Patent Description:
This disclosure generally relates to medical devices and, more particularly, to detection of medical events by medical devices.

Medical devices may be external or implanted. Such medical devices may be used to detect medical events experienced by a patient, and in some cases deliver therapy to the patient to treat detected medical events. A diagnostic medical device may sense parametric patient data and periodically upload such data to, e.g., another medical device, a remote monitoring system, or a clinician or caregiver for review. Therapeutic medical devices may be used to treat various symptoms, diseases, or conditions, such as cardiac arrythmia, chronic pain, tremor, Parkinson's disease, epilepsy, urinary or fecal incontinence, sexual dysfunction, obesity, spasticity, gastroparesis, hypoglycemia or hyperglycemia, neurological or sleep disorders, etc. Therapeutic medical devices may deliver therapy in the form of electrical stimulation, mechanical stimulation, drug delivery, or other therapy delivery techniques.

|<CIT> relates to medical premonitory event estimation. <CIT> relates to multi-tier prediction of cardiac tachyarrythmia. <CIT> relates to an enhanced wearable therapy device paired with an insertable cardiac monitor.

Methods described hereinafter are not specifically claimed but are presented for understanding the invention.

In general, the disclosure describes multi-tier techniques to identify a treatable event, such as a treatable arrhythmia in a patient. As described herein, a treatable event refers to an event for which therapy may be delivered to treat the treatable event. As examples, responsive to a medical device detecting a treatable event, the medical device may perform an action to treat the treatable event, such as deliver therapy to the patient or generating a notification to another medical device or a patient, clinician, or caregiver such that the other medical device or patient, clinician, or caregiver may provide therapy for the treatable event or a notification to the patient to, e.g., take medication.

In one example of the techniques of the disclosure, a first medical device (such as an IMD) senses parametric data for a patient. The IMD determines, based on the parametric data, that the patient is experiencing a treatable event. In response to detecting the treatable event, the IMD attempts to establish a wireless connection to a second medical device (e.g., an external device such as an external programmer or mobile device). If the IMD is unable to establish the wireless connection, the IMD may determine whether the IMD should attempt to treat the treatable event, such as by delivering therapy to the patient for the treatable event or issuing an alert or notification to another device or clinician that the treatable event has occurred.

If the IMD is able to establish the wireless connection, then IMD transmits at least a portion of the parametric data to the external device. The external device reviews the parametric data to confirm whether the patient is experiencing the treatable event. In some examples, the external device performs a more sophisticated analysis of the parametric data, analyzes a larger portion of the parametric data, or analyzes different aspects of the parametric data than the IMD so as to increase an accuracy in the determination of whether the patient is experiencing the treatable event. The external device selects an instruction for responding to the treatable event based on the second analysis and transmits the instruction to the IMD. For example, upon verifying that the patient is experiencing the treatable event, the external device instructs the IMD to proceed with delivery of therapy. In some examples, upon verifying that the patient is experiencing the treatable event, the external device instructs the IMD to generate a notification or alert indicative of the treatable event.

However, in response to determining that the patient is not experiencing the treatable event, the external device transmits, to the IMD, an instruction configured to cause the IMD to abort delivery of therapy to the patient. The IMD receives the instruction and aborts delivery of therapy to the patient, e.g., prior to delivery of therapy to the patient or during delivery of therapy to the patient. As another example, in response to determining that the patient is not experiencing the treatable event, the external device transmits, to the IMD, an instruction configured to cause the IMD to abort reporting of the treatable event. The IMD receives the instruction and does not generate a notification of the treatable event, e.g., prior to or during generation of the notification and/or transmission of the notification to a clinician or remote monitoring system.

In some examples, if the IMD is unable to establish the wireless connection, the IMD may analyze a relatively smaller amount of parametric data, and/or perform an analysis that requires a relatively shorter amount of time to complete. In this fashion, the IMD may classify an event as treatable or not treatable and rapidly determine whether to perform an action in response to the occurrence of the treatable event. However, if the IMD is able to establish the wireless connection, then the IMD may continue to sense parametric data and extend or continue performance of the first analysis while the external device performs the second analysis. For example, after establishing the wireless connection, the IMD may analyze a relatively larger amount of parametric data, and/or perform an analysis that requires a relatively longer amount of time to complete. This may allow the IMD to have higher specificity in classifying treatable events because more data is analyzed. Furthermore, the IMD may provide additional time to the external device such that the external device may have sufficient time to receive the parametric data, analyze the transmitted parametric data, verify the determination by the IMD to deliver therapy to the patient, and instruct the IMD whether to proceed with an action responsive to the occurrence of the treatable event.

Thus, the techniques disclosed herein may allow for enhanced patient care and increase the accuracy or specificity of treatable event detection in a patient. For example, the techniques disclosed herein enable the efficient use of sophisticated, but more computationally-intensive analysis of the parametric data so as to more accurately determine whether a treatable event has occurred in the patient, thereby increasing the likelihood that therapy delivered by the IMD is appropriate. Furthermore, by using an external device to perform computationally-intensive analysis of the parametric data, an IMD system as described herein may more accurately determine whether a treatable event has occurred in the patient without increasing the power consumption, size, or computational ability of the IMD.

In one example, this disclosure describes an exemplary method comprising: sensing, by a first device, parametric data for a patient; determining, by the first device and based on a first analysis of the parametric data, that the patient is experiencing a treatable event; in response to determining that the patient is experiencing the treatable event, determining, by the first device, whether a second device is available for wireless communication; in response to determining that the second device is available for wireless communication, transmitting, from the first device to the second device, at least a portion of the parametric data for a second analysis of the at least a portion of the parametric data by the second device; and receiving, by the first device and from the second device, an instruction for responding to the treatable event, the instruction selected based on the second analysis of the at least a portion of the parametric data by the second device.

In another example, this disclosure describes a first device comprising: sensing circuitry configured to sense parametric data for a patient; processing circuitry configured to: determine, based on a first analysis of the parametric data, that the patient is experiencing a treatable event; and in response to determining that the patient is experiencing the treatable event, determine whether a second device is available for wireless communication; and communication circuitry configured to: transmit, to the second device, at least a portion of the parametric data for a second analysis of the at least a portion of the parametric data by the second device in response to the determination that the second device is available for wireless communication; and receive, from the second device, an instruction for responding to the treatable event, the instruction selected based on the second analysis of the at least a portion of the parametric data by the second device.

In another example, this disclosure describes an exemplary method comprising: establishing, by a second device, wireless communication with a first device; receiving, by the second device and from the first device, parametric data for a patient; receiving, by the second device and from the first device, an indication that the first device has determined, based on a first analysis of the parametric data, that the patient is experiencing a treatable event; performing, by the second device, a second analysis of the parametric data to verify whether the patient is experiencing the treatable event; selecting, by the second device and based on the second analysis of the parametric data, an instruction for responding to the treatable event; and transmitting, by the second device and to the first device, the instruction for responding to the treatable event.

In another example, this disclosure describes a second device comprising: communication circuitry configured to: establish wireless communication with a first device; receive, from the first device, parametric data for a patient; and receive, from the first device, an indication that the first device has determined, based on a first analysis of the parametric data, that the patient is experiencing a treatable event; and processing circuitry configured to: perform a second analysis of the parametric data to verify whether the patient is experiencing the treatable event; and select, based on a second analysis of the parametric data, an instruction for responding to the treatable event, wherein the communication circuitry is further configured to transmit, to the first device, the instruction for responding to the treatable event.

In another example, this disclosure describes a system comprising: a first device configured to: sense parametric data for a patient; determine, based on a first analysis of the parametric data, that the patient is experiencing a treatable event; in response to determining that the patient is experiencing the treatable event, determine whether a second device is available for wireless communication; in response to determining that the second device is available for wireless communication, transmit, to the second device, at least a portion of the parametric data; and the second device configured to: receive the at least a portion of the parametric data; perform a second analysis of the at least a portion of the parametric data to verify whether the patient is experiencing the treatable event; and select, based on a second analysis of the parametric data, an instruction for responding to the treatable event; and transmit, to the first device, the instruction for responding to the treatable event, wherein the first device is further configured to: receive the instruction for responding to the treatable event.

Techniques are disclosed for a medical device system that provides improved specificity of detection of treatable events. In examples, where the medical device system is a therapy delivery system, the techniques set forth herein may enable such a medical device system to improve an evaluation of the appropriateness of delivery of therapy for such treatable events. In examples, where the medical device system is a diagnostics systems, the techniques set forth herein may enable such a medical device system to improve the accuracy of event detection and notification.

In some examples, the treatable events comprise cardiac arrhythmia events, including cardiac tachyarrhythmia such as ventricular tachyarrhythmia (VT) or ventricular fibrillation (VF), and an IMD is configured to treat such events by providing cardiac pacing therapy or defibrillation shock therapy or by generating a notification of the treatable event to inform another medical device, clinician, or the patient of the occurrence of the treatable event. Arrhythmia is a group of conditions in which a heartbeat of a heart of a patient is irregular. Arrhythmia may occur where the heartbeat is too slow (e.g., bradycardia) or too fast (e.g., tachycardia). Arrhythmia may take the form of, e.g., extra heartbeats, such as premature atrial contractions, premature ventricular contractions and premature junctional contractions, supraventricular tachycardias, ventricular arrhythmias and bradyarrhythmias; supraventricular tachycardias, such as atrial fibrillation, atrial flutter, or paroxysmal supraventricular tachycardia; or ventricular arrhythmias, such as ventricular fibrillation or ventricular tachycardia, or bradyarrhythmias. If arrhythmia continues for more than a few seconds, it may result in cardiogenic shock and cessation of effective blood circulation. Consequently, sudden cardiac death (SCD) may result in a matter of minutes.

