Patent Description:
Malignant tachyarrhythmia, for example, ventricular fibrillation (VF), 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. Sudden cardiac death (SCD) may result in a matter of minutes.

A wearable automated external defibrillator (WAED), also referred to as a wearable cardiac defibrillator (WCD), is an option for patients having an identified risk of malignant tachyarrhythmia, but for whom an implantable cardioverter-defibrillator is not indicated or desired. WAEDs typically include straps or a garment carrying its components, such as sensing and defibrillation electrodes, processing circuitry, and shock generation and sensing circuitry, which allow such components to be worn by a patient. WAEDs typically implement conventional AED arrhythmia detection algorithms in which seconds of the most recent electrocardiogram data are analyzed, and which do not consider other types of sensor data typically not available to the WAED.

<CIT> and <CIT> relate to a system and method for distinguishing a cardiac event from noise in an electrocardiogram signal.

<CIT> relates to medical premonitory event estimation.

In some aspects, this disclosure describes examples of systems, devices, and techniques for detection of tachyarrhythmia and, in some cases, cardiac defibrillation. A patient may wear a defibrillation apparatus, such as a WAED. The processing circuitry of the apparatus may implement a machine learning algorithm to probabilistically determine a state of a patient. e.g., to determine a tachyarrhythmia state of the patient as part of is tachyarrhythmia detection algorithm and/or a state of a comorbidity, such as chronic obstructive pulmonary disease (COPD). The processing circuitry of the apparatus may have a graphics processing unit (GPU) and central processing unit (CPU) architecture, which may allow the apparatus to implement the machine learning algorithm.

The machine learning algorithm enables the defibrillation apparatus to implement a probabilistic determination, e.g., using a Bayesian, random forest, and/or decision tree methodology, of whether the patient's condition is normal or not normal, e.g., whether the patient is experiencing or will experience a treatable tachyarrhythmia, which may also be referred to as the tachyarrhythmia state of the patient. The machine learning algorithm and the GPU architecture also enable the apparatus to make the probabilistic determination by considering large amounts of diverse data together, and identifying patterns in the data. The data may include present and historical values of (or values derived from) time-varying signals, such as an electrocardiogram (ECG) and/or other sensed physiological or environmental signals.

In some examples, the apparatus receives data including signals, values, or independent determinations of patient state, for consideration by the GPU according to the machine learning algorithm, from one or more other sensing devices, which may be wearable by or implanted in the patient. The apparatus may be the master for such other sensing devices in a master/slave communication relationship. The probabilistic determination based on a variety of types of data and signal sources may allow a more accurate determination of patient state, particularly when confronted with noise in one or more signals. In some examples, the probabilistic determination may allow prediction of tachyarrhythmia prior to its occurrence, and preventative therapy, such as cardiac pacing or other electrical stimulation to disrupt a cardiac rhythm, rather than defibrillation shock in response to fibrillation.

The apparatus, via the GPU, may update a machine learning algorithm based on the collected data. In some examples, e.g., during an initial training phase with the patient's data, the updates to the machine learning algorithm may also be based on feedback from a user or other device regarding whether the algorithm's determinations of the patient's state were correct. Furthermore, in some examples, a remote system may implement a more extensive GPU architecture, e.g., with a greater number of cores or otherwise with a greater ability to process data and update the algorithm. The remote system may receive the collected data and patient state decisions from the apparatus, update its instance of the algorithm based on the data and decisions, and provide the updates to the apparatus. In some examples, different populations may be distinguished from one another based on different characteristics, and the remote system may use population-specific data to update population-specific machine learning algorithms for patients within each population. Additionally, in cases in which a patient wearing such a defibrillation apparatus is later indicated for implantation of an ICD, the tachyarrhythmia detection algorithm of the ICD may implement or be configured based on the machine learning algorithm stored in the defibrillation apparatus, which has learned to detect tachyarrhythmias of the particular patient over time.

In one example, an apparatus configured to be worn by a patient for cardiac defibrillation comprises sensing electrodes configured to sense a cardiac signal of the patient, defibrillation electrodes, therapy delivery circuitry configured to deliver defibrillation therapy to the patient via the defibrillation electrodes, communication circuitry configured to receive data of at least one physiological signal of the patient from at least one sensing device separate from the apparatus, and a memory configured to store the data, the cardiac signal, and a machine learning algorithm. The apparatus further comprises processing circuitry configured to apply the machine learning algorithm to the data and the cardiac signal to probabilistically determine at least one state of the patient, and determine whether to control delivery of the defibrillation therapy based on the at least one probabilistically-determined state of the patient.

