interventions to suppress PVCs such as medications and PVC ablations.

<FIG> illustrates the environment of an example medical system <NUM> in conjunction with a patient <NUM>, in accordance with one or more techniques of this disclosure. The example techniques may be used with an IMD <NUM>, which may be in wireless communication with at least one of external device <NUM> and other devices not pictured in <FIG>. In some examples, IMD <NUM> is implanted outside of a thoracic cavity of patient <NUM> (e.g., subcutaneously in the pectoral location illustrated in <FIG>). IMD <NUM> may be positioned near the sternum near or just below the level of the heart of patient <NUM>, e.g., at least partially within the cardiac silhouette. IMD <NUM> includes a plurality of electrodes (not shown in <FIG>), and is configured to sense a cardiac EGM via the plurality of electrodes. IMD <NUM> may transmit data to external device <NUM> (or any other device). The transmitted data may include values of physiological parameters measured by IMD <NUM>, indications of episodes of arrhythmia or other maladies detected by IMD <NUM>, and physiological signals recorded by IMD <NUM>. For example, external device <NUM> may receive information related to detection of PVCs by IMD <NUM>, such as at least a portion of a cardiac EGM signal, morphological information about PVCs or a count or other quantification of PVCs, e.g., over a time period. In some examples, IMD <NUM> may transmit cardiac EGM segments due to IMD <NUM> determining that an episode of arrhythmia or another malady occurred during the segment, or in response to a request to record the segment from patient <NUM> or another user. In some examples, IMD <NUM> takes the form of the LINQ™ ICM, or another ICM similar to, e.g., a version or modification of, the LINQ™ ICM.

External device <NUM> may be a computing device with a display viewable by the user and an interface for providing input to external device <NUM> (i.e., a user input mechanism). In some examples, external device <NUM> may be a notebook computer, tablet computer, workstation, one or more servers, cloud, data center, cellular phone, personal digital assistant, or another computing device that may run an application that enables the computing device to interact with IMD <NUM>. External device <NUM> is configured to communicate with IMD <NUM> and, optionally, another computing device (not illustrated in <FIG>), via wireless communication. External device <NUM>, for example, may communicate via near-field communication technologies (e.g., inductive coupling, NFC or other communication technologies operable at ranges less than <NUM>-<NUM>) and far-field communication technologies (e.g., RF telemetry according to the <NUM> or Bluetooth® specification sets, or other communication technologies operable at ranges greater than near-field communication technologies).

External device <NUM> may be used to configure operational parameters for IMD <NUM>. External device <NUM> may be used to retrieve data from IMD <NUM>. The retrieved data may include values of physiological parameters measured by IMD <NUM>, indications of episodes of arrhythmia or other maladies detected by IMD <NUM>, and physiological signals recorded by IMD <NUM>. For example, external device <NUM> may retrieve information related to detection of PVCs by IMD <NUM>, such as at least a portion of a cardiac EGM signal, morphological information about PVCs or a count or other quantification of PVCs, e.g., over a time period since the last retrieval of information by external device. External device <NUM> may also retrieve cardiac EGM segments recorded by IMD <NUM>, e.g., due to IMD <NUM> determining that an episode of arrhythmia or another malady occurred during the segment, or in response to a request to record the segment from patient <NUM> or another user. As discussed in greater detail below with respect to <FIG>, one or more remote computing devices may interact with IMD <NUM> in a manner similar to external device <NUM>, e.g., to program IMD <NUM> and/or exchange data with IMD <NUM>, via a network.

Processing circuitry of medical system <NUM>, e.g., of IMD <NUM>, external device <NUM>, and/or of one or more other computing devices, may be configured to perform the example techniques of this disclosure for detecting a PVC, classifying the PVC, and, in response to one or more PVC storage criteria being met, triggering the storage or transmission of cardiac EGM information associated with the PVC. In some examples, the processing circuitry of medical system <NUM> analyzes a cardiac EGM signals sensed by IMD <NUM> to determine whether a PVC has occurred. The PVC storage criteria may include a PVC burden being above a PVC burden threshold or when a new PVC classification is detected, as described in greater detail below. Although described in the context of examples in which IMD <NUM> that senses the cardiac EGM comprises an insertable cardiac monitor, example systems including one or more implantable or external devices of any type configured to sense a cardiac EGM may be configured to implement the techniques of this disclosure.

<FIG> is a functional block diagram illustrating an example configuration of IMD <NUM> of <FIG> in accordance with one or more techniques described herein. In the illustrated example, IMD <NUM> includes electrodes 16A and 16B (collectively "electrodes <NUM>"), antenna <NUM>, processing circuitry <NUM>, sensing circuitry <NUM>, communication circuitry <NUM>, storage device <NUM>, switching circuitry <NUM>, and sensors <NUM>. Although the illustrated example includes two electrodes <NUM>, IMDs including or coupled to more than two electrodes <NUM> may implement the techniques of this disclosure in some examples.

Processing circuitry <NUM> may include fixed function circuitry and/or programmable processing circuitry. Processing circuitry <NUM> may 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 <NUM> may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry. The functions attributed to processing circuitry <NUM> herein may be embodied as software, firmware, hardware or any combination thereof.

Sensing circuitry <NUM> may be selectively coupled to electrodes <NUM> via switching circuitry <NUM>, e.g., to select the electrodes <NUM> and polarity, referred to as the sensing vector, used to sense a cardiac EGM, as controlled by processing circuitry <NUM>. Sensing circuitry <NUM> may sense signals from electrodes <NUM>, e.g., to produce a cardiac EGM, in order to facilitate monitoring the electrical activity of the heart. Sensing circuitry <NUM> also may monitor signals from sensors <NUM>, which may include one or more accelerometers, pressure sensors, and/or optical sensors, as examples. In some examples, sensing circuitry <NUM> may include one or more filters and amplifiers for filtering and amplifying signals received from electrodes <NUM> and/or sensors <NUM>.