Most arrhythmias may be treated through the delivery of electrical stimulation. For example, a medical device may sense a treatable arrhythmia via one or more electrodes disposed on a lead and deliver electrical stimulation therapy, such as pacing pulses or an anti-tachycardia shock, via the one or more electrodes to treat the sensed arrhythmia. Such medical devices may be, e.g., a device external to the patient or an implantable medical device (IMD), such as an implantable cardioverter defibrillator (ICD) for the case of ventricular fibrillation. Electrodes coupled to the ICD may be placed within the heart, on the heart, or otherwise at locations that facilitate delivery of electrical therapy to the heart, such as intrathoracic locations outside of the heart, or subcutaneous, extrathoracic locations.

A medical device system as described herein includes a first device (e.g., an IMD) and a second device (e.g., an external device, such as an external programmer or a mobile device). The IMD senses parametric data for a patient. The IMD determines, based on the parametric data, that the patient is experiencing a treatable event. In response to detecting the treatable event, the IMD attempts to establish a wireless connection to the external device. If the IMD is unable to establish the wireless connection, the IMD may determine whether the IMD should take an action to respond to the treatable event, such as delivery of therapy for the treatable event, transmittal of a notification of the occurrence of the treatable event, etc..

If the IMD is able to establish the wireless connection, then IMD transmits at least a portion of the parametric data to the external device. The external device reviews the parametric data using an algorithm with high specificity to confirm whether the patient is experiencing the treatable event. In some examples, the external device performs a more sophisticated analysis of the parametric data, analyzes a larger portion of the parametric data, or analyzes different aspects of the parametric data than the IMD so as to increase an accuracy in the determination of whether the patient is experiencing the treatable event. For example, the external device may implement a machine learning system or other types of artificial intelligence. The external device selects an instruction for responding to the treatable event based on the second analysis and transmits the instruction to the IMD. For example, upon verifying that the patient is experiencing the treatable event, the external device instructs the IMD to proceed with responding to the treatable event, such as by delivering therapy to the patient. However, in response to determining that the patient is not experiencing the treatable event, the external device transmits, to the IMD, an instruction configured to cause the IMD to abort a response to the treatable event, such as by aborting delivery of therapy to the patient. The IMD receives the instruction and aborts delivery of therapy to the patient, e.g., prior to delivery of therapy to the patient or during delivery of therapy to the patient.

In some examples, the external device determines that the parametric data depicts a treatable event and so transmits an instruction to the IMD to proceed with responding to the treatable event, such as by delivering therapy for the treatable event. Alternatively, the external device may determine that the parametric data depicts a treatable event and take no action so as to allow the IMD to continue its procedures for responding to the treatable event, e.g., by delivering therapy for the treatable event. In some examples, the external device determines that the treatable event initially detected by the IMD is a false positive, and transmits an instruction causing the IMD to abort delivery of therapy. If the therapy has not occurred, the IMD terminates its therapy delivery procedures. In some examples, the external device determines that the parametric data does not depict a treatable event but too much time has passed such that the IMD has already taken an action, such as by delivering therapy. In this case, the external device may forgo instructing the IMD to abort therapy.

In some examples, if the IMD is unable to establish the wireless connection, the IMD may analyze a relatively smaller amount of parametric data, and/or perform an analysis that requires a relatively shorter amount of time to complete. In this fashion, the IMD may classify an event as treatable or not treatable and rapidly determine whether therapy should be delivered. As an example of the above, the treatable event may be a cardiac arrhythmia event. In this example, the IMD performs initial arrhythmia classification on sensed parametric data to identify a potential cardiac arrhythmia event. The initial arrhythmia classification may comprise, e.g., tachyarrhythmia classification using, e.g., <NUM> out of a previously sensed <NUM> heartbeats having an interval no greater than <NUM> milliseconds. If the IMD is unable to establish a wireless connection to the external device (e.g., such as where the external device is not within range of the IMD), the IMD may determine, based on the initial arrhythmia classification, to proceed with delivery of therapy.

In other examples, if the IMD is unable to establish the wireless connection, the IMD may continue analysis so as to analyze a relatively larger amount of parametric data so as to increase the accuracy of an initial classification of the treatable event. For example, the treatable event may be a cardiac arrhythmia event. In this example, the IMD performs initial arrhythmia classification on sensed parametric data to identify a potential cardiac arrhythmia event. The initial arrhythmia classification may comprise, e.g., tachyarrhythmia classification using, e.g., <NUM> out of a previously sensed <NUM> heartbeats having an interval no greater than <NUM> milliseconds. If the IMD is unable to establish a wireless connection to the external device (e.g., such as where the external device is not within range of the IMD), the IMD may continue to perform tachyarrhythmia classification using a longer determination time, e.g., <NUM> out of a previously sensed <NUM> heartbeats having an interval no greater than <NUM> milliseconds. In this example, if the additional analysis by the IMD verifies the initial classification, the IMD may proceed with delivery of therapy. However, if the additional analysis by the IMD does not verify the initial classification, the IMD may abort delivery of therapy. In this fashion, the IMD may offload additional analysis of the parametric data to the external device where possible, thereby decreasing the power usage by the IMD when the external device is within range to establish a wireless connection.

Additionally, if the IMD is able to establish the wireless connection, then the IMD may continue to sense parametric data and extend or continue performance of the first analysis while the external device performs the second analysis. For example, after establishing the wireless connection, the IMD may analyze a relatively larger amount of parametric data, and/or perform an analysis that requires a relatively longer amount of time to complete.

Continuing with the foregoing example of cardiac arrhythmia, upon establishing a wireless connection with the external device, the IMD continues to sense parametric data from the patient and extends performance of arrhythmia classification of the new parametric patient data while the external device performs the second analysis of the parametric data. For example, the IMD may continue to perform tachyarrhythmia classification using a longer determination time, e.g., <NUM> out of a previously sensed <NUM> heartbeats having an interval no greater than <NUM> milliseconds. By continuing the analysis of the parametric data while the external device performs the second analysis, the IMD may have higher specificity in classifying treatable events because more data is analyzed. Furthermore, the IMD may provide additional time to the external device such that the external device may have sufficient time to receive the parametric data, analyze the transmitted parametric data, verify the determination by the IMD to deliver therapy to the patient, and instruct the IMD whether to proceed with delivery of therapy.

In some examples, in response to determining that the continued analysis of the parametric data performed by the IMD indicates the treatable event, the IMD may proceed with delivery of therapy to the patient. In this fashion, the IMD may override the second analysis by the external device with the continued analysis by the IMD. This may be desirable where the second analysis performed by the external device is based on out-of-date parametric data, and where the continued analysis performed by the IMD is based on the most recent parametric data and is indicative of increasing severity of symptoms in the patient.

<FIG> is a block diagram illustrating an example system for detecting treatable events in accordance with the techniques of the disclosure. Medical device system <NUM> includes IMD <NUM> and external device <NUM>. As illustrated by example system <NUM> in <FIG>, IMD <NUM> may, in some examples, be an implantable cardiac pacemaker, implantable cardioverter/defibrillator (ICD), or pacemaker/cardioverter/defibrillator, for example. IMD <NUM> is connected to leads <NUM>, <NUM> and <NUM>. IMD <NUM> is communicatively coupled, e.g., capable of being selectively communicatively coupled, to external device <NUM>. Although not illustrated in <FIG>, external device <NUM> may be communicatively coupled to one or more computing devices over a communication network.

In patients with a high risk of ventricular fibrillation, the use of an ICD has been shown to be beneficial at preventing sudden cardiac death (SCD). Malignant tachyarrhythmia, for example, ventricular fibrillation, is an uncoordinated contraction of the cardiac muscle of the ventricles in the heart, and is the most commonly identified arrhythmia in cardiac arrest patients. If this arrhythmia continues for more than a few seconds, it may result in cardiogenic shock and cessation of effective blood circulation. Consequently, sudden cardiac death (SCD) may result in a matter of minutes. An ICD is a battery powered electrical shock device. An ICD may include an electrical housing electrode (sometimes referred to as a can electrode), and is typically coupled to one or more other electrodes via electrical lead wires. Electrodes coupled to the ICD may be placed within the heart, on the heart, or otherwise at locations that facilitate delivery of electrical therapy to the heart, such as intrathoracic locations outside of the heart, or subcutaneous, extrathoracic locations.

If a tachyarrhythmia is sensed, the ICD may send a pulse via the electrodes to shock the heart and restore its normal rhythm. Some ICDs have been configured to attempt to terminate detected tachyarrhythmias by delivery of anti-tachycardia pacing (ATP) prior to delivery of a shock. Additionally, ICDs have been configured to deliver relatively high magnitude post-shock pacing after successful termination of a tachyarrhythmia with a shock, in order to support the heart as it recovers from the shock. Some ICDs also deliver bradycardia pacing, cardiac resynchronization therapy (CRT), or other forms of pacing.