In another example, a method for monitoring cardiac signals and determining whether to deliver defibrillation therapy by apparatus configured to be worn by a patient comprises sensing, via sensing electrodes of the apparatus, a cardiac signal of the patient, receiving, by communication circuitry of the apparatus, data of at least one physiological signal of the patient from at least one sensing device separate from the apparatus, storing, by a memory of the apparatus, the cardiac signal, the data, and a machine learning algorithm, applying, by processing circuitry of the apparatus, the machine learning algorithm to the data and the cardiac signal to probabilistically determine at least one state of the patient, and determining, by the processing circuitry, whether to control delivery of defibrillation therapy by therapy delivery circuitry of the apparatus based on the at least one probabilistically-determined state of the patient.

In another example, a system for cardiac defibrillation comprises an apparatus configured to deliver defibrillation therapy, wherein the apparatus is configured to be worn by a patient, wherein the apparatus comprises processing circuitry comprising a first graphics processing unit (GPU), sensing electrodes configured to sense a cardiac signal of the patient:, defibrillation electrodes, therapy delivery circuitry configured to deliver defibrillation therapy to the patient via the defibrillation electrodes, and a memory configured to store the cardiac signal and a machine learning algorithm. The system further comprises a computing system communicatively coupled to the apparatus, the computing system comprising a second GPU, wherein the first GPU is configured to apply the machine learning algorithm to the cardiac signal, and the processing circuitry is configured to determine whether to control delivery of the defibrillation therapy based on a result of the application of the machine learning algorithm to the cardiac signal, and wherein the second GPU is configured to update the machine learning algorithm based on the cardiac signal and population data, wherein the population data comprises data of cardiac signals from a plurality of other patients.

In another example, a system for determining a tachyarrhythmia state of a patient comprises an apparatus configured to be worn by the patient and a sensing device separate from the apparatus. The apparatus comprises sensing electrodes configured to sense a cardiac signal of the patient, communication circuitry configured to receive data of at least one physiological signal of the patient from the sensing device via wireless communication, a memory configured to store the data, the cardiac signal, and a machine learning algorithm, and processing circuitry configured to apply the machine learning algorithm to the data and the cardiac signal to determine the treatable tachyarrhythmia state of the patient. The processing circuitry of the apparatus is configured to request the data as a master from the sensing device as a slave according to a master/slave relationship.

In some aspects, the techniques described herein include collecting data form one or more sources, applying a machine learning algorithm to the data, determining whether to deliver therapy based on a result of the application of the machine learning algorithm to the data and, if it is determined to deliver therapy, delivering the therapy. The machine learning algorithm may be implemented by a GPU of a defibrillation apparatus, such as a WAED. The machine learning algorithm may be updated based on data of the patient and data from a greater population. The machine learning algorithm may be configured to characterize the patient data as normal or non-normal or otherwise determine one or more states of the patient.

As an example, a patient may wear the vest apparatus (e.g., the WAED) having a GPU and also have an insertable cardiac monitor implanted subcutaneously. Each of these devices may sense a cardiac signal of the heart of the patient. The vest apparatus may control the insertable cardiac monitor to perform various tasks, such as transmit sensed signals to the vest apparatus. In other words, the vest apparatus may act as a master for the insertable cardiac monitor in a master/slave relationship.

Based on the signals, the machine learning algorithm implemented by the GPU of the vest apparatus may determine that the patient has an arrhythmia, and the vest apparatus may be configured to deliver therapy to the patient to treat the arrhythmia. The patient (or a healthcare professional) may confirm, e.g., via a user interface of the vest apparatus or another device, whether an adverse event is in fact occurring. In this way, the patient and/or a healthcare provider, may contribute to the development of the machine learning algorithm, e.g., provide reinforcement in near real-time for reinforced learning by the algorithm. In some examples, the cardiac monitor or another device may make an independent determination of the arrhythmia, which may provide near real-time feedback (reinforcement) for reinforced learning development of the machine learning algorithm. The GPU, based on the preceding events and signals, may update the machine learning algorithm.

<FIG> is a conceptual diagram illustrating an example system <NUM> that may be used to deliver therapy to a heart of a patient <NUM>, such as to provide therapy for ventricular fibrillation. System <NUM> may include an apparatus <NUM>, one or more sensing devices <NUM>, a network <NUM>, and one or more external devices <NUM>. Apparatus <NUM>, in an example, may be worn by patient <NUM>. Apparatus <NUM> may be a vest apparatus that includes a garment, electronics, and electrodes, as described further herein. Apparatus <NUM> may be configured to monitor the heart of patient <NUM>, and may configured to provide therapy. In some examples, apparatus <NUM> includes one or more sense electrodes configured to sense a phenomenon (e.g., a physiological signal such as a cardiac signal) of patient <NUM>. In some examples, apparatus <NUM> is a WAED.

Sensing device(s) <NUM> are configured to sense a phenomenon of patient <NUM> and/or the patient's environment. Apparatus <NUM> may be configured to sense the same or different phenomena of patient <NUM> than sensing device(s) <NUM>. As illustrated in <FIG>, apparatus <NUM> and sensing device(s) <NUM> may communicate via one or more links <NUM>. In some examples, links <NUM> may be Bluetooth® links, such as Bluetooth® Low Energy (BLE) links.