Sensing circuitry <NUM> and/or processing circuitry <NUM> may be configured to detect cardiac depolarizations (e.g., P-waves of atrial depolarizations or R-waves of ventricular depolarizations) when the cardiac EGM amplitude crosses a sensing threshold. For cardiac depolarization detection, sensing circuitry <NUM> may include a rectifier, filter, amplifier, comparator, and/or analog-to-digital converter, in some examples. In some examples, sensing circuitry <NUM> may output an indication to processing circuitry <NUM> in response to sensing of a cardiac depolarization. In this manner, processing circuitry <NUM> may receive detected cardiac depolarization indicators corresponding to the occurrence of detected R-waves and P-waves in the respective chambers of heart. Processing circuitry <NUM> may use the indications of detected R-waves and P-waves for determining inter-depolarization intervals, heart rate, and detecting arrhythmias, such as tachyarrhythmias and asystole.

Sensing circuitry <NUM> may also provide one or more digitized cardiac EGM signals to processing circuitry <NUM> for analysis, e.g., for use in detecting a PVC, and/or for analysis to determine whether one or more PVC storage criteria are satisfied according to the techniques of this disclosure. In some examples, processing circuitry <NUM> may store the digitized cardiac EGM or one or more portions of the digitized cardiac EGM associated with a PVC in storage device <NUM>. Processing circuitry <NUM> of IMD <NUM>, and/or processing circuitry of another device that retrieves or receives data from IMD <NUM>, may analyze the cardiac EGM to determine whether one or more PVC storage criteria are satisfied according to the techniques of this disclosure.

Communication circuitry <NUM> may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as external device <NUM>, another networked computing device or server, or another IMD or sensor. Under the control of processing circuitry <NUM>, communication circuitry <NUM> may receive downlink telemetry from, as well as send uplink telemetry to external device <NUM> or another device with the aid of an internal or external antenna, e.g., antenna <NUM>. In addition, processing circuitry <NUM> may communicate with a networked computing device via an external device (e.g., external device <NUM>) and a computer network, such as the Medtronic CareLink® Network. Antenna <NUM> and communication circuitry <NUM> may be configured to transmit and/or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, Wi-Fi, or other proprietary or non-proprietary wireless communication schemes.

In some examples, storage device <NUM> includes computer-readable instructions that, when executed by processing circuitry <NUM>, cause IMD <NUM> and processing circuitry <NUM> to perform various functions attributed to IMD <NUM> and processing circuitry <NUM> herein. Storage device <NUM> may 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. Storage device <NUM> may store, as examples, programmed values for one or more operational parameters of IMD <NUM> and/or data collected by IMD <NUM> for transmission to another device using communication circuitry <NUM>. Data stored by storage device <NUM> and transmitted by communication circuitry <NUM> to one or more other devices may include at least a portion of a cardiac EGM signal associated with one or more PVCs, morphological information about PVCs, a count or other quantification of PVCs, and/or other cardiac EGM information, as examples.

<FIG> is a conceptual side-view diagram illustrating an example configuration of IMD <NUM> of <FIG> and <FIG>. In the example shown in <FIG>, IMD <NUM> may include a leadless, subcutaneously-implantable monitoring device having a housing <NUM> and an insulative cover <NUM>. Electrode 16A and electrode 16B may be formed or placed on an outer surface of cover <NUM>. Circuitries <NUM>-<NUM>, described above with respect to <FIG>, may be formed or placed on an inner surface of cover <NUM>, or within housing <NUM>. In the illustrated example, antenna <NUM> is formed or placed on the inner surface of cover <NUM>, but may be formed or placed on the outer surface in some examples. In some examples, one or more of sensors <NUM> may be formed or placed on the outer surface of cover <NUM>. In some examples, insulative cover <NUM> may be positioned over an open housing <NUM> such that housing <NUM> and cover <NUM> enclose antenna <NUM> and circuitries <NUM>-<NUM>, and protect the antenna and circuitries from fluids such as body fluids.

One or more of antenna <NUM> or circuitries <NUM>-<NUM> may be formed on the inner side of insulative cover <NUM>, such as by using flip-chip technology. Insulative cover <NUM> may be flipped onto a housing <NUM>. When flipped and placed onto housing <NUM>, the components of IMD <NUM> formed on the inner side of insulative cover <NUM> may be positioned in a gap <NUM> defined by housing <NUM>. Electrodes <NUM> may be electrically connected to switching circuitry <NUM> through one or more vias (not shown) formed through insulative cover <NUM>. Insulative cover <NUM> may be formed of sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material. Housing <NUM> may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes <NUM> may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes <NUM> may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.

<FIG> is a block diagram illustrating an example configuration of components of external device <NUM>. In the example of <FIG>, external device <NUM> includes processing circuitry <NUM>, communication circuitry <NUM>, storage device <NUM>, and user interface <NUM>.

Processing circuitry <NUM> may include one or more processors that are configured to implement functionality and/or process instructions for execution within external device <NUM>. For example, processing circuitry <NUM> may be capable of processing instructions stored in storage device <NUM>. Processing circuitry <NUM> may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry <NUM> may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry <NUM>.

Communication circuitry <NUM> may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as IMD <NUM>. Under the control of processing circuitry <NUM>, communication circuitry <NUM> may receive downlink telemetry from, as well as send uplink telemetry to, IMD <NUM>, or another device. Communication circuitry <NUM> may be configured to transmit or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, Wi-Fi, or other proprietary or non-proprietary wireless communication schemes. Communication circuitry <NUM> may also be configured to communicate with devices other than IMD <NUM> via any of a variety of forms of wired and/or wireless communication and/or network protocols.

Storage device <NUM> may be configured to store information within external device <NUM> during operation. Storage device <NUM> may include a computer-readable storage medium or computer-readable storage device. In some examples, storage device <NUM> includes one or more of a short-term memory or a long-term memory. Storage device <NUM> may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. Storage device may be used to store at least a portion of a cardiac EGM signal associated with one or more PVCs, morphological information about PVCs, a count or other quantification of PVCs, and/or other cardiac EGM information received from IMD <NUM>. In some examples, storage device <NUM> is used to store data indicative of instructions for execution by processing circuitry <NUM>. Storage device <NUM> may be used by software or applications running on external device <NUM> to temporarily store information during program execution.