Returning to <FIG>, IMD <NUM> senses electrical signals attendant to the depolarization and repolarization of heart <NUM>, e.g., a cardiac electrogram (EGM), via electrodes on one or more leads <NUM>, <NUM> and <NUM> or the housing of IMD <NUM>. IMD <NUM> may also deliver therapy in the form of electrical signals to heart <NUM> via electrodes located on one or more leads <NUM>, <NUM> and <NUM> or a housing of IMD <NUM>. The therapy may be pacing, cardioversion and/or defibrillation pulses. IMD <NUM> may monitor EGM signals collected by electrodes on leads <NUM>, <NUM> or <NUM>, and based on the EGM signal, diagnose, and treat cardiac episodes, such as tachyarrhythmias.

In some examples, IMD <NUM> includes communication circuitry <NUM> including any suitable circuitry, firmware, software, or any combination thereof for communicating with another device, such as external device <NUM> of <FIG>. For example, communication circuitry <NUM> may include one or more processors, memory, wireless radios, antennae, transmitters, receivers, modulation and demodulation circuitry, filters, amplifiers, or the like for radio frequency communication with other devices, such as external device <NUM>. IMD <NUM> may use communication circuitry <NUM> to receive downlinked data from to control one or more operations of IMD <NUM> and/or send uplinked data to external device <NUM>.

Leads <NUM>, <NUM>, <NUM> extend into the heart <NUM> of patient <NUM> to sense electrical activity of heart <NUM> and/or deliver electrical stimulation to heart <NUM>. In the example shown in <FIG>, right ventricular (RV) lead <NUM> extends through one or more veins (not shown), the superior vena cava (not shown), and right atrium <NUM>, and into right ventricle <NUM>. Left ventricular (LV) lead <NUM> extends through one or more veins, the vena cava, right atrium <NUM>, and into the coronary sinus <NUM> to a region adjacent to the free wall of left ventricle <NUM> of heart <NUM>. Right atrial (RA) lead <NUM> extends through one or more veins and the vena cava, and into the right atrium <NUM> of heart <NUM>.

While example system <NUM> of <FIG> depicts IMD <NUM>, in other examples, the techniques of the disclosure may be applied to other types of medical devices that are not necessarily implantable. For example, a medical device in accordance with the techniques of the disclosure may include a wearable medical device or "smart" apparel worn by patient <NUM>. For example, such a medical device may take the form of a wristwatch worn by patient <NUM>, circuitry that is adhesively affixed to patient <NUM>, or a wearable automated external defibrillator (WAED). In another example, a medical device as described herein may include an external medical device with implantable electrodes.

In some examples, external device <NUM> takes the form of an external programmer or mobile device, such as a mobile phone, a "smart" phone, a laptop, a tablet computer, a personal digital assistant (PDA), a wearable electronic device, a handheld computing device, computer workstation, server or other networked computing device, etc. In some examples, external device <NUM> is a CareLink™ monitor available from Medtronic, Inc. While depicted as a single device in the example of <FIG>, in some examples, external device <NUM> comprises one or more computing devices that implement a remote monitoring or remote care system. A user, such as a physician, technician, surgeon, electro-physiologist, or other clinician, may interact with external device <NUM> to retrieve physiological or diagnostic information from IMD <NUM>. A user, such as patient <NUM> or a clinician as described above, may also interact with external device <NUM> to program IMD <NUM>, e.g., select or adjust values for operational parameters of IMD <NUM>. External device <NUM> may include processing circuitry, a memory, a user interface, and communication circuitry capable of transmitting and receiving information to and from IMD <NUM>.

IMD <NUM> and external device <NUM> may communicate via wireless communication using any techniques known in the art. Examples of communication techniques may include, for example, include radiofrequency (RF) telemetry, which may be an RF link established via an antenna according to Bluetooth® or Bluetooth® Low Energy (BLE)®, WiFi, or medical implant communication service (MICS), though other techniques are also contemplated. In some examples, external device <NUM> may include a programming head that may be placed proximate to the patient's body near the IMD <NUM> implant site in order to improve the quality or security of communication between IMD <NUM> and external device <NUM>.

In accordance with the techniques of the disclosure, medical device system <NUM> implements a multi-tier system to determine whether to deliver therapy to patient <NUM>. In one example, IMD <NUM> senses parametric data for patient <NUM>. In some examples, IMD <NUM> senses cardiac electrogram data, such as an electrocardiogram (ECG), from patient <NUM>. IMD <NUM> determines, based on the parametric data, that patient <NUM> is experiencing a treatable event and determines that IMD <NUM> should deliver therapy to patient <NUM>. For example, IMD <NUM> determines that patient <NUM> is experiencing a cardiac arrhythmia event and determines that IMD <NUM> should deliver therapy, e.g., cardiac pacing or shock therapy. In response to detecting the treatable event, IMD <NUM> attempts to establish a wireless connection to external device <NUM>. If IMD <NUM> is unable to establish the wireless connection to external device <NUM>, IMD <NUM> proceeds to deliver therapy to patient <NUM>.

If IMD <NUM> is able to establish the wireless connection to external device <NUM>, then IMD <NUM> transmits at least a portion of the parametric data to external device <NUM>. External device <NUM> reviews the parametric data to confirm whether patient <NUM> is experiencing the treatable event. In some examples, external device <NUM> analyzes a larger portion of the parametric data or different aspects of the parametric data than the IMD so as to increase an accuracy in the determination of whether the patient is experiencing the treatable event. The analysis performed by external device <NUM> may be more sophisticated or more computationally-intensive than IMD <NUM> and thereby provide greater specificity than the analysis by IMD <NUM>. For example, IMD <NUM> may perform simple feature detection to identify the treatable event, whereas external device <NUM> may, e.g., analyze a larger portion of the parametric data than IMD <NUM> and/or apply a machine learning model to the parametric data. In some examples, external device <NUM> performs signal processing of the parametric data, such as a Fourier transform of multiple electrical signals to identify an indicative frequency shift or periodic pattern indicative of a treatable event. In some examples, external device <NUM> analyzes other types of information, such as electronic medical records (EMR) in conjunction with the patient parametric data (e.g. worsening heart failure or kidney failure recorded in a last doctor visit) or symptomatic information provided by a user, such as a clinician or patient, via a user interface.

External device <NUM> selects an instruction for responding to the treatable event based on the verification that the patient is experiencing the treatable event and transmits the instruction to IMD <NUM>. For example, external device <NUM> confirms that the parametric data indicates patient <NUM> is experiencing the treatable event. In response to determining that the patient is experiencing the treatable event, external programmer <NUM> transmits an instruction to IMD <NUM> to proceed with delivery of therapy. However, in other examples, external device <NUM> determines that the parametric data does not indicate that patient <NUM> is experiencing the treatable event. In response to determining that patient <NUM> is not experiencing the treatable event, external programmer <NUM> transmits, to IMD <NUM>, an instruction configured to cause IMD <NUM> to abort delivery of therapy to patient <NUM>. IMD <NUM> receives the instruction from external programmer <NUM> and proceeds accordingly (e.g., by either delivering therapy for the treatable event or aborting delivery of therapy for the treatable event, as is the case). In examples where external programmer <NUM> instructs IMD <NUM> to abort therapy, IMD <NUM> may receive the instruction prior to delivery of therapy and aborts delivery of therapy to patient <NUM> without delivering therapy to patient <NUM>. In other examples where external programmer <NUM> instructs IMD <NUM> to abort therapy, IMD <NUM> may receive the instruction during delivery of therapy and abort delivery of therapy to patient <NUM> during delivering therapy to patient <NUM>. In still other examples, external device <NUM> determines that the parametric data does not indicate that patient <NUM> is experiencing the treatable event but IMD <NUM> is already delivering or has delivered therapy to patient <NUM>, and so forgoes instructing IMD <NUM> to abort delivery of therapy. In this fashion, system <NUM> may act to increase an accuracy in the determination of whether patient <NUM> is experiencing the treatable event without increasing the computational complexity of IMD <NUM> and a commensurate increase in power consumption of IMD <NUM>.

In the foregoing examples, system <NUM> is described as performing arrhythmia classification and determining whether patient <NUM> is experiencing a cardiac arrhythmia event. In such an example, cardiac arrhythmia events may be considered urgent events that require immediate delivery of therapy. For example, IMD <NUM> may determine that patient <NUM> is experiencing a cardiac arrhythmia event and request confirmation of the cardiac arrhythmia event from external device <NUM> within a short time period (e.g., seconds or minutes). In such an example, IMD <NUM> may elect to proceed with delivery of therapy if IMD <NUM> does not receive a response from external device <NUM> within a predetermined amount of time.