In some examples, apparatus <NUM> acts as a master for sensing device(s) <NUM> according to a master/slave relationship. Use of sensing device(s) <NUM> as slaves for apparatus <NUM> may improve the overall sensitivity of the machine algorithm implemented by apparatus <NUM> by, for example, improving true positives while avoiding false positives due to signal interference and false signals. Additionally, communication according to a master/slave relationship may limit the communication-related consumption of a power source of sensing device(s) <NUM> relative to other, more frequent communication schemes. In such a relationship, apparatus <NUM> requests data from sensing device(s) <NUM> only when needed, e.g., when there is a preliminary indication that the patient's state may be not normal based on the cardiac signal (and possibly other data) sensed by apparatus <NUM>.

In some examples, apparatus <NUM> will wake up a sensing device <NUM> using a specified magnetic, radio-frequency (RF), or electrical signal. In some examples, once a connection is established between apparatus <NUM> and sensing device <NUM>, periodic advertisements may maintain the connection. In some examples, organizational or globally unique identifiers may be used by apparatus <NUM> to distinguish among sensing devices <NUM>. In some examples, communication between apparatus <NUM> and sensing devices <NUM> may generally by according to the Bluetooth® or BLE protocols.

Network <NUM> may represent any single network or combination of networks that facilitate communication between devices. As one example, network <NUM> may represent a combination of wireless and wired networks (e.g., the Internet) that facilitate communication between one or more external devices <NUM> and apparatus <NUM> (and/or sensing devices <NUM>). External devices <NUM> and network <NUM> may comprise a remote patient monitoring system, such as the Carelink® network, available from Medtronic plc, of Dublin. In some examples, external device(s) <NUM> comprise one or more servers, and one or more personal computers that a healthcare provider may interact with via a user interface. In some examples, system <NUM> includes multiple external devices <NUM> (e.g., a remote patient monitoring system and one or more personal computers). In some examples, external device(s) <NUM> may comprise a cloud-based computing system.

External device <NUM> may be configured to receive patient data from apparatus <NUM> and/or sensing devices <NUM>, and store the data in memory. External device <NUM> may store data collected from populations of patients. In some examples the population data includes information about patient <NUM>, but in other examples the population data does not necessarily include information about patient <NUM>.

As will be described in greater detail below. apparatus <NUM> may include a GPU that applies a machine learning algorithm to data for patient <NUM> to, for example, determine whether fibrillation is detected and whether to deliver defibrillation therapy. Apparatus <NUM> may deliver defibrillation therapy based on the determination made by the GPU. The data may include data received from sensing device(s) <NUM>. The GPU may update the machine learning algorithm, i.e., the algorithm may learn to better classify future patient data, based on the patient data. External device <NUM> may also include a GPU and determine updates for the machine learning algorithm based on the patient data, and population data, and provide the updates to defibrillation apparatus <NUM>.

<FIG> is a conceptual diagram of system <NUM>, which is one example of a defibrillation apparatus <NUM> and sensing devices <NUM> of system <NUM> of <FIG>. System <NUM> may include apparatus <NUM>, which is one example of apparatus <NUM>. As illustrated. system <NUM> includes sensing devices 230A-230D (collectively, "sensing devices <NUM>"), although in some examples system <NUM> may include fewer, more, or different sensing devices <NUM>. In general, patient <NUM> may be implanted with, wear, or interact with any one or more sensing devices <NUM>. Sensing devices <NUM> are examples of sensing devices <NUM> of <FIG>.

System <NUM> may include a belt <NUM>. In some examples, belt <NUM> is part of apparatus <NUM>, and belt <NUM> is configured to secure portions of apparatus <NUM> to patient <NUM> (e.g., a battery pack connected to electronics of apparatus <NUM>). In general, apparatus <NUM> is configured to be worn by patient <NUM>.

System <NUM> may be configured for cardiac defibrillation. For example, system <NUM> includes a variety of sensors (e.g., of apparatus <NUM> and one or more of sensing devices 230A-230D) configured to sense signals, such as physiological signals of patient <NUM> and characteristics of the patient's environment. For example, apparatus <NUM> may include sense electrodes and associated sensing circuity configured to sense a cardiac signal of patient <NUM>. In some examples, apparatus <NUM> may be a WAED.