Data exchanged between external device <NUM> and IMD <NUM> may include operational parameters. External device <NUM> may transmit data including computer readable instructions which, when implemented by IMD <NUM>, may control IMD <NUM> to change one or more operational parameters and/or export collected data. For example, external device may receive data from IMD <NUM>, including at least a portion of a cardiac EGM signal associated with one or more PVCs, morphological information about PVCs, a count or other quantification of PVCs (e.g., totals and/or by classification), and/or other cardiac EGM information, for example. In some examples, processing circuitry <NUM> may transmit an instruction to IMD <NUM> which requests IMD <NUM> to export collected data (e.g., at least a portion of a cardiac EGM signal associated with one or more PVCs, morphological information about PVCs, a count or other quantification of PVCs, and/or other cardiac EGM information) to external device <NUM>. Either way, external device <NUM> may receive the collected data from IMD <NUM> and store the collected data in storage device <NUM>. Processing circuitry <NUM> may implement any of the techniques described herein to analyze cardiac EGMs received from IMD <NUM>, e.g., to determine whether PVC storage criteria is met to store the collected data from IMD <NUM> or to transmit the collected data from IMD <NUM> and/or other PVC data (e.g., at least a portion of a cardiac EGM signal associated with one or more PVCs, morphological information about PVCs, a count or other quantification of PVCs, and/or other cardiac EGM information) to transmit to another device (e.g., a server, cloud, data center) over a network.

A user, such as a clinician or patient <NUM>, may interact with external device <NUM> through user interface <NUM>. User interface <NUM> includes a display system (not shown), such as a liquid crystal display (LCD) or a light emitting diode (LED) display or other type of screen, with which processing circuitry <NUM> may present information related to IMD <NUM>, e.g., cardiac EGM signals, indications of detections of PVCs, PVC morphology information, and quantifications of detected PVCs, such as a quantification of PVC burden. As described in further detail below, <FIG> illustrates exemplary PVC information that may be presented to a user. In addition, user interface <NUM> may include an input mechanism to receive input from the user. The input mechanisms may include, for example, any one or more of buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, a touch screen, or another input mechanism that allows the user to navigate through user interfaces presented by processing circuitry <NUM> of external device <NUM> and provide input. In other examples, user interface <NUM> also includes audio circuitry for providing audible notifications, instructions or other sounds to the user, receiving voice commands from the user, or both.

<FIG> is a block diagram illustrating an example system that includes an access point <NUM>, a network <NUM>, external computing devices, such as a server <NUM>, and one or more other computing devices 100A-100N (collectively, "computing devices <NUM>"), which may be coupled to IMD <NUM> and external device <NUM> via network <NUM>, in accordance with one or more techniques described herein. In this example, IMD <NUM> may use communication circuitry <NUM> to communicate with external device <NUM> via a first wireless connection, and to communicate with an access point <NUM> via a second wireless connection. In the example of <FIG>, access point <NUM>, external device <NUM>, server <NUM>, and computing devices <NUM> are interconnected and may communicate with each other through network <NUM>.

Access point <NUM> may include a device that connects to network <NUM> via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point <NUM> may be coupled to network <NUM> through different forms of connections, including wired or wireless connections. In some examples, access point <NUM> may be a user device, such as a tablet or smartphone, that may be co-located with the patient. IMD <NUM> or external device <NUM> may be configured to transmit data, such as PVC detection information, PVC morphology information, PVC quantifications (e.g., PVC burden), and/or cardiac EGM signals, to access point <NUM> in response to PVC storage criteria being met. Access point <NUM> may then communicate the received data to server <NUM> via network <NUM>.

In some cases, server <NUM> may be configured to provide a secure storage site for data that has been collected from IMD <NUM> and/or external device <NUM>. In some cases, server <NUM> may assemble data in web pages or other documents for viewing by trained professionals, such as clinicians, via computing devices <NUM>. One or more aspects of the illustrated system of <FIG> may be implemented with general network technology and functionality, which may be similar to that provided by the Medtronic CareLink® Network. In some examples, server <NUM> may comprise one or more servers, a cloud, one or more databases, and/or a data center.

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

In the example illustrated by <FIG>, server <NUM> includes a storage device <NUM>, e.g., to store data retrieved from IMD <NUM>, and processing circuitry <NUM>. Although not illustrated in <FIG> computing devices <NUM> may similarly include a storage device and processing circuitry. Processing circuitry <NUM> may include one or more processors that are configured to implement functionality and/or process instructions for execution within server <NUM>. For example, processing circuitry <NUM> may be capable of processing instructions stored in memory <NUM>. Processing circuitry <NUM> may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry <NUM> may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry <NUM>. Processing circuitry <NUM> of server <NUM> and/or the processing circuity of computing devices <NUM> may implement any of the techniques described herein to analyze cardiac EGMs received from IMD <NUM>, e.g., to determine whether to store or transmit cardiac EGM information associated with a PVC in response to one or more PVC storage criteria being met.

Storage device <NUM> may include a computer-readable storage medium or computer-readable storage device. In some examples, memory <NUM> includes one or more of a short-term memory or a long-term memory. Storage device <NUM> may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device <NUM> is used to store data indicative of instructions for execution by processing circuitry <NUM>.

<FIG> is a graph illustrating a cardiac EGM signal <NUM> and an example technique for detecting and classifying PVCs based on the cardiac EGM signal. For example, the techniques of this disclosure may use different features such as inter-depolarization (e.g., R-R) interval and morphology characteristics to distinguish a PVC depolarization from a normal ventricular depolarization. IMD <NUM> senses cardiac EGM signal <NUM> and detects the timing of ventricular depolarizations 122A, 122B, 122C, and 122D (collectively, "ventricular depolarizations <NUM>") using ventricular depolarization, e.g., R-wave, detection techniques such as those described with respect to <FIG>.

In some examples, IMD <NUM> senses ventricular depolarizations <NUM> using two or more, e.g., primary and secondary, sensing channels. The different sensing channels may have different hardware, different firmware settings, and/or different software settings for processing cardiac EGM signal <NUM> to detect ventricular depolarizations <NUM>. For example, a primary sensing channel may implement a relatively shorter blanking, e.g., <NUM> milliseconds (ms), auto-adjusting threshold having relatively higher amplitudes for depolarization detection. For the primary sensing channel, some examples may implement the techniques described in <CIT>.