In other examples not specifically described herein, system <NUM> may perform detection and classification of other types of treatable events, both urgent and non-urgent. For example, for treatable events that are non-urgent, IMD <NUM> may request confirmation of a treatable event and wait until external device <NUM> confirms the presence of the treatable event prior to taking action. Such treatable events that are non-urgent may include, e.g., treatable events that are of insufficient magnitude to require intervention by IMD <NUM> (e.g., a minor, stable arrythmia that does not require delivery of an ATP shock or defibrillation shock) but may nevertheless warrant notification to a clinician to enable the clinician to adjust the therapy provided by IMD <NUM> (e.g., such as by adjusting one or more parameters defining cardiac pacing therapy) so as to provide more efficacious therapy to patient <NUM>. For example, treatable events that are non-urgent may include a slow ventricular tachyarrhythmia that is hemodynamically stable, an instance of atrial fibrillation, worsening heart failure of the patient over time, or non-cardiac conditions, such as mild instances of hypoglycemia or hyperglycemia. For such non-urgent events, IMD <NUM> may wait minutes, hours, days, etc. so as to allow external device <NUM> to validate the determination of the non-urgent treatable event. The techniques of the disclosure allow IMD <NUM> to be tailored to use different criteria in determining whether to proceed with delivery of therapy upon detecting a treatable event or wait until IMD <NUM> receives confirmation of the treatable event from external device <NUM> according to various "decide and act" or "wait and see" methodologies.

In the example of <FIG>, IMD <NUM> is described as a therapy delivery device. However, in other examples, IMD <NUM> may be a diagnostic device, such as a device that senses parametric patient data and uploads such data to a remote monitoring center for analysis or review by a clinician. In such an example, in response to detecting a treatable event, IMD <NUM> attempts to establish a wireless connection to external device <NUM>. If IMD <NUM> is unable to establish the wireless connection to external device <NUM>, IMD <NUM> proceeds to generate a notification of the occurrence of the treatable event. If IMD <NUM> is able to establish the wireless connection to external device <NUM>, then IMD <NUM> transmits at least a portion of the parametric data to external device <NUM>. External device <NUM> reviews the parametric data to confirm whether patient <NUM> is experiencing the treatable event. If external device <NUM> confirms that the parametric data indicates patient <NUM> is experiencing the treatable event, external programmer <NUM> transmits an instruction to IMD <NUM> to proceed with generation of the notification of the occurrence of the treatable event. In some examples, external programmer <NUM> may itself generate and transmit a notification of the occurrence of the treatable event, e.g., to a patient, clinician, caregiver, or remote monitoring system. However, if external device <NUM> determines that the parametric data does not indicate that patient <NUM> is experiencing the treatable event, external programmer <NUM> transmits, to IMD <NUM>, an instruction configured to cause IMD <NUM> to abort generation of the notification of the occurrence of the treatable event. A clinician or another device may use the notification of the occurrence of the treatable event as a basis to initiate delivery of therapy to patient <NUM>. In this fashion, system <NUM> may act to increase an accuracy in the detection of treatable events as well as the generation of notifications of such treatable events.

Medical device system <NUM> is described as including IMD <NUM> and external device <NUM>. However, the techniques of the disclosure may be used with various other types and combinations of devices other than IMD <NUM> and external device <NUM>. For example, medical device system <NUM> may implement the techniques of the disclosure with a first device and a second device, each of which may be, for example, any combination of implantable or external devices, such as a mobile phone, a "smart" phone, a laptop, a tablet computer, a personal digital assistant (PDA), a wearable electronic device, a handheld computing device, computer workstation, an external programmer, external therapy or monitoring device, or an IMD.

Thus, the techniques disclosed herein may allow for enhanced patient care and increase the accuracy or specificity of treatable event detection in a patient. For example, the techniques disclosed herein enable the efficient use of sophisticated, but more computationally-intensive analysis of the parametric data so as to more accurately determine whether a treatable event has occurred in the patient, thereby increasing the likelihood that therapy delivered by the IMD is appropriate. Furthermore, by using an external device to perform computationally-intensive analysis of the parametric data, an IMD system as described herein may more accurately determine whether a treatable event has occurred in the patient without increasing the power consumption, size, or computational ability of the IMD and enabling the IMD to take advantage of the increased computational ability of the external device.

<FIG> is a conceptual diagram illustrating IMD <NUM> and leads <NUM>, <NUM>, <NUM> of system <NUM> of <FIG> in greater detail. In the illustrated example, bipolar electrodes <NUM> and <NUM> are located adjacent to a distal end of lead <NUM>, and bipolar electrodes <NUM> and <NUM> are located adjacent to a distal end of lead <NUM>. In addition, four electrodes <NUM>, <NUM>, <NUM> and <NUM> are located adjacent to a distal end of lead <NUM>. Lead <NUM> may be referred to as a quadrapolar LV lead. In other examples, lead <NUM> may include more or fewer electrodes. In some examples, LV lead <NUM> comprises segmented electrodes, e.g., in which each of a plurality of longitudinal electrode positions of the lead, such as the positions of electrodes <NUM>, <NUM>, <NUM> and <NUM>, includes a plurality of discrete electrodes arranged at respective circumferential positions around the circumference of lead.

In the illustrated example, electrodes <NUM> and <NUM>-<NUM> take the form of ring electrodes, and electrodes <NUM> and <NUM> may take the form of extendable helix tip electrodes mounted retractably within insulative electrode heads <NUM> and <NUM>, respectively. In some examples, each of electrodes <NUM>, <NUM>, <NUM>-<NUM>, and <NUM> is electrically coupled to a respective conductor within the lead body of its associated lead <NUM>, <NUM>, <NUM> and thereby coupled to circuitry within IMD <NUM>.

In some examples, IMD <NUM> includes one or more housing electrodes, such as housing electrode <NUM> illustrated in <FIG>, which may be formed integrally with an outer surface of hermetically-sealed housing <NUM> of IMD <NUM> or otherwise coupled to housing <NUM>. In some examples, housing electrode <NUM> is defined by an uninsulated portion of an outward facing portion of housing <NUM> of IMD <NUM>. Other divisions between insulated and uninsulated portions of housing <NUM> may be employed to define two or more housing electrodes. In some examples, a housing electrode comprises substantially all of housing <NUM>.

Housing <NUM> encloses signal generation circuitry that generates therapeutic signals, such as cardiac pacing, cardioversion, and defibrillation pulses, as well as sensing circuitry for sensing electrical signals attendant to the depolarization and repolarization of heart <NUM>. Housing <NUM> may also enclose a memory for storing the sensed electrical signals. Housing <NUM> may also enclose a communication circuitry <NUM> for communication between IMD <NUM> and external device <NUM>.

IMD <NUM> senses electrical signals attendant to the depolarization and repolarization of heart <NUM> via electrodes <NUM>, <NUM>, <NUM>, <NUM>-<NUM>, and <NUM>. IMD <NUM> may sense such electrical signals via any bipolar combination of electrodes <NUM>, <NUM>, <NUM>-<NUM>, and <NUM>. Furthermore, any of the electrodes <NUM>, <NUM>, <NUM>-<NUM>, and <NUM> may be used for unipolar sensing in combination with housing electrode <NUM>.

The illustrated numbers and configurations of leads <NUM>, <NUM> and <NUM> and electrodes are merely examples. Other configurations, i.e., number and position of leads and electrodes, are possible. In some examples, system <NUM> may include an additional lead or lead segment having one or more electrodes positioned at different locations in the cardiovascular system for sensing and/or delivering therapy to patient <NUM>. For example, instead of or in addition to intracardiac leads <NUM>, <NUM> and <NUM>, system <NUM> may include one or more epicardial or extravascular (e.g., subcutaneous or substernal) leads not positioned within heart <NUM>.

In accordance with the techniques of the disclosure, IMD <NUM> senses parametric data from patient <NUM>. IMD <NUM> performs a first analysis of the parametric data to determine whether patient <NUM> is experiencing a treatable event. In some examples, IMD <NUM> determines whether patient <NUM> is experiencing cardiac arrhythmia, such as bradycardia, tachycardia, or fibrillation. In some examples, the treatable event is an urgent event that requires immediate delivery of therapy (e.g., within seconds or minutes after detection). In some examples, the treatable event is a non-urgent event that does not require immediate delivery of therapy (e.g., therapy may be delivered within minutes, hours, or days after detection). In some examples, IMD <NUM> performs feature detection on the parametric data to identify the treatable event. In examples where the treatable event is a cardiac arrhythmia event, IMD <NUM> may perform feature detection on cardiac electrogram data IMD <NUM> may compare features of the parametric data to one or more criteria to detect a treatable event.

In some examples, upon determining that patient <NUM> is experiencing a treatable event, IMD <NUM> determines that IMD <NUM> should deliver therapy to patient <NUM> to provide therapy for the treatable event. For example, upon determining that patient <NUM> is experiencing a cardiac arrhythmia event, IMD <NUM> determines that IMD <NUM> should deliver therapy, e.g., cardiac pacing or shock therapy to patient <NUM>. Prior to delivering the therapy, IMD <NUM> attempts to establish a wireless connection to external device <NUM>. If IMD <NUM> is unable to establish the wireless connection to external device <NUM>, IMD <NUM> proceeds to deliver therapy to patient <NUM>.