A sensing device <NUM>, such as sensing device 230B, may include a sensor (e.g., electrodes and associated sensing circuitry) configured to sense a cardiac signal of patient <NUM>. Therefore, the heart of patient <NUM> may be the source of the signal sensed by apparatus <NUM> and may also be the source of the signal sensed by sensing device 230B. However, due factors like the location on the body of patient <NUM> or the type of sensors used, the cardiac signals sensed by different devices may be slightly different (e.g., with respect to type or degree of noise or motion artifact). As such, by including multiple devices, system <NUM> is configured to determine the state (e.g., normal or not normal) of the heart of patient <NUM> based on multiple signals. In addition, the use of multiple devices to sense signals coming from the same source (e.g., from the heart of patient <NUM> or from other part of patient <NUM>) helps to verify a determination of the patient state. In this way, system <NUM> may have decreased false alarms of non-normal patient states (e.g., a false alarm of a treatable tachyarrhythmia state), which may also be referred to as false positives, relative to systems using only one device. In some examples, false positives due to signal interference and false signals that may be specific to one device or sensing modality may be avoided. Avoiding false alarms may help to avoid delivering unnecessary therapy (e.g., an inappropriate shock). In these and other ways, system <NUM> may be more reliable over systems that rely only on one device to determine the state of patient <NUM>.

Apparatus <NUM> may include communication circuitry configured to receive the sensed signals and/or other data derived from the sensed signal from the sensing devices <NUM>. As used herein, the term data may refer to, as examples, signals, data derived from signals, and determinations made based on signals or other data by any device. In some examples, apparatus <NUM> includes memory configured to store, among other things, the data received from sensing devices <NUM>, the cardiac signal sensed by apparatus <NUM>, and a machine learning algorithm. Apparatus <NUM> comprises processing circuitry, such as described further herein, which may include a GPU. In some examples, the GPU is configured to apply the machine learning algorithm to the data, e.g., to one or more physiological signals received from sensing devices <NUM> and the cardiac signal sensed by apparatus <NUM>. The processing circuitry may be configured to determine whether to control the delivery of defibrillation therapy (e.g.. via therapy delivery circuitry and defibrillation electrodes of apparatus <NUM>) to patient <NUM>. This determination by the processing circuitry, in some examples, is based on a result of the application of the machine learning algorithm to the physiological signal and the cardiac signal. For example, the processing circuitry may be configured to probabilistically determine one or more states of patient <NUM> based on the application of the machine learning algorithm to the data, and control delivery of therapy or take one or more other actions based on the determined patient state(s).

An example of a sensing device <NUM> is sensing device 230A. Sensing device 230A may comprise a headband, hat, or the like configured to be worn on the head of patient <NUM>, and position one or more sensors on the head. Such a sensor may comprise an electroencephalography (EEG) sensor. e.g., electrodes configured to sense and EEG signal and associated sensing circuitry. The sensed EEG signal is one example of the physiological signal described above. For example, apparatus <NUM> may determine a patient state to, for example, determine whether to deliver defibrillation therapy, based on the EEG signal. Different waveform morphology and timing data of the EEG, as learned from the patient and/or population data, may be associated with either supraventricular or ventricular origin of tachyarrhythmia, and thus useful for the determination of the treatable tachyarrhythmia state, e.g., whether or not the patient state is treatable tachyarrhythmia, of the patient by the machine learning algorithm. The EEG signal data considered by the machine learning algorithm may include data derived from a Lorenz plot or another measure of the amount and/or pattern of variability of the EEG signal. In some examples, apparatus <NUM> may diagnose specific cardiac conditions, such as atrial fibrillation (AF) via heart rate variability (HRV), based on the ECG and EEG signal data. Apparatus <NUM> may also determine one or more comorbidity states, such as of epilepsy, or stroke, or other disorders that may be comorbid with cardiac arrhythmia, based on the EEG signal or other data derived from the EEG signal.

Another example of a sensing device <NUM> is sensing device 230B. Sensing device 230B may be a cardiac monitor (e.g., an implantable cardiac monitor). For example, sensing device 230B may take the form of a Reveal LINQ® Insertable Cardiac Monitor (ICM), available from Medtronic plc, of Dublin, Ireland. As described above. apparatus <NUM> may sense a first cardiac signal from the heart of patient <NUM>, while patient device 230B senses a second cardiac signal from the heart of patient <NUM>.

Sensing device 230B may also comprise sensors configured to sense other signals indicative of other physiological phenomena of patient <NUM>. For example, sensing device 230B may be configured to sense temperature, posture, activity, blood oxygenation, and tissue perfusion of patient <NUM>. Further, although described herein primarily as a cardiac monitor, sensing device 230B in other examples may be any implantable medical device (IMD) configured to sense one or more physiological signals of patient and, in some examples, to deliver therapy, such as an implantable pacemaker, ICD, neurostimulator, implantable pressure sensor, or drug delivery device. Apparatus <NUM> may determine a patient state to, for example, determine whether to deliver defibrillation therapy, based on the signals, or other data derived therefrom, sensed by sensing device 230B.

Another example of a sensing device <NUM> is sensing device 230C. Sensing device 230C may be worn on patient <NUM>, such as on an extremity (e.g., the arm or the wrist). Sensing device 230C is a wearable device, such as a watch or activity monitor comprising one or more sensors, for example. Sensing device 230C may be configured to sense a heart rate, activity, and the concentration of various substances in fluids, e.g., salts in blood or perspiration, of patient <NUM>. Apparatus <NUM> may determine a patient state to, for example, determine whether to deliver defibrillation therapy, based on the signals, or other data derived therefrom, sensed by sensing device 230C.