However, because the ventricular depolarization wave, e.g., QRS complexes, of PVC depolarizations (i.e., PVCs) are typically wider and have relatively lower frequency content than normal depolarizations, the primary sensing channel may under sense PVC depolarizations. A secondary sensing channel may include a relatively longer blanking, e.g., <NUM>, fixed threshold, which may facilitate detection of PVC depolarizations that may have not been detected by the primary sensing channel. Processing circuitry <NUM> and/or sensing circuitry <NUM> may determine the fixed threshold used by the secondary sensing channel to detect a depolarization in a given cardiac cycle based on amplitudes of one or more prior ventricular depolarizations.

Characteristics that distinguish PVC depolarizations from normal ventricular depolarizations include: shorter intervals between a PVC depolarization and the preceding adjacent (in time) depolarization; longer intervals between a PVC depolarization and a subsequent adjacent depolarization; and differing depolarization and repolarization wave morphologies as between PVC depolarizations and normal ventricular depolarizations. In order to determine whether a current ventricular depolarization 122C is a PVC depolarization, processing circuitry <NUM> of IMD <NUM> or other processing circuitry of system <NUM> may consider interval and morphological information for current ventricular depolarization 122C, preceding (in time) adjacent depolarization 122B, and subsequent (in time) adjacent depolarization 122D. The processing circuitry may iteratively determine whether each of ventricular depolarizations <NUM> is a PVC depolarization in this manner by proceeding to the next depolarization, e.g., depolarization 122C becomes the preceding adjacent depolarization, depolarization 122D becomes the current depolarization, and the next (in time) depolarization after depolarization 122D becomes the subsequent adjacent depolarization. Although the techniques for determining whether a ventricular depolarization is a PVC depolarization are described herein primarily as being performed by processing circuitry <NUM> of IMD <NUM>, such techniques may be performed, in whole or part, by processing circuitry of any one or more devices of system <NUM>, such as processing circuitry <NUM> of external device <NUM>, processing circuitry <NUM> of server <NUM>, or processing circuitry of one or more computing devices <NUM>.

In some examples, processing circuitry <NUM> determines respective inter-depolarization intervals 124A-124C (collectively "inter-depolarization intervals <NUM>"), e.g., R-R intervals, for each of depolarizations <NUM>. For example, processing circuitry <NUM> may determine inter-depolarization interval 124A for preceding adjacent depolarization 122B as the interval between the time of detection of ventricular depolarization 122A and the time of detection of ventricular depolarization 122B. Similarly, processing circuitry <NUM> may determine inter-depolarization interval 124B for current depolarization 122C as the interval between the time of detection of ventricular depolarization 122B and the time of detection of ventricular depolarization 122C, and inter-depolarization interval 124C for subsequent adjacent depolarization 122D as the interval between the time of detection of ventricular depolarization 122C and the time of detection of ventricular depolarization 122D.

Processing circuitry <NUM> may also identify respective segments of a digitized version of cardiac EGM signal <NUM> for each of ventricular depolarizations 122B-122D within respective windows 126A-126C (collectively "windows <NUM>"). Each of windows <NUM> may include a predetermined number of samples, e.g., sixteen samples sampled at <NUM>, of cardiac EGM signal <NUM>. The locations of the windows <NUM> and, thus, which samples of cardiac EGM signal <NUM> are within a given window <NUM>, may be set relative to the time point at which processing circuitry <NUM> detected the corresponding ventricular depolarization <NUM>, or another fiducial marker of cardiac EGM signal <NUM>. In some examples, each of windows <NUM> includes sixteen samples of cardiac EGM signal <NUM> starting four samples before the point of detection of the respective depolarization <NUM>.

To determine whether current ventricular depolarization 122C is a PVC depolarization, processing circuitry <NUM> may determine whether ventricular depolarizations 122B-122D satisfy one or more morphological criteria based on the segments within respective windows <NUM>. For each of depolarizations 122B-122D, processing circuitry <NUM> may determine, as examples, one or more of a maximum amplitude, a minimum amplitude, a maximum slope, and a minimum slope within the respective window 126A-126C. Processing circuitry <NUM> may determine the time interval, e.g., number of samples, also referred to herein as the slope interval, between the point of the maximum slope and the point of the minimum slope for each of depolarizations 122B-122D. Processing circuitry <NUM> may determine the slope of cardiac EGM signal <NUM> using any known techniques, such as by determining a derivative or differential signal of cardiac EGM signal <NUM>.

The morphological criteria may include criteria relating the degree of correlation between the various possible pairings of depolarizations 122B-122D. Processing circuitry <NUM> may determine correlation values for a pair of depolarizations by performing a correlation operation with the segments of cardiac EGM signal <NUM> within the respective windows <NUM> for the depolarizations. Example correlation operations include any known cross-correlation, wavelet-based comparison, feature set comparison, or difference sum techniques.

An example formula for computing cross correlation is: <MAT> where x and y are the two segments of cardiac EGM signal <NUM> to be compared and different values of L are the different lags over which the cross-correlation is computed. This equation represents shifting one of the segments by a lag (L), multiplying it with the other segment point-by-point, and adding the multiplied result point-by-point. The same process is followed for different lags. In some examples, the lags are +/- four samples. The maximum of C(L) will happen at the lag where the two segments x and y match the best with each other. In such examples, processing circuitry <NUM> may determine the maximum of C(L) as the correlation value for a given comparison between two ventricular depolarizations <NUM>.

In order to conserve the processing and power resources of IMD <NUM>, processing circuitry <NUM> may implement a difference sum technique for determining correlation values representative of the degree of correlation between the various pairings of depolarizations 122B-122D. Processing circuitry <NUM> may determine a point-by-point difference between the segments of cardiac EGM signal <NUM> for the two depolarizations <NUM> at various lags, such as +/- four samples, and the lag which has the lowest difference sum will have the highest correlation between the depolarizations <NUM>. An example formula for computing the difference sum is: <MAT> where x and y are the two segments of cardiac EGM signal <NUM> to be compared and different values of L are the different lags over which the difference sum is computed. In contrast to C(L), the lowest difference sum value D(L) will occur at the lag where the two segments x and y match best with each other. In other words, the lag with the greatest degree correlation between segments x and y will have the lowest difference sum value D(L).