If IMD <NUM> is able to establish the wireless connection to external device <NUM>, then IMD <NUM> transmits the parametric data to external device <NUM> for a second analysis of the parametric data by external device <NUM> to verify the occurrence of the treatable event. IMD <NUM> may commence delivery of therapy to patient <NUM>. In some examples, IMD <NUM> receives, from external programmer <NUM>, an instruction configured to cause IMD <NUM> to proceed with delivery of therapy to patient <NUM>. In response to receiving the instruction, IMD <NUM> proceeds with delivery of therapy to patient <NUM>. In some examples, IMD <NUM> receives, from external programmer <NUM>, an instruction configured to cause IMD <NUM> to abort delivery of therapy to patient <NUM>. In response to receiving the instruction, IMD <NUM> aborts delivery of therapy to patient <NUM>. In some examples, IMD <NUM> receives the instruction prior to delivery of therapy and aborts delivery of therapy to patient <NUM> without delivering therapy to patient <NUM>. In other examples, IMD <NUM> receives the instruction during delivery of therapy and aborts delivery of therapy to patient <NUM> during delivering therapy to patient <NUM>.

In some examples, IMD <NUM> may continue to sense parametric patent data and extend or continue the first analysis of the parametric patient data while external device <NUM> performs the second analysis. For example, after establishing the wireless connection, IMD 16may analyze a relatively larger amount of parametric data, and/or perform an analysis that requires a relatively longer amount of time to complete. This may allow IMD <NUM> to have higher specificity in classifying treatable events because more data is analyzed. Furthermore, IMD <NUM> may provide additional time to external device <NUM> such that external device <NUM> may have sufficient time to receive the parametric data, analyze the transmitted parametric data, verify the determination by IMD <NUM> to deliver therapy to the patient, and instruct IMD <NUM> whether to proceed with delivery of therapy.

Although described herein in the context of example IMD <NUM> that provides electrical therapy, the techniques disclosed herein may be used with other types of devices. For example, the techniques may be implemented with one or more of an extra-cardiac defibrillator coupled to electrodes outside of the heart or outside of the cardiovascular system, a transcatheter pacemaker configured for implantation within the heart, such as the Micra™ transcatheter pacing system commercially available from Medtronic PLC of Dublin Ireland, an insertable cardiac monitor, such as the Reveal LINQ™ ICM, also commercially available from Medtronic PLC, a neurostimulator, a drug delivery device, a wearable device such as a wearable cardioverter defibrillator, a fitness tracker, or other wearable device, a mobile device, such as a mobile phone, a "smart" phone, a laptop, a tablet computer, a personal digital assistant (PDA), or "smart" apparel such as "smart" glasses or a "smart" watch.

<FIG> is a block diagram of example configuration of IMD <NUM> according to the techniques of the disclosure. In the illustrated example, IMD <NUM> includes processing circuitry <NUM>, memory <NUM>, communication circuitry <NUM>, sensing circuitry <NUM>, therapy delivery circuitry <NUM>, sensors <NUM>, and power source <NUM>. Memory <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 (e.g., performing cardiac arrhythmia detection and delivering therapy, such as anti-tachycardia pacing, bradycardia pacing, and post-shock pacing therapy, etc.). Memory <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 or analog media.

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.

Processing circuitry <NUM> controls therapy delivery circuitry <NUM> to deliver therapy to heart <NUM> according to therapy parameters, which may be stored in memory <NUM>. For example, processing circuitry <NUM> may control therapy delivery circuitry <NUM> to deliver electrical pulses with the amplitudes, pulse widths, frequency, or electrode polarities specified by the therapy parameters. In this manner, therapy delivery circuitry <NUM> may deliver pacing pulses (e.g., ATP pulses, bradycardia pacing pulses, or post-shock pacing therapy) to heart <NUM> via one or more of electrodes <NUM>, <NUM>, <NUM>, <NUM>-<NUM>, and <NUM>. In some examples, therapy delivery circuitry <NUM> may deliver pacing stimulation, e.g., ATP therapy, bradycardia therapy, or post-shock pacing therapy, in the form of voltage or current electrical pulses. In other examples, therapy delivery circuitry <NUM> may deliver one or more of these types of stimulation in the form of other signals, such as sine waves, square waves, or other substantially continuous time signals.

In some examples, processing circuitry <NUM> may implement one or more treatable event detection algorithms 60A stored by memory <NUM> as treatable event detection algorithms 60B (collectively, treatable event detection algorithms <NUM>). Processing circuitry <NUM> applies a treatable event detection algorithm <NUM> to sensed parametric data to determine whether the sensed parametric data is indicative of a treatable event, such as a cardiac arrhythmia. As described in more detail below, each of the treatable event detection algorithms <NUM> may, e.g., analyze parametric data over different lengths of time, require different amounts of computational resources, have higher or lower specificity, etc..

Therapy delivery circuitry <NUM> is electrically coupled to electrodes <NUM>, <NUM>, <NUM>, <NUM>-<NUM>, and <NUM>. In other examples, IMD <NUM> may utilize other numbers of electrodes not depicted in <FIG>. IMD <NUM> may use any combination of electrodes to deliver therapy and/or detect electrical signals from patient <NUM>. In some examples, therapy delivery circuitry <NUM> includes a charging circuit, one or more pulse generators, capacitors, transformers, switching modules, and/or other components capable of generating and/or storing energy to deliver as pacing therapy, cardiac resynchronization therapy, other therapy or a combination of therapies. In some examples, therapy delivery circuitry <NUM> delivers therapy as one or more electrical pulses according to one or more therapy parameter sets defining an amplitude, a frequency, a voltage or current of the therapy, or other parameters of the therapy.

Sensing circuitry <NUM> monitors signals from one or more combinations (also referred to as vectors) of two or more electrodes from among electrodes <NUM>, <NUM>, <NUM>, <NUM>-<NUM>, and <NUM> in order to monitor electrical activity of heart <NUM>, impedance, or other electrical phenomenon. In some examples, sensing circuitry <NUM> includes one or more analog components, digital components or a combination thereof. In some examples, sensing circuitry <NUM> includes one or more sense amplifiers, comparators, filters, rectifiers, threshold detectors, analog-to-digital converters (ADCs) or the like. In some examples, sensing circuitry <NUM> converts sensed signals to digital form and provides the digital signals to processing circuitry <NUM> for processing or analysis. In one example, sensing circuitry <NUM> amplifies signals from electrodes <NUM>, <NUM>, <NUM>, <NUM>-<NUM>, and <NUM> and converts the amplified signals to multi-bit digital signals by an ADC.

In some examples, sensing circuitry <NUM> performs sensing of the cardiac electrogram to determine heart rates or heart rate variability, or to detect arrhythmias (e.g., tachyarrhythmias or bradycardia) or to sense other parameters or events from the cardiac electrogram. Sensing circuitry <NUM> may also include a switching circuitry to select which of the available electrodes (and the electrode polarity) are used to sense the heart activity, depending upon which electrode combination, or electrode vector, is used in the current sensing configuration. Processing circuitry <NUM> may control the switching circuitry to select the electrodes that function as sense electrodes and their polarity. Sensing circuitry <NUM> may include one or more detection channels, each of which may be coupled to a selected electrode configuration for detection of cardiac signals via that electrode configuration. In some examples, sensing circuitry <NUM> compares processed signals to a threshold to detect the existence of atrial or ventricular depolarizations and indicate the existence of the atrial depolarization (e.g., P-waves) or ventricular depolarizations (e.g., R-waves) to processing circuitry <NUM>. Sensing circuitry <NUM> may comprise one or more amplifiers or other circuitry for comparison of the cardiac electrogram amplitude to a threshold, which may be adjustable.

Processing circuitry <NUM> may include a timing and control module, which may be embodied as hardware, firmware, software, or any combination thereof. The timing and control module may comprise a dedicated hardware circuit, such as an ASIC, separate from other processing circuitry <NUM> components, such as a microprocessor, or a software module executed by a component of processing circuitry <NUM>, which may be a microprocessor or ASIC. The timing and control module may implement programmable counters. If IMD <NUM> is configured to generate and deliver bradycardia pacing pulses to heart <NUM>, such counters may control the basic time intervals associated with DDD, VVI, DVI, VDD, AAI, DDI, DDDR, VVIR, DVIR, VDDR, AAIR, DDIR and other modes of pacing.

Memory <NUM> may be configured to store a variety of operational parameters, therapy parameters, sensed and detected data, and any other information related to the therapy and treatment of patient <NUM>. In the example of <FIG>, memory <NUM> may store sensed cardiac EGMs, e.g., associated with detected or predicted arrhythmias, and therapy parameters that define the delivery of therapy provided by therapy delivery circuitry <NUM>. In other examples, memory <NUM> may act as a temporary buffer for storing data until it can be uploaded to external device <NUM> of <FIG>.

Communication circuitry <NUM> includes any suitable circuitry, firmware, software, or any combination thereof for communicating with another device, such as external device <NUM> of <FIG>. For example, communication circuitry <NUM> may include one or more antennae, modulation and demodulation circuitry, filters, amplifiers, or the like for radio frequency communication with other devices, such as external device <NUM>. Under the control of processing circuitry <NUM>, communication circuitry <NUM> may receive downlink telemetry from and send uplink telemetry to external device <NUM> with the aid of an antenna, which may be internal and/or external. Processing circuitry <NUM> may provide the data to be uplinked to external device <NUM> and the control signals for the telemetry circuit within communication circuitry <NUM>, e.g., via an address/data bus. In some examples, communication circuitry <NUM> may provide received data to processing circuitry <NUM> via a multiplexer.