Another example of a sensing device <NUM> is sensing device 230D. Sensing device 230D may be a mobile computing device (e.g., a mobile telephone). As such, sensing device 230D may comprise a microphone, a camera, processing circuitry, and other components that may be used to determine information about or related to patient <NUM>. For example, patient <NUM> may take a picture of food or drink consumed by patient <NUM>. As another example, sensing device 230D may use GPS or other location techniques to track a location of patient <NUM>. System <NUM> may automatically track such data and determine the patient state therefrom. For example, system <NUM> may take into account information regarding the food consumption of patient <NUM>, such as if the patient consumes higher than normal (e.g., higher than a previously determined baseline) amount of a particular type of food (e.g.. electrolytes) that may affect the health of patient <NUM>.

Location data collected by sensing device 230D (or another sensing device <NUM> or apparatus <NUM>) may be used by the systems described herein in a variety of ways. For example, the location data may indicate activities undertaken by the patient and environments or environmental conditions to which the patient is exposed. Apparatus <NUM> may use this data when determining the state of the patient, e.g., with respect to treatable tachyarrhythmia, respiratory disorders (such as COPD), or other comorbidities that may be particularly influenced by environments to which the patient is exposed. Further, the data collected for a given patient may be marked with location data. In some examples, external devices <NUM> (<FIG>) may use location data to demarcate different patient populations for population-based learning by the machine learning algorithm.

Another example of a sensing device <NUM> is a cardiovascular pressure monitoring device. A cardiovascular pressure monitoring device may be an implantable pressure monitoring device, implantable in a chamber of the heart, the pulmonary artery, or another cardiovascular location, such as the pressure monitoring devices described in commonly-assigned <CIT> and <CIT> In other examples, a cardiovascular pressure monitoring device may be an external pressure monitoring device, e.g., including a cuff-based blood pressure measurement system. The cardiovascular pressure signals produced by such sensors may include values of systolic, diastolic, or mean pressures, including pulmonary artery pressure or peripheral vascular pressure, as examples. The pressure signals, e.g., morphology or trends of the signal, may indicate a present or predicted treatable tachyarrhythmia state, or comorbidities, such as heart failure and COPD.

In some examples, one or more sensing devices <NUM> may provide signals or values for pulse rate, oxygen saturation, and respiration rate. In some examples, sensing device comprises an integrated pulse oximetry system providing values or signals for these parameters. The trends or morphology of these parameters may indicate a present or predicted treatable tachyarrhythmia state, or comorbidities, such as heart failure and COPD.

Although primarily described herein as implanted or worn, sensing devices may be any device configured to collect data about the patient or the patient's environment. Another example of a sensing device <NUM> may be a glucose monitor configured to sense a physiological signal of patient <NUM> (e.g., a glucose concentration). Other examples of sensing devices <NUM> include a spirometer, a medical imaging device, a food scale, or an air quality sensing device.

In general, such as described further herein, signals sensed by one or more sensing devices, or other data derived from such signals may be used by machine learning algorithms to determine the state of patient <NUM>. For example, apparatus <NUM> may determine a tachyarrhythmia state of the patient, e.g., whether or not the patient is experiencing or will likely experience a treatable tachyarrhythmia, and whether to deliver defibrillation, or another therapy configured to terminate or prevent tachyarrhythmia, based on the determined state. Apparatus <NUM> may be configured to deliver therapy based on the determinations. In some examples, apparatus <NUM> may be configured to additionally determine whether a comorbidity, such as COPD, has manifested in patient <NUM> based on application of the machine learning algorithm to such data.

<FIG> is a block diagram of an example configuration of a defibrillation apparatus <NUM>, which may be an example of apparatus <NUM> of <FIG> or apparatus <NUM> of <FIG>. Apparatus <NUM> may comprise communication circuitry <NUM>, a memory <NUM>, a user interface <NUM>, processing circuitry <NUM>, a power source <NUM>, sensing circuitry <NUM>, sensing electrodes <NUM>, therapy delivery circuitry <NUM> and defibrillation electrodes <NUM>. Sensing circuitry <NUM> is electrically coupled to sense electrodes <NUM>, and therapy delivery circuitry <NUM> is electrically coupled to defibrillation electrodes <NUM>. Processing circuitry <NUM> may comprise a GPU <NUM> and a CPU <NUM>. Memory <NUM> may be configured to store one or more algorithms <NUM>.

Communication circuitry <NUM> includes any suitable hardware, firmware, software, or any combination thereof for communicating with another device (e.g., any device described herein, such as sensing devices <NUM> of <FIG> or external devices <NUM> of <FIG>). Communication circuitry <NUM> may be configured to transmit or receive radiofrequency ("RF") signals via an antenna (not shown), or other signals via a wired connection with another device or a network. Communication circuitry <NUM> may be configured for such communication with other devices, such as sensing devices <NUM> or <NUM>, or external devices <NUM>. As examples, communication circuitry may include resistors, inductors, capacitors, amplifiers, and/or transistors configured generate, modulate, filter, and/or demodulate signals according to any of a variety of communication protocols. In some examples, communication circuitry <NUM> may be coupled to one or more electrodes, and configured with similar circuitry to transmit and receive signals via the electrodes for tissue conductance communication (TCC).