In some examples, to determine whether current ventricular depolarization 122C is a PVC depolarization (e.g., to detect a PVC), processing circuitry <NUM> determines a correlation value between current ventricular depolarization 122C and each of preceding adjacent ventricular depolarization 122B and subsequent adjacent depolarization 122D. In the example illustrated by <FIG>, current ventricular depolarization 122C is a PVC depolarization and both adjacent ventricular depolarizations 122B and 122D are normal ventricular depolarizations. Since ventricular depolarization 122C has a different morphology than both of adjacent ventricular depolarizations 122B and 122D, the correlation values determined by processing circuitry <NUM> for these two comparisons are both expected to indicate a relatively low degree of correlation, e.g., a relatively high difference sum value. Processing circuitry <NUM> may also determine a correlation value between adjacent ventricular depolarizations 122B and 122D. Since ventricular depolarizations 122B and 122D are both normal ventricular depolarizations expected to have similar morphologies, the correlation value between them is expected to indicate a relative high degree of correlation, e.g., a relatively low difference sum value. Processing circuitry <NUM> may apply any combination of one or more of the morphological criteria described herein.

To determine whether current ventricular depolarization 122C is a PVC depolarization, processing circuitry <NUM> may also evaluate the respective inter-depolarization intervals 124A-124C for ventricular depolarizations 122B-122D. Since current ventricular depolarization 122C is a PVC depolarization, inter-depolarization interval 124B is expected to be shorter than inter-depolarization interval 124A and inter-depolarization interval 124C is expected to be longer than inter-depolarization interval 124A due to a compensatory pause following the PVC depolarization. Processing circuitry <NUM> may also evaluate the maximum and minimum amplitudes, and the slope intervals, for ventricular depolarizations 122B-122D to determine whether depolarization 122C is a PVC depolarization. Since 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 and minimum slope for depolarization 122C is expected to be more than that of a normal depolarization, such as adjacent depolarizations 122B and 122D. For PVC detection, some examples may implement the techniques described in <CIT>; <CIT>and <CIT>; and <CIT>.

After determining that current ventricular depolarization 122C is a PVC depolarization, processing circuitry <NUM> may classify current ventricular depolarization 122C. For example, processing circuitry <NUM> may store a plurality of classifications in storage device <NUM>. In the example shown in <FIG>, storage device <NUM> may contain classifications <NUM>, <NUM>, and <NUM> represented by PVC morphologies 127A, 128A, and 129A, respectively. In some examples, each of PVC morphologies 127A, 128A, and 129A may comprise the average or mean morphology of the PVC signals detected under the corresponding classification. In other examples, each of the of PVC morphologies 127A, 128A, and 129A may comprise the last PVC signal classified under the corresponding classification.

To classify ventricular depolarization 122C or the portion of EGM signal <NUM> associated with ventricular depolarization 122C (e.g., the portion of EGM signal <NUM> contained within window 126B) (herein referred to as "PVC 122C"), processing circuitry <NUM> may determine a difference between PVC 122C and each of PVC morphologies 127A, 128A, and 129A to identify the closest classification to PVC 122C. For example, processing circuitry <NUM> may determine correlation values between PVC 122C and each of PVC morphologies 127A, 128A, and 129A. To determine these correlation values, processing circuitry <NUM> may perform any of the correlation operations described above (e.g., cross correlation, wavelet-based comparison, feature set comparison, or difference sum techniques). For example, processing circuitry <NUM> may determine correlation values between PVC 122C and each of PVC morphologies 127A, 128A, and 129A using the difference sum technique to identify the closest classification (e.g., the classification with the lowest difference sum). In some examples, processing circuitry <NUM> can determine the Euclidean distance between PVC 122C and each of the PVC morphologies to identify the closest classification (e.g., the PVC morphology with the lowest Euclidean distance to PVC 122C). An example formula for computing a Euclidean distance is: <MAT> where x and y are the two points of PVC 122C and a stored PVC morphology. In some examples, processing circuitry <NUM> may determine N Euclidean distances between N different points on PVC 122C and each stored PVC morphology and either add the N Euclidean distances or calculate the average Euclidean distance to determine the correlation value between PVC 122C and each stored PVC morphology. Either way, the PVC morphology with the lowest Euclidean distance will represent the closest stored classification to PVC 122C. In some examples, processing circuitry <NUM> may classify PVC 122C using a clustering algorithm, such as K-Means clustering, for example.

In the example shown in <FIG>, processing circuitry <NUM> may determine PVC morphology 128A as the closest classification to PVC 122C after determining the correlation values (e.g., the difference sum) between PVC 122C and each of PVC morphologies 127A, 128A, and 129A, and finding that the correlation value of PVC morphology 128A is the lowest of the three correlation values. While only three classifications are shown in <FIG>, processing circuitry <NUM> may store fewer or more classifications in accordance with this disclosure. For example, processing circuitry <NUM> may store any number (N) classifications, and processing circuitry <NUM> would 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 <NUM> may then determine whether the correlation value (e.g., the difference sum) between PVC 122C and PVC morphology 128A is below a threshold value. If the correlation value is equal to or above the threshold value, processing circuitry <NUM> will classify PVC 122C as a new classification (e.g., "Classification <NUM>") and optionally store PVC 122C as the PVC morphology for that new classification. If the correlation value is below the threshold value, processing circuitry <NUM> will classify PVC 122C under "Classification <NUM>. " In some examples, when the PVC morphology 128A represents the average or mean morphology of the PVC signals detected under "Classification <NUM>," processing circuitry <NUM> will update PVC morphology 128A (e.g., recalculate the mean or average PVC morphology) to include the morphology of PVC 122C (e.g., as shown in PVC morphology 128AA of <FIG>) if PVC 122C is classified as "Classification <NUM>.

<FIG> illustrates exemplary classified PVC cardiac EGM signals. In particular, <FIG> shows classifications <NUM>, <NUM>, and <NUM> represented by PVC morphologies 127A, 128AA, and 129A, respectively. While only three classifications are shown in <FIG>, processing circuitry <NUM> may store fewer or more classifications in accordance with this disclosure. In this example, each of PVC morphologies 127A, 128AA, and 129A comprise the average or mean morphology of the PVC signals detected under the corresponding classification. For example, PVC morphology 127A represents the average or mean of PVC signals 127B, 127C, and 127D; PVC morphology 128AA represents the average or mean of PVC signals 128B, 128C, 128D, and PVC 122C of <FIG>; and PVC morphology 129A represents the average or mean of PVC signals 129B and 129C. In some examples, PVC morphology 128AA may include PVC 122C of <FIG>. In some examples, each of PVC signals 127B, 127C, 127D, 128B, 128C, 128D, 129B, and 129C comprise portions of a cardiac EGM signal corresponding to a PVC (e.g., the P, QRS, and T waves of the PVC).