Power source <NUM> may be any type of device that is configured to hold a charge to operate the circuitry of IMD <NUM>. Power source <NUM> may be provided as a rechargeable or non-rechargeable battery. In other example, power source <NUM> may incorporate an energy scavenging system that stores electrical energy from movement of IMD <NUM> within patient <NUM>.

In accordance with the techniques of the disclosure, processing circuitry <NUM> senses, via sensing circuitry <NUM> and/or sensors <NUM>, parametric data from patient <NUM>. Sensors <NUM> may include one or more sensors, such as one or more accelerometers, pressure sensors, optical sensors for O2 saturation, etc. In some examples, the parametric data includes one or more of an activity level of the patient, a heart rate of the patient, a posture of the patient, a cardiac electrogram of the patient, a blood pressure of the patient, accelerometer data for the patient, or other types of parametric data. The activity level may, in some examples, be a summation of activity over a period of time, such as one or more seconds or minutes.

Processing circuitry <NUM> analyzes the parametric data to determine whether patient <NUM> is experiencing a treatable event. In some examples, the treatable event is an urgent event that requires immediate delivery of therapy (e.g., within seconds or minutes after detection). In some examples, the treatable event is a non-urgent event that does not require immediate delivery of therapy (e.g., therapy may be delivered within minutes, hours, or days after detection). In some examples, the treatable event is an occurrence of cardiac arrhythmia, such as bradycardia, tachycardia, or fibrillation. In some examples, processing circuitry <NUM> performs feature detection on the parametric data to identify the treatable event. In some examples, processing circuitry <NUM> performs feature detection on one or more of cardiac electrogram data such as electrocardiogram data, electrode impedance measurements, accelerometer data, temperature data for patient <NUM>, or audio data of a heart of patient <NUM>. In some examples, processing circuitry <NUM> examines criteria related to an RR interval length and/or a frequency of RR intervals, such as a threshold RR length, to detect a cardiac arrhythmia and/or classify the cardiac arrhythmia as a treatable event.

In some examples, upon determining that patient <NUM> is experiencing a treatable event, processing circuitry <NUM> determines that IMD <NUM> should deliver therapy to patient <NUM> to provide therapy for the treatable event. For example, upon determining that patient <NUM> is experiencing a cardiac arrhythmia event, processing circuitry <NUM> determines that IMD <NUM> should deliver therapy, e.g., cardiac pacing or shock therapy to patient <NUM>. Prior to delivering the therapy, processing circuitry <NUM> attempts to establish, via communication circuitry <NUM>, a wireless connection to external device <NUM>. If processing circuitry <NUM> is unable to establish the wireless connection to external device <NUM>, processing circuitry <NUM> proceeds to control therapy delivery circuitry <NUM> to deliver therapy to patient <NUM>.

If processing circuitry <NUM> is able to establish the wireless connection to external device <NUM>, then processing circuitry <NUM> transmits, via communication circuitry <NUM>, at least some of the parametric data to external device <NUM>. Processing circuitry <NUM> may commence delivery of therapy to patient <NUM>. In some examples, processing circuitry <NUM> receives, from external programmer <NUM> via communication circuitry <NUM>, an instruction configured to cause processing circuitry <NUM> to abort delivery of therapy to patient <NUM>. In response to receiving the instruction, processing circuitry <NUM> aborts delivery of therapy to patient <NUM>. In some examples, processing circuitry <NUM> receives the instruction prior to delivery of therapy and aborts delivery of therapy to patient <NUM> without delivering therapy to patient <NUM>. In other examples, processing circuitry <NUM> receives the instruction during delivery of therapy and aborts delivery of therapy to patient <NUM> during delivering therapy to patient <NUM>.

In some examples, if processing circuitry <NUM> is unable to establish the wireless connection, then processing circuitry <NUM> may apply event detection algorithms <NUM> to a relatively smaller amount of parametric data, and/or perform an analysis that requires a relatively shorter amount of time to complete. For example, processing circuitry <NUM> may perform tachyarrhythmia classification on, e.g., <NUM> out of a previously sensed <NUM> heartbeats having an interval no greater than <NUM> milliseconds. In this fashion, processing circuitry <NUM> may classify an event as treatable or not treatable and rapidly determine whether therapy should be delivered.

However, if processing circuitry <NUM> is able to establish the wireless connection, then processing circuitry <NUM> may continue to sense parametric data and apply event detection algorithms <NUM> to the updated parametric data while external device <NUM> performs the second analysis. For example, after establishing the wireless connection, processing circuitry <NUM> may apply event detection algorithms <NUM> to analyze a relatively larger amount of parametric data, and/or perform an analysis that requires a relatively longer amount of time to complete. For example, processing circuitry <NUM> may perform, e.g., tachyarrhythmia classification on, e.g., <NUM> of a previously-sensed <NUM> heartbeats having an interval no greater than <NUM> milliseconds. This may allow IMD <NUM> to have higher specificity in classifying treatable events because more data is analyzed. Furthermore, IMD <NUM> may provide additional time to external device <NUM> such that external device <NUM> may have sufficient time to receive the parametric data, analyze the transmitted parametric data, verify the determination by processing circuitry <NUM> to deliver therapy to the patient, and instruct IMD <NUM> whether to proceed with delivery of therapy.

<FIG> is a block diagram illustrating an example configuration of external device <NUM> that operates in accordance with one or more techniques of the present disclosure. In some examples, external device <NUM> takes the form of an external programmer or mobile device, such as a mobile phone, a "smart" phone, a laptop, a tablet computer, a personal digital assistant (PDA), a wearable electronic device, a handheld computing device, computer workstation, server or other networked computing device, etc. In some examples, external device <NUM> is a CareLink™ monitor available from Medtronic, Inc.

In one example, external device <NUM> includes processing circuitry <NUM> for executing applications <NUM> that include machine learning system <NUM> or any other applications described herein. Although shown in <FIG> as a stand-alone external device <NUM> for purposes of example, external device <NUM> may be any component or system that includes processing circuitry or other suitable computing environment for executing software instructions and, for example, need not necessarily include one or more elements shown in <FIG> (e.g., communication circuitry <NUM>; and in some examples components such as storage device(s) <NUM> may not be co-located or in the same chassis as other components).

As shown in the example of <FIG>, external device <NUM> includes processing circuitry <NUM>, one or more input devices <NUM>, communication circuitry <NUM>, one or more output devices <NUM>, one or more storage devices <NUM>, and user interface (UI) device(s) <NUM>. External device <NUM>, in one example, further includes one or more application(s) <NUM> such as machine learning system <NUM>, and operating system <NUM> that are executable by external device <NUM>. Each of components <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> are coupled (physically, communicatively, and/or operatively) for inter-component communications. In some examples, communication channels <NUM> may include a system bus, a network connection, an inter-process communication data structure, or any other method for communicating data. As one example, components <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> may be coupled by one or more communication channels <NUM>.

Processing circuitry <NUM>, in one example, is 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>. Examples of 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 integrated logic circuitry.

One or more storage devices <NUM> may be configured to store information within external device <NUM> during operation. Storage device <NUM>, in some examples, is described as a computer-readable storage medium. In some examples, storage device <NUM> is a temporary memory, meaning that a primary purpose of storage device <NUM> is not long-term storage. Storage device <NUM>, in some examples, is described as a volatile memory, meaning that storage device <NUM> does not maintain stored contents when the computer is turned off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art. In some examples, storage device <NUM> is used to store program instructions for execution by processing circuitry <NUM>. Storage device <NUM>, in one example, is used by software or applications <NUM> running on external device <NUM> to temporarily store information during program execution.

Storage devices <NUM>, in some examples, also include one or more computer-readable storage media. Storage devices <NUM> may be configured to store larger amounts of information than volatile memory. Storage devices <NUM> may further be configured for long-term storage of information. In some examples, storage devices <NUM> include non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.

External device <NUM>, in some examples, also includes communication circuitry <NUM>. External device <NUM>, in one example, utilizes communication circuitry <NUM> to communicate with external devices, such as IMD <NUM> of <FIG>. Communication circuitry <NUM> may include a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information. Other examples of such network interfaces may include <NUM>, <NUM>, <NUM>, and WiFi radios.

External device <NUM>, in one example, also includes one or more user interface devices <NUM>. User interface devices <NUM>, in some examples, are configured to receive input from a user through tactile, audio, or video feedback. Examples of user interface device(s) <NUM> include a presence-sensitive display, a mouse, a keyboard, a voice responsive system, video camera, microphone or any other type of device for detecting a command from a user. In some examples, a presence-sensitive display includes a touch-sensitive screen.

One or more output devices <NUM> may also be included in external device <NUM>. Output device <NUM>, in some examples, is configured to provide output to a user using tactile, audio, or video stimuli. Output device <NUM>, in one example, includes a presence-sensitive display, a sound card, a video graphics adapter card, or any other type of device for converting a signal into an appropriate form understandable to humans or machines. Additional examples of output device <NUM> include a speaker, a cathode ray tube (CRT) monitor, a liquid crystal display (LCD), or any other type of device that can generate intelligible output to a user.