In general, any device described herein may be configured to communicate with any other device (e.g., any device described herein may comprise communication circuitry like that of communication circuitry <NUM>). In some examples, communication circuitry <NUM> may be configured to communicate via Bluetooth® (e.g., transmit or receive Bluetooth® RF signals). For example, communication circuitry <NUM> may be configured to transmit or receive a BLE radio signal. In some cases, communication circuitry <NUM> comprises a BLE module. In some examples, multiple elements of system <NUM> are connected to one another via Bluetooth® connection.

In some examples, communication circuitry <NUM> comprises input circuitry configured to receive a signal from another device. In some examples, communication circuitry <NUM> comprises output circuitry configured to transmit information from apparatus <NUM> to another device. For instance, communication circuitry <NUM> may comprise circuitry configured to transmit information about a patient state to external device <NUM>.

Communication circuity <NUM> may send information to another device or to a network (e.g., network <NUM> of <FIG>) on a continuous basis, at periodic intervals, or upon request from another device. Communication circuitry <NUM> may also send a command to another device for sending information to apparatus <NUM>. For example, communication circuitry <NUM> may be configured to send a command to a sensing device <NUM> or <NUM> to command the sensing device to transmit a physiological or other signal, or data derived from such a signal, to apparatus <NUM>.

Memory <NUM> may store instructions that cause processing circuitry <NUM> to provide the functionality described herein, and information used by processing circuitry <NUM> to provide the functionality ascribed to apparatus <NUM> as described herein. Memory <NUM> may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a randomaccess memory (RAM), read only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital or analog media. Memory <NUM> may also include a removable memory portion that may be used to provide memory updates or increases in memory capacities. A removable memory may also allow patient data to be easily transferred to another computing device, or to be removed before apparatus <NUM> is used to for another purpose (e.g., upgrade, clean, or resize the garment of apparatus <NUM>).

Memory <NUM> may be configured to store one or more algorithms <NUM>. For example, algorithm <NUM> may comprise a machine learning algorithm. The machine learning algorithm may be configured to determine one or more states of patient <NUM>. In an example, the machine learning algorithm is applied via processing circuitry <NUM>, e.g., GPU <NUM>, to data about patient <NUM>, such as physiological signals or data derived from the signals. The machine learning algorithm may be automatically updated, e.g., continue to learn, based on new data stored in memory <NUM>, via processing circuity <NUM>. In the case of signals, the data may comprise amplitude and temporal information (e.g., an electrocardiogram (ECG) with a measured voltage over time). In general, the data may include values that vary over time, and the machine learning algorithm may consider features of such a signal, or changes in such features over time.

Memory <NUM> may be configured to store data about patient <NUM> or the patient's environment. In some examples, such data is sensed by a sensing device <NUM> or <NUM>. For example, memory <NUM> may be configured to store data about the environment that the patient was in at a particular time, e.g., as indicated by location data, fluid state information (e.g., hydration level or edema), and patient health record information. Memory <NUM> may be configured to store data about patient <NUM> in tables, lists, or graphical formats.

User interface <NUM> may be configured or otherwise operable to receive input from a user, such as patient <NUM> or a healthcare provider. User interface <NUM> may be configured to display information to the user. For example, user interface <NUM> may comprise one or more lights, a display, a motor configured to provide a vibration alert (e.g., similar to "vibrate" mode on a smartphone), a speaker configured to alert the user of an indication. Patient <NUM> may interact with user interface <NUM>, which may include display configured to present graphical user interface to the patient, and a keypad or another mechanism for receiving input from the patient.

In some examples, patient <NUM> or a caregiver interacts with user interface <NUM> to provide information to system <NUM>. For example, patient <NUM> or the caregiver may contribute to the development of the machine learning algorithm by providing feedback about an event. For example, user interface <NUM> may indicate to patient <NUM> that therapy is indicated based on the state of patient <NUM>, but patient <NUM> may override the delivery of therapy (e.g., if patient <NUM> knows that therapy should not be indicated or that the detection is a "false positive"). For example, user interface <NUM> comprises one or more buttons (e.g., digital or physical buttons). Although illustrated in the example of <FIG> as being part of apparatus <NUM>, the user interface through which a patient or another user may provide feedback, also referred to as reinforcement, for reinforced learning by the machine learning algorithm, may be a user interface provided by any one or more computing devices. For example, the user interface may be a user interface of a smart telephone, wearable device (e.g., smart watch), or a dedicated device for communicating with apparatus <NUM>.