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

In some examples, processing circuitry <NUM> stores each of PVC signals 127A, 127B, 127C, 127D, 128AA, 128B, 128C, 128D, 129A, 129B, and 129C in a cloud (e.g., external device <NUM> and/or <NUM>). In some examples, the cloud may periodically (e.g., every week, month, year, or any other period of time) update the average or mean morphology of the last N number (e.g., <NUM>, <NUM>) of stored PVC signals. In some examples, the cloud may not purge any stored PVC signals.

In some examples, if a particular classification may not match to any detected PVC for a sufficiently long period of time (e.g., a <NUM> months, <NUM> months, a year, or any other period of time), processing circuitry <NUM> may "retire" or "archive" that classification (e.g., processing circuitry <NUM> may no longer compare future detected PVCs to that classification) as it may not be applicable anymore to the clinical situation at present. For example, processing circuitry <NUM> may keep track of how many matches are being generated for a given classification and when the PVCs were detected.

<FIG> is a flow diagram illustrating an example operation for triggering the storage of a cardiac EGM signal associated with a PVC. Although the example operation of <FIG> is described as being performed by processing circuitry <NUM> of IMD <NUM> and with respect to cardiac EGM signal <NUM> of <FIG>, in other examples some or all of the example operation may be performed by processing circuitry of another device and with respect to any cardiac EGM.

According to the example of <FIG>, processing circuitry <NUM> senses a cardiac EGM signal of a patient (e.g., via electrodes <NUM>) (<NUM>). Next, processing circuitry <NUM> detects a PVC within the cardiac EGM signal (e.g., as described above with reference to <FIG> or any other known method of detecting PVCs within a cardiac EGM signal).

In some examples, processing circuitry <NUM> keeps a count of detected PVCs for a period of time (e.g., <NUM> hours, <NUM> hours, a week, a month).

Processing circuitry <NUM> then classifies the detected PVC (<NUM>). As described above with reference to <FIG>, to classify PVC 122C, processing circuitry <NUM> may compare the morphology of PVC <NUM> to each of PVC morphologies 127A, 128A, and 129A corresponding to the classifications stored in storage device <NUM> to identify the closest classification to PVC 122C. If the difference between PVC 122C and the closest classification in storage device <NUM> is equal to above a threshold difference, processing circuitry <NUM> will create a new classification and store PVC 122C as the PVC morphology for that new classification. If the difference between PVC 122C and the closest classification in storage device <NUM> is less than a threshold difference, processing circuitry <NUM> will classification PVC 122C as the closest classification. In some examples, processing circuitry <NUM> can determine the Euclidean distance between PVC 122C and each of the PVC morphologies to identify the closest classification (e.g., the PVC morphology with the lowest Euclidean distance to PVC 122C). In the example shown in <FIG>, PVC morphology 128A is the closest classification to PVC 112C and the difference (e.g., the correlation value) between PVC morphology 128A and PVC 112C is below a threshold value and processing circuitry <NUM> classifies PVC 122C under "Classification <NUM>. " In some examples, when the PVC morphology 128A represents the average or mean morphology of the PVC signals detected under "Classification <NUM>," processing circuitry <NUM> will update PVC morphology 128A (e.g., recalculate the mean or average PVC morphology) to include the morphology of PVC 122C (e.g., as shown in PVC morphology 128AA of <FIG>). In some examples, processing circuitry <NUM> may classify PVC 122C using a clustering algorithm, such as K-Means clustering, for example. In some examples, processing circuitry <NUM> keeps a count of detected or other quantification of the PVCs detected for a given classification (e.g., determines a PVC burden by classification).

Processing circuitry <NUM> further determines whether the PVC storage criteria is met (<NUM>). In some examples, PVC storage criteria is met if the PVC burden exceeds a PVC burden threshold, if a new PVC classification is detected, if a bigeminy or trigeminy event is detected, or if an R-on-T phenomenon is detected (e.g., as described in further detail below with reference to <FIG>).

Based on a determination that PVC storage criteria is not met (NO branch of <NUM>), processing circuitry <NUM> may eschew storing the portion of cardiac EGM signal <NUM> associated with PVC 122C (<NUM>). Based on a determination that PVC storage criteria is met (YES branch of <NUM>), processing circuitry <NUM> may store the portion of cardiac EGM signal <NUM> associated with PVC 122C (<NUM>). For example, processing circuitry <NUM> may store the portion of cardiac EGM signal <NUM> associated with PVC 122C in storage device <NUM>. In some examples, processing circuitry <NUM> stores PVC 122C with other PVCs occurring within a first period of time (e.g., <NUM> hours, a week, a month). In some examples, processing circuitry <NUM> may transmit the portion of cardiac EGM signal <NUM> associated with PVC 122C to another device via a network in response to a determination that PVC storage criteria is met (YES branch of <NUM>). For example, processing circuitry <NUM> may transmit the portion of cardiac EGM signal <NUM> associated with PVC 122C to external device <NUM>, server <NUM>, any of computing devices <NUM>, or any other device. In other examples, the portion of cardiac EGM signal <NUM> associated with PVC 122C may include one or more other beats around the PVC 122C. For example, processing circuitry <NUM> may store or transmit a portion of the cardiac EGM signal including PVC 122C with a duration between two and fourteen minutes. In some examples, processing circuitry <NUM> may store or transmit other PVC information, including PVC burden information (e.g., total and/or by classification) for a given duration of time (e.g., <NUM> hours, a week, a month) or timing information (e.g., start and end time), for example.

<FIG> is a flow diagram illustrating an example operation for triggering the storage of a portion of a cardiac EGM signal associated with a PVC. The example operation of <FIG> may be an example implementation of element <NUM> of <FIG>, and is illustrated as beginning from element <NUM>. In other examples, the example operation of <FIG> may be performed as part of another method for triggering the storage of a portion of a cardiac EGM signal associated with a PVC. In some examples, elements <NUM>-<NUM> may be performed in any order. In some examples, one or more of elements <NUM>-<NUM> need not be performed.