External device <NUM> may include operating system <NUM>. Operating system <NUM>, in some examples, controls the operation of components of external device <NUM>. For example, operating system <NUM>, in one example, facilitates the communication of one or more applications <NUM> and machine learning system <NUM> with processing circuitry <NUM>, communication circuitry <NUM>, storage device <NUM>, input device <NUM>, user interface devices <NUM>, and output device <NUM>.

Application <NUM> may also include program instructions and/or data that are executable by external device <NUM>. Example application(s) <NUM> executable by external device <NUM> may include machine learning system <NUM>. Other additional applications not shown may alternatively or additionally be included to provide other functionality described herein and are not depicted for the sake of simplicity.

In accordance with the techniques of the disclosure, applications <NUM> include machine learning system <NUM>. In one example, processing circuitry <NUM> executes machine learning system <NUM> to determine whether patient <NUM> of <FIG> is experiencing a treatable event, such as a cardiac arrhythmia event. External device <NUM> may use machine learning system <NUM> to verify a determination by IMD <NUM> of <FIG> that patient <NUM> is experiencing the treatable event.

In one example, machine learning system <NUM> applies a machine learning model to the parametric data for patient <NUM> to determine, e.g., whether or not the parametric data is indicative of patient <NUM> experiencing a cardiac arrhythmia event. In some examples, machine learning system <NUM> further generates a confidence in the determination of whether patient <NUM> is experiencing a cardiac arrhythmia event.

In some examples, the machine learning model is generated by a neural network system, a deep learning system, or other type of supervised or unsupervised machine learning system. For example, the machine learning model may be generated by a feedforward neural network, such as a convolutional neural network, a radial basis function neural network, a recurrent neural network, a modular or associative neural network. In some examples, machine learning system <NUM> trains the machine learning model with parametric data for a plurality of patients to generate the determination of whether patient <NUM> is experiencing a cardiac arrhythmia event. In some examples, after the machine learning model has been pretrained with the parametric data for the plurality of patients, machine learning system <NUM> further trains the machine learning model with parametric data specific to patient <NUM>.

In some examples, machine learning system <NUM> trains the machine learning model with the parametric data for the plurality of patients, determines an error rate of the machine learning model, and then feeds the error rate back to the machine learning model so as to allow the machine learning model to update its predictions based on the error rate. In some examples, machine learning system <NUM> may receive, e.g., from a clinician, feedback indicating whether a cardiac arrhythmia event detected by machine learning system <NUM> actually occurred in patient <NUM>. In some examples, machine learning system <NUM> may receive, from IMD <NUM>, a message indicating that IMD <NUM> has detected (or has not detected) a cardiac arrhythmia event in patient <NUM>. Machine learning system <NUM> may update the machine learning model with the feedback indicating whether the detected cardiac arrhythmia actually occurred in patient <NUM>. Thus, the training process may occur iteratively so as to incrementally improve arrhythmia detection made by the machine learning model by "learning" from correct and incorrect analysis of the parametric data made by the machine learning model in the past. Further, the training process may be used to further fine-tune a machine learning model that is trained using population-based data to provide more accurate predictions for a particular individual (e.g., patient <NUM>).

Once the machine learning model has been trained to detect treatable events from parametric data for patient <NUM> that are of a threshold accuracy selected by the clinician, machine learning system <NUM> may use the machine learning model to provide detection of cardiac arrhythmias of patient <NUM>. For example, machine learning system <NUM>, executed by processing circuitry <NUM>, receives, via communication circuitry <NUM>, parametric data collected by IMD <NUM>. In some examples, machine learning system <NUM> may also receive other parametric data collected by external device <NUM>, such as geographic location, accelerometer data, or input from patient <NUM>. In some examples, the parametric data includes one or more of an activity level of the patient, a heart rate of the patient, a posture of the patient, a cardiac electrogram of the patient, a blood pressure of the patient, accelerometer data for the patient, or other types of parametric data. In other examples, external device <NUM> receives parametric data for patient <NUM> from other devices, such as a wearable medical device, wearable sensors, or a mobile device (e.g., a smartphone) of patient <NUM>.

Machine learning system <NUM> applies the trained machine learning model to the parametric data to detect treatable events occurring in patient <NUM>. In some examples, external device <NUM> uses machine learning system <NUM> to verify the detection of treatable events by IMD <NUM> of <FIG>. For example, the machine learning model converts the parametric data into one or more vectors and tensors (e.g., multi-dimensional arrays) that represent the parametric data. The machine learning model may apply mathematical operations to the one or more vectors and tensors to generate a mathematical representation of the parametric data. The machine learning model may determine different weights that correspond to identified relationships between the parametric data and an occurrence of cardiac arrhythmia. The machine learning model may apply the different weights to the parametric data to generate a determination of whether or not a cardiac arrhythmia event is present in the parametric data and a confidence in the determination.

In the foregoing example, external device <NUM> implements machine learning system <NUM> to determine whether patient <NUM> is experiencing a treatable event. In other examples, external device <NUM> may instead apply feature detection to the parametric data to determine whether patient <NUM> is experiencing a treatable event in a similar fashion as IMD <NUM> applies feature detection to parametric data as described above.

In an example where the treatable event is a cardiac arrhythmia event, processing circuitry <NUM> performs feature detection on parametric data, including electrocardiogram data. In this example, the parametric data includes one or more of an average frequency or an average amplitude of a T-wave of an electrocardiogram of patient <NUM>. Processing circuitry <NUM> receives a raw electrocardiogram signal from IMD <NUM>, and extracts features from the raw electrocardiogram signal. In some examples, processing circuitry <NUM> identifies one or more of T-wave alternans, QRS morphology measures, etc. For example, processing circuitry <NUM> identifies one or more features of a T-wave of an electrocardiogram of patient <NUM> and applies a model to the one or more identified features to determine whether patient <NUM> is experiencing a cardiac arrhythmia event. In some examples, the one or more identified features are one or more amplitudes of the T-wave. In some examples, the one or more identified features are a frequency of the T-wave. In some examples, the one or more identified features include at least an amplitude of the T-wave and a frequency of the T-wave.

While IMD <NUM> may perform relatively less complex algorithms to detect treatable events due to battery and processing power constraints, external device <NUM> may not be so limited. For example, external device <NUM> may be easily charged, a larger battery, or have significantly more computing resources. Therefore, external device <NUM> may apply an algorithm that is more computationally-expensive, algorithmically complex, consumes more power, or analyzes parametric data over a longer period of time than IMD <NUM> (e.g., minutes or hours for external device <NUM> versus seconds or minutes for IMD <NUM>).

In some examples, processing circuitry <NUM> confirms (e.g., via machine learning system <NUM> or via feature detection) that IMD <NUM> has correctly determined that the parametric data indicates a treatable event occurring in patient <NUM>. In one example, processing circuitry <NUM> transmits an instruction to IMD <NUM> to cause IMD <NUM> to proceed with delivery of therapy to patient <NUM>. In another example, processing circuitry <NUM> may take no action so as to allow IMD <NUM> to proceed with delivery of therapy to patient <NUM> according to the internal instructions of IMD <NUM>.

In some examples, processing circuitry <NUM> determines (e.g., via machine learning system <NUM> or via feature detection) that IMD <NUM> has incorrectly determined that the parametric data indicates a treatable event and that no treatable event is occurring within patient <NUM>. In some examples, if processing circuitry <NUM> determines that IMD <NUM> has not yet commenced delivery of therapy to patient <NUM> or is currently delivering therapy to patient <NUM>, processing circuitry <NUM> transmits, via communication circuitry <NUM> and to IMD <NUM>, an instruction configured to cause IMD <NUM> to abort delivery of therapy to patient <NUM>. The instruction may cause IMD <NUM> to abort delivery of therapy to patient <NUM> either prior to delivering therapy or mid-delivery of therapy to patient <NUM>. In some examples, processing circuitry <NUM> determines that the parametric data does not indicate that patient <NUM> is experiencing the treatable event but IMD <NUM> is already delivering or has delivered therapy to patient <NUM>, and so may forego causing IMD <NUM> to abort delivery of therapy. In this fashion, external device <NUM> may provide increased accuracy and specificity to an analysis of parametric data to verify determinations by IMD <NUM> of whether or not patient <NUM> is experiencing treatable events.

<FIG> and <FIG> are flowcharts illustrating an example operation in accordance with the techniques of the disclosure. For convenience, <FIG> is described with respect to IMD <NUM> of <FIG>, and <FIG> is descried with respect to external device <NUM> of <FIG> and <FIG>. In some examples, the operation of <FIG> is an operation for providing multi-tier detection of treatable events, such as cardiac arrhythmia events, in patient <NUM>.

As depicted in the example of <FIG>, processing circuitry <NUM> of IMD <NUM> senses, via sensing circuitry <NUM> and/or sensors <NUM>, parametric data from patient <NUM> (<NUM>). In some examples, the parametric data includes one or more of an activity level of the patient, a heart rate of the patient, a posture of the patient, a cardiac electrogram of the patient, a blood pressure of the patient, accelerometer data for the patient, or other types of parametric data. The activity level may, in some examples, be a summation of activity over a period of time, such as one or more seconds or minutes.