The patient or caregiver provides information about the false alarm via the user interface, and the machine learning algorithm may be adjusted based on the new information about the false alarm. The reinforcement from the user may be in near real-time, e.g., as soon as the patient or caregiver reacts to the determination by the machine learning algorithm. Adjustments to the machine learning algorithm may include adjusting weights and/or connections between nodes, as examples.

Processing circuitry <NUM> may be configured to carry out the techniques described herein. Processing circuitry <NUM> may include fixed function processing circuitry or programmable processing circuitry, and may comprise, for example, 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), discrete logic circuitry, or any other processing circuitry configured to provide the functions attributed to processing circuity <NUM> herein may be embodied as firmware, hardware, software or any combination thereof. Processing circuitry <NUM> may control other elements in system <NUM> of <FIG>. In some examples processing circuity <NUM> may control other devices (e.g., patient devices <NUM> or <NUM>).

In some examples, processing circuitry <NUM> includes GPU <NUM>. In general GPU <NUM> may be configured to apply the machine learning algorithm to the data collected by apparatus <NUM>. e.g., via sensing circuitry <NUM> and/or from sensing devices <NUM> or <NUM>, which may include patient signals (e.g., one or more physiological signals from sensing devices and the cardiac signal sensed by sensing circuitry <NUM>). GPU <NUM> may be configured to automatically update the machine learning algorithm, such as based on the collected information, updates determined by external device <NUM> based on population data, determinations made by other devices, such as a sensing device <NUM> or <NUM>, or patient or other user inputs. GPU <NUM> may comprise a plurality of parallel cores, which enable parallel application of data sets including variety of different data from various sources to the machine learning algorithm.

With reference to <FIG>, external device <NUM> may also comprise a GPU. In an example. GPU <NUM> of apparatus <NUM> may comprise a first GPU. The GPU of the external device may comprise a second GPU. Any processing circuitry described herein, including the first and second GPUs, may be configured to perform the techniques attributed to processing circuitry <NUM> or the processing circuitry of the external device, in some examples.

Based on learning from sets of data. e.g., regarding patient <NUM>, the machine learning algorithm may discover patterns in, and relationships, between several independent and interdependent variables derivable from the data. Ongoing consideration of these variables, or other learned variables from continued updating of the algorithm, may allow the machine learning algorithm to probabilistically determine, e.g., classify, diagnose, and/or predict, a state of the patient. The machine learning algorithm may be configured to employ any one or more of Bayesian, random forest, decision tree, linear regression, deep learning, neural network and/or dimensionality reduction techniques, as examples. In some examples, a result of the application of the machine learning algorithm to the data. e.g., one or more physiological signals from sensing devices <NUM> or <NUM> and/or the cardiac signal from sensing circuitry <NUM> includes classification of the data, e.g., the cardiac signal and physiological signal (individually or collectively) as one of normal or not normal, or indicating one or more other states of the patient, such as whether a treatable tachyarrhythmia is indicated or predicted, or whether one or more comorbidities are indicated or predicted.

In some examples, processing circuitry includes CPU <NUM>. In general, CPU <NUM> may be configured to control the activities of the components of apparatus <NUM>, such as communication circuitry <NUM>, user interface <NUM>, sensing circuitry <NUM>. and therapy delivery circuitry <NUM>. CPU <NUM> may also be configured to acquire data for consideration by GPU <NUM>, and control the functionality of GPU <NUM>. In some examples, CPU <NUM> is configured to execute an arrhythmia detection algorithm (e.g., a treatable tachyarrhythmia detection algorithm). The arrhythmia detection algorithm may be stored in memory <NUM>. In some examples, CPU <NUM> is configured to update the tachyarrhythmia detection algorithm based on a result of the application of the machine learning algorithm to the data (e.g., a physiological signal sensed by a patient device and the cardiac signal sensed by the apparatus). CPU <NUM>, for example, is configured to control the delivery of defibrillation therapy (e.g., whether or not defibrillation is delivered) based on the result of the application of the machine learning algorithm to the physiological signal the cardiac signal. In some examples, CPU <NUM> may be configured to control delivery of other therapies by therapy delivery circuitry <NUM> based on the result of the application of the machine learning algorithm to the physiological signal and the cardiac signal, such as therapies configured to prevent a predicted arrhythmia.

Power source <NUM> delivers operating power to various components of apparatus <NUM>. Power source <NUM> may include a small rechargeable or non-rechargeable battery and a power generation circuit to produce the operating power. Recharging may be accomplished through a wired connection to a voltage source, or through proximal inductive interaction between an external charger and an inductive charging coil within apparatus <NUM>. Power source <NUM> may comprise replaceable batteries, in some examples, and apparatus <NUM> may be configured such that patient <NUM> may access the cavity where power source <NUM> is stored to replace the batteries.