According to the example of <FIG>, processing circuitry <NUM> determines whether a PVC burden threshold is exceeded (<NUM>). In some examples, processing circuitry <NUM> may determine that PVC burden threshold is exceeded if it is above <NUM>% of a period a time (e.g., <NUM> hours, <NUM> hours, <NUM> hours, a week, a month). In some examples, processing circuitry <NUM> will determine a PVC burden for all detected PVCs. In some examples, processing circuitry <NUM> will determine a PVC burden for each PVC classification stored in storage device <NUM>. For example, processing circuitry <NUM> will determine a total PVC burden, a first PVC burden for Classification <NUM> (PVC morphology 127A), a second PVC burden for Classification <NUM> (PVC morphology 128A or 128AA), and a third PVC burden for Classification <NUM> (PVC morphology 129A). In this way, processing circuitry <NUM> will determine whether any of the total, first, second, or third PVC burdens exceeds the PVC burden threshold (<NUM>). Either way, if a PVC burden threshold is exceeded (YES branch of <NUM>), processing circuitry <NUM> will store the portion of cardiac EGM signal <NUM> associated with the detected PVC (<NUM>).

If processing circuitry <NUM> determines that the PVC burden threshold is not exceeded (NO branch of <NUM>), processing circuitry <NUM> determines whether a new PVC classification is detected (<NUM>). As described above with reference to <FIG>, processing circuitry <NUM> will detect a new classification if the closest existing classification to the detected PVC is too different. For example, if the correlation value (e.g., the difference sum) between PVC 122C and PVC morphology 128A is equal to or above a threshold value in the example in <FIG>, processing circuitry <NUM> will classify PVC 122C as a new classification (e.g., "Classification <NUM>"). If a new PVC classification is detected (e.g., because the correlation value between the detected PVC and the closest classification stored in storage device <NUM> it too great) (YES branch of <NUM>), processing circuitry <NUM> will store the portion of cardiac EGM signal <NUM> associated with the detected PVC (<NUM>).

If processing circuitry <NUM> determines that a new PVC classification is not detected (NO branch of <NUM>), processing circuitry <NUM> determines whether a bigeminy or trigeminy event is detected in a cardiac EGM signal (<NUM>). Processing circuitry <NUM> will detect a bigeminy event in a cardiac EGM signal if each detected normal beat is followed by a PVC or by any other abnormal beat. For example, a beat pattern in a cardiac EGM signal comprising a normal beat, a PVC, a normal beat, a PVC, and so on would constitute a bigeminy event (whether or not the detected PVCs are of the same or different classifications). Processing circuitry <NUM> will detect a trigeminy event in a cardia EGM signal if processing circuitry <NUM> detects two normal beats followed by a PVC or if processing circuitry <NUM> detects a normal beat followed by two PVCs (YES branch of <NUM>). For example, to detect the bigeminy event in cardiac EGM signal <NUM>, processing circuitry <NUM> may determine that 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 (whether or not the detected PVCs are of the same or different classifications). In the example shown in <FIG>, processing circuitry <NUM> may detect a bigeminy event in cardiac EGM signal <NUM> because ventricular depolarization 122A is a PVC depolarization, ventricular depolarization 122B is a normal depolarization, and ventricular depolarization 122C is a PVC. In some examples, processing circuitry <NUM> may determine whether a quadrigeminy event occurs at element <NUM>. Processing circuitry <NUM> will detect a quadrigeminy event in a cardia EGM signal if each detected normal beat is followed by three consecutive PVCs or if every fourth beat is a PVC (whether or not the detected PVCs are of the same or different classifications). In some examples, processing circuitry <NUM> may determine whether a couplet event occurs at element <NUM> if processing circuitry <NUM> detects two consecutive PVCs in a cardiac EGM signal (whether or not the detected PVCs are of the same or different classifications). In some examples, processing circuitry <NUM> may determine whether a triplet event occurs at element <NUM> if processing circuitry <NUM> detects three consecutive PVCs in a cardiac EGM signal (whether or not the detected PVCs are of the same or different classifications). If a bigeminy, a trigeminy, a quadrigeminy, a couplet, or a triplet event is detected in a cardiac EGM signal (YES branch of <NUM>), processing circuitry <NUM> will store the portion of cardiac EGM signal <NUM> associated with the detected PVCs (<NUM>).

If processing circuitry <NUM> determines that a bigeminy or trigeminy is not detected in a cardiac EGM signal (NO branch of <NUM>), processing circuitry <NUM> determines whether an R-on-T phenomenon is detected (<NUM>). Processing circuitry <NUM> will detect an R-on-T phenomenon when it detects a PVC (e.g., a PVC depolarization) on the T-wave of the previous beat in the cardiac EGM signal. An R-on-T phenomenon is a particularly dangerous event because ventricular fibrillation and death can occur. During the T-wave (repolarization), the heart muscle is very sensitive to outside stimulus and a strong PVC can send the myocardium into fibrillation. If an R-on-T phenomenon is detected in a cardiac EGM signal (YES branch of <NUM>), processing circuitry <NUM> will store the portion of cardiac EGM signal <NUM> associated with the detected PVC (<NUM>). In some examples, processing circuitry <NUM> will generate an automated clinician alert in response to detecting an R-on-T phenomenon. If processing circuitry <NUM> determines that an R-on-T phenomenon is not detected in a cardiac EGM signal (NO branch of <NUM>), processing circuitry <NUM> may eschew storing the portion of cardiac EGM signal <NUM> associated with the detected PVC (<NUM>).

In some examples, processing circuitry <NUM> may determine whether one or more of criteria <NUM>-<NUM> are met. For example, processing circuitry <NUM> may perform elements <NUM>-<NUM> for every detected PVC. In that example, processing circuitry <NUM> will store which of criteria <NUM>-<NUM> are met in addition to storing the portion of cardiac EGM signal <NUM> associated with the detected PVC. For example, if a PVC burden threshold is exceeded (YES branch of <NUM>), processing circuitry <NUM> may store an indication that the PVC burden was exceeded and determines whether a new PVC classification is also detected (<NUM>). If processing circuitry <NUM> determines that a new PVC classification is detected (YES branch of <NUM>), processing circuitry <NUM> may store an indication that a new PVC classification was detected and also determines whether a bigeminy or trigeminy event is detected in a cardiac EGM signal (<NUM>). If processing circuitry <NUM> determines that a bigeminy, trigeminy, quadrigeminy, couplet, or triplet event was detected in a cardiac EGM signal (YES branch of <NUM>), processing circuitry <NUM> processing circuitry <NUM> may store the detected PVCs and an indication that bigeminy, trigeminy, quadrigeminy, couplet, or triplet event was detected and also determines whether an R-on-T phenomenon is detected (<NUM>). In this example, elements <NUM>-<NUM> could be performed serially, concurrently, or in any order. <FIG> is a flow diagram illustrating an example operation for classifying PVCs. The example operation of <FIG> may be an example implementation of element <NUM> of <FIG>. In other examples, the example operation of <FIG> may be performed as part of another method for triggering the storage of a portion of a cardiac EGM signal associated with a PVC.