Processing circuitry <NUM> analyzes the parametric data to detect a treatable event (<NUM>). In some examples, processing circuitry <NUM> determines whether the parametric data is indicative of a cardiac arrhythmia event. In response to determining that the patient is experiencing a treatable event, processing circuitry <NUM> attempts to establish, via communication circuitry <NUM>, a wireless connection to external device <NUM> (<NUM>). If processing circuitry <NUM> is unable to establish the wireless connection to external device <NUM> (e.g., "NO" block of <NUM>), processing circuitry <NUM> determines whether to deliver therapy to patient <NUM> (<NUM>). In some examples, processing circuitry <NUM> classifies the treatable event to determine if delivery of therapy is appropriate for the type of treatable event. For example, processing circuitry <NUM> performs feature detection on the parametric data to classify, e.g., a cardiac arrhythmia event as tachycardia or fibrillation. In some examples, processing circuitry <NUM> performs feature detection on one or more of cardiac electrogram data such as electrocardiogram data, electrode impedance measurements, accelerometer data, temperature data for patient <NUM>, or audio data of a heart of patient <NUM>. In some examples, where processing circuitry <NUM> is unable to establish the wireless connection to external device <NUM>, processing circuitry <NUM> may perform a first algorithm of low complexity to classify the cardiac arrhythmia. For example, processing circuitry <NUM> may perform, e.g., tachyarrhythmia classification using, e.g., <NUM> out of a previously sensed <NUM> heartbeats having an interval no greater than <NUM> milliseconds.

In response to determining that IMD <NUM> should delivery therapy (e.g., "YES" block of <NUM>), processing circuitry <NUM> controls therapy delivery circuitry <NUM> to deliver therapy to patient <NUM> (<NUM>). For example, upon determining that patient <NUM> is experiencing a cardiac arrhythmia event, processing circuitry <NUM> controls therapy delivery circuitry <NUM> to deliver therapy, e.g., cardiac pacing or shock therapy to patient <NUM>. In response to determining that IMD <NUM> should not delivery therapy (e.g., "NO" block of <NUM>), processing circuitry <NUM> does not take action and does not deliver therapy to patient <NUM> at the present time (<NUM>).

If processing circuitry <NUM> is able to establish the wireless connection to external device <NUM> (e.g., "YES" block of <NUM>), then processing circuitry <NUM> transmits, via communication circuitry <NUM>, the parametric data to external device <NUM> via the wireless connection (e.g., element "A" of <FIG> and <FIG>) (<NUM>).

Furthermore, after establishing the wireless connection and transmitting the parametric data, processing circuitry <NUM> determines whether to deliver therapy to patient <NUM> (<NUM>). For example, processing circuitry <NUM> classifies the treatable event to determine if delivery of therapy is appropriate for the type of treatable event. In some examples, processing circuitry <NUM> performs feature detection on the parametric data to classify, e.g., a cardiac arrhythmia event as tachycardia or fibrillation. In some examples, processing circuitry <NUM> performs feature detection on one or more of cardiac electrogram data such as electrocardiogram data, electrode impedance measurements, accelerometer data, temperature data for patient <NUM>, or audio data of a heart of patient <NUM>. In some examples, processing circuitry <NUM> transmits, via communication circuitry <NUM>, an indication to external device <NUM> that processing circuitry <NUM> has determined, based on the analysis of the parametric data, that the patient is experiencing a treatable event.

In some examples, where processing circuitry <NUM> is able to establish the wireless connection to external device <NUM>, processing circuitry <NUM> may continue analysis of the parametric data to classify the treatable event. For example, processing circuitry <NUM> may perform, e.g., tachyarrhythmia classification using, e.g., <NUM> of a previously-sensed <NUM> heartbeats having an interval no greater than <NUM> milliseconds. This may allow IMD <NUM> to have higher specificity in classifying treatable events because more data is analyzed. Furthermore, IMD <NUM> may provide additional time to external device <NUM> such that external device <NUM> may have sufficient time to receive the parametric data, analyze the transmitted parametric data, verify the determination by IMD <NUM> to deliver therapy to the patient, and instruct IMD <NUM> whether to proceed with delivery of therapy (as described in <FIG> below).

In response to determining that IMD <NUM> should not delivery therapy (e.g., "NO" block of <NUM>), processing circuitry <NUM> does not take action and does not deliver therapy to patient <NUM> at the present time (<NUM>). In response to determining that IMD <NUM> should delivery therapy (e.g., "YES" block of <NUM>), processing circuitry <NUM> determines whether IMD <NUM> has received an abort instruction from external device <NUM> (e.g., element "B" of <FIG> and <FIG>) (<NUM>). In response to determining that IMD <NUM> has received an abort instruction from external device <NUM> (e.g., "YES" block of <NUM>), processing circuitry <NUM> does not take action and does not deliver therapy to patient <NUM> at the present time (<NUM>). In some examples, the instruction to abort delivery of therapy received from external device <NUM> may override the determination by processing circuitry <NUM> that the cardiac arrhythmia event requires IMD <NUM> to deliver therapy to patient <NUM>.

In response to determining that IMD <NUM> has received an abort instruction from external device <NUM> (e.g., "YES" block of <NUM>), processing circuitry <NUM> controls therapy delivery circuitry <NUM> to deliver therapy to patient <NUM> (<NUM>). For example, upon determining that patient <NUM> is experiencing a cardiac arrhythmia event, processing circuitry <NUM> controls therapy delivery circuitry <NUM> to deliver therapy, e.g., cardiac pacing or shock therapy to patient <NUM>. In some examples, processing circuitry <NUM> receives the instruction to abort delivery of therapy during delivery of therapy. In such an example, processing circuitry <NUM> may abort delivery of therapy to patient <NUM> during delivering therapy to patient <NUM>.

As depicted in the example of <FIG>, external programmer <NUM> receives, from IMD <NUM> via the wireless connection (e.g., element "A" of <FIG> and <FIG>), the parametric data (<NUM>). In some examples, the wireless connection is established according to one of Bluetooth® or BLE®. External programmer <NUM> determines whether a treatable event is present within the parametric data (<NUM>). In some examples, processing circuitry <NUM> of external device <NUM> applies machine learning system <NUM> to the parametric data to determine, e.g., whether or not the parametric data is indicative of patient <NUM> experiencing a cardiac arrhythmia event. In some examples, machine learning system <NUM> further generates a confidence in the determination of whether patient <NUM> is experiencing a cardiac arrhythmia event. In some examples, processing circuitry <NUM> applies feature detection to the parametric data to determine whether patient <NUM> is experiencing the treatable event. In this fashion, external device <NUM> may verify the determination by IMD <NUM> that a treatable event, such as a cardiac arrhythmia event, is present within the parametric data.

In response to confirming that a treatable event is present within the parametric data (e.g., "YES" block of <NUM>), processing circuitry <NUM> confirms that IMD <NUM> has correctly determined that the parametric data indicates a treatable event. Processing circuitry <NUM> may take no action so as to permit IMD <NUM> to deliver therapy to patient <NUM> (<NUM>). In other examples, processing circuitry <NUM> may transmit a message to IMD <NUM> confirming that IMD <NUM> has correctly determined that the parametric data indicates a treatable event such that IMD <NUM> may immediately proceed with delivery of therapy without waiting for a response from external programmer <NUM> to time out.

In response to determining that a treatable event is not present within the parametric data (e.g., "NO" block of <NUM>), processing circuitry <NUM> determines that IMD <NUM> has incorrectly detected a treatable event. In some examples, if processing circuitry <NUM> determines that IMD <NUM> has not yet commenced delivery of therapy to patient <NUM> or is currently delivering therapy to patient <NUM>, processing circuitry <NUM> transmits, via communication circuitry <NUM> and to IMD <NUM> (e.g., element "B" of <FIG> and <FIG>), an instruction configured to cause IMD <NUM> to abort delivery of therapy to patient <NUM> (<NUM>). In some examples, processing circuitry <NUM> determines that the parametric data does not indicate that patient <NUM> is experiencing the treatable event but IMD <NUM> is already delivering or has delivered therapy to patient <NUM>, and so takes no action. In this fashion, external device <NUM> may provide increased accuracy and specificity to an analysis of parametric data to verify determinations by IMD <NUM> of whether or not patient <NUM> is experiencing treatable events.

In some examples, the techniques of the disclosure include a system that comprises means to perform any method described herein. In some examples, the techniques of the disclosure include a computer-readable medium comprising instructions that cause processing circuitry to perform any method described herein.

Claim 1:
A medical device system comprising a first device (<NUM>), the first device comprising:
sensing circuitry (<NUM>) configured to sense parametric data for a patient;
processing circuitry (<NUM>) configured to:
determine, based on a first analysis of the parametric data, that the patient is experiencing a treatable event; and
in response to determining that the patient is experiencing the treatable event, determine whether a second device (<NUM>) is available for wireless communication; and communication circuitry (<NUM>) configured to:
transmit, to the second device, at least a portion of the parametric data for a second analysis of the at least a portion of the parametric data by the second device in response to the determination that the second device is available for wireless communication; and
receive, from the second device, an instruction for responding to the treatable event, the instruction selected based on the second analysis of the at least a portion of the parametric data by the second device.