According to the example of <FIG>, processing circuitry <NUM> determines a difference between a detected PVC and each stored PCV classification in storage device <NUM> (<NUM>). As shown in the example in <FIG>, storage device <NUM> may contain classifications <NUM>, <NUM>, and <NUM> represented by PVC morphologies 127A, 128A, and 129A, respectively. In some examples, processing circuitry <NUM> may determine a difference between PVC 122C and each of PVC morphologies 127A, 128A, and 129A by determining correlation values between PVC 122C and each of PVC morphologies 127A, 128A, and 129A. To determine these correlation values, processing circuitry <NUM> may perform any of the correlation operations described above with reference to <FIG> (e.g., cross correlation, wavelet-based comparison, feature set comparison, difference sum, or Euclidean distance techniques). For example, processing circuitry <NUM> may determine correlation values between PVC 122C and each of PVC morphologies using the difference sum technique. Based on the correlation values, processing circuitry <NUM> then identifies the closest stored classification to PVC 122C (<NUM>). In the example shown in <FIG>, processing circuitry <NUM> may identify PVC morphology 128A as the closest classification to PVC 122C (e.g., the classification with the lowest difference sum).

Processing circuitry <NUM> may then determines whether the difference between the detected PVC and the closest classification is below a threshold value (<NUM>). For example, processing circuitry <NUM> may determine whether the correlation value (e.g., the difference sum) between PVC 122C and PVC morphology 128A is below a threshold value. Based on a determination that the correlation value is equal to or above the threshold value (NO branch of <NUM>), processing circuitry <NUM> will classify PVC 122C as a new classification (e.g., "Classification <NUM>") and optionally store PVC 122C as the PVC morphology for that new classification (<NUM>). Based on a determination that the correlation value is below the threshold value (YES branch of <NUM>), processing circuitry <NUM> will classify PVC 122C under "Classification <NUM>. " In some examples, when the PVC morphology 128A represents the average or mean morphology of the PVC signals detected under "Classification <NUM>," processing circuitry <NUM> will update PVC morphology 128A (e.g., recalculate the mean or average PVC morphology) to include the morphology of PVC 122C (e.g., as shown in PVC morphology 128AA of <FIG>).

<FIG> is a graph illustrating example PVC information that may be presented to a user. For example, graph <NUM> may comprise a user interface for use by a physician, clinical technician, or any other user to review PVC information classified and stored in accordance with techniques of this disclosure.

In the example shown in <FIG>, the number of PVCs detected (e.g., the PVC burden) for each day are presented. For example, <FIG> illustrates the total PVC burden on each day with line graph <NUM>. <FIG> also illustrates bar graphs with a visual break down of the PVC burden by classification on each day. For example, the per-day PVC burden for Classification <NUM> is shown by bars 1002A, 1002B, and 1002C (collectively, "bars <NUM>"); the per-day PVC burden for Classification <NUM> is shown by bars 1004A, 1004B, and 1004C (collectively, "bars <NUM>"); and the per-day PVC burden for Classification <NUM> is shown by bars 1006A, 1006B, and 1006C (collectively, "bars <NUM>"). While <FIG> shows bars <NUM>, <NUM>, and <NUM> stacked on top of each other in order with the highest PVC burden per day at the bottom and the lowest PVC burden per day at the top, it is understood that each of bars <NUM>, <NUM>, and <NUM> may be displayed adjacent to each other and/or in any order. In some examples, a physician, clinical technician, or any other user may select any of bars <NUM>, <NUM>, and <NUM> and the system will display additional PVC information (in the same screen or in a pop up screen). For example, the system may display stored portions of the cardiac EGM signal corresponding to the detected PVCs for that particular day. For example, a user may select bar 1002A and the system may display stored portions of the cardiac EGM signal corresponding to the detected Classification <NUM> PVCs for <NUM>/<NUM>. In this way, the system may help physicians detect and identify patterns of PVCs to better treat their patients, particularly patients with existing heart problems. For example, physicians may want to view and analyze EGM data about the morphologies of the PVCs detected in a particular patient. This may assist a physician to localize the origin of the PVCs and/or determine whether there are multiple triggers causing the PVCs, which may facilitate more accurate determinations of cardiac wellness and risk of sudden cardiac death, and may lead to clinical interventions to suppress PVCs such as medications.

As described above, processing circuitry, such as processing circuitry <NUM> of IMD <NUM>, may 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 described techniques in hardware may improve the computation and power performance of the implementing device, e.g., IMD <NUM>. As examples, processing circuitry may include hardware configured to compute difference sums or other correlation values, and include firmware for other functionality described herein.

The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the techniques may be implemented within one or more microprocessors, 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, stimulators, or other devices. The terms "processor" and "processing circuitry" may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry, and alone or in combination with other digital or analog circuitry.

For aspects implemented in software, at least some of the functionality ascribed to the systems and devices described in this disclosure may be embodied as instructions on a computer-readable storage medium such as RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. The instructions may be executed to support one or more aspects of the functionality described in this disclosure.

Claim 1:
A medical system comprising:
a plurality of electrodes configured to sense a cardiac electrogram (EGM) signal of a patient; and
processing circuitry configured to:
detect a premature ventricular contraction (PVC) within the cardiac EGM signal;
determine whether PVC storage criteria is met;
in response to a determination that the PVC storage criteria is met, store a portion of the cardiac EGM signal associated with the PVC; and
in response to a determination that the PVC storage criteria is not met, eschew storing the portion of the cardiac EGM signal associated with the PVC.