In some examples, sensing circuitry <NUM> may be configured to generate a signal. In some examples, sensing circuitry <NUM> comprises amplifiers, filters, analog-to-digital converters, and other circuitry configured to generate and condition signals for receipt by processing circuitry <NUM>. Sensing circuitry <NUM> may be coupled to sense electrodes <NUM>, and may, for example, receive cardiac electrical signals from various combinations of two or more sense electrodes <NUM>. Sensing circuitry <NUM> may be configured to sense cardiac events attendant to depolarization and repolarization of cardiac tissue.

Sensing circuitry <NUM> may include one or more sensing channels, each of which may be selectively coupled to respective combinations of sense electrodes <NUM> to detect electrical activity of the heart. Different sense electrodes <NUM> may be positioned within apparatus <NUM> in a position to effectively measure cardiac signals of patient <NUM>. Each sensing channel may be configured to amplify, filter and rectify the cardiac electrical signal received from selected electrodes coupled to the respective sensing channel to detect cardiac events, such as R-waves, and may also include an analog-to-digital converter to provide a digitized representation of the time-varying signal to processing circuitry <NUM>. Processing circuitry <NUM>, e.g., GPU <NUM>, may determine, from the sensed cardiac signal, whether the cardiac signals indicate a normal or not normal state and, if not normal, whether the cardiac signals indicate one of a plurality of not normal subclassifications, such as bradycardia, treatable tachyarrhythmia, syncope, noise from the signal (e.g., <NUM> Hertz noise), motion artifacts, or loss of signal. In some examples, apparatus <NUM> may include sensors for sensing any of a variety of physiological or other signals described herein in addition to sensing the cardiac signal.

The data set applied to the machine learning algorithm may include a variety of data from a variety of sources that include values that change over time. In the case of signals, the data may comprise amplitude and temporal information, e.g., an electrocardiogram (ECG) with a measured voltage over time. In general, the machine learning algorithm may consider features of a signal formed by changing values over time, or changes in such features over time. For example, in the case of a cardiac signal, the variables may relate to features of the cardiac signal, such as the P-wave, R-wave, QRS-complex, S-T segment, Q-T interval, and T-wave, as well as heart rate and heart rate variability. The variables may be related to the morphology of the signal, such as slope, area under curve, or maximum or minimum amplitude or width of morphological features of the signal. Other variables related to morphology of the signal may include values or features identified by a Fourier or wavelet transform, a turning point algorithm, or other signal transform or decomposition techniques. The variables may include differences over time and/or relative to baselines of such features, rates of change of such features, or differences in such features between different sensing vectors. Signals having features that may be evaluated by the machine learning algorithm in this manner are not limited to a physiological signal, but may instead be values of any measurable or derivable parameter over time, such as R-R interval length or variability, or other parameters derivable from an ECG, that may form a trend-based curve.

Therapy delivery circuitry <NUM> may be coupled to defibrillation electrodes <NUM>. In some examples, therapy delivery circuitry <NUM> is configured to deliver a therapy shock to the heart of patient <NUM>, such as based on a determination that patient <NUM> has a non-normal cardiac state, such as an occurring or predicted treatable tachyarrhythmia. For example, if patient <NUM> has fibrillation or other treatable tachyarrhythmia, apparatus <NUM> may deliver therapy to patient <NUM> via defibrillation electrodes <NUM>. In some examples, therapy delivery circuitry <NUM> may be configured to deliver various types of therapy (e.g., pacing and/or defibrillation, or in some instances, drug therapy). In some examples, the therapy is preventative, such as pacing or drug therapy configured to a treatable tachyarrhythmia predicted by the machine learning algorithm. In examples in which the therapy comprises electrical pulses, e.g., pacing or defibrillation, therapy delivery circuitry <NUM> may include one or more capacitors, charge pumps, current sources, or other signal generation circuitry, as well as switching circuitry to couple the signal to electrodes <NUM>. In examples in which the therapy comprises a drug therapy, therapy delivery circuitry <NUM> may comprises circuitry configured to generate a signal to drive a pump.

<FIG> is a block diagram illustrating an example configuration of certain components of apparatus <NUM>. As illustrated by <FIG>, apparatus <NUM> may comprise various elements connected to one another by a flex material <NUM>. In particular, GPU <NUM> is connected to circuitry <NUM>, and circuitry <NUM> is connected to power source <NUM>, by flex material <NUM>. Circuitry <NUM> may comprise communication circuitry <NUM> and CPU <NUM>, and in some examples sensing circuitry <NUM> and therapy delivery circuitry <NUM> (not shown in <FIG>). In general, the separation of the relatively larger GPU <NUM> and power source <NUM> from the other components of apparatus <NUM>, and the flex material connections, may provide a particular form factor for apparatus <NUM>, e.g., that may be more easily worn by patient <NUM>.

Flex material <NUM> may provide electrical connections between the components of apparatus, e.g., may include conductors running through, on, or between layers of flexible material. In some examples flex material <NUM> comprises a rigid flex electronic substrate construction. Such a construction may enable various form factors for apparatus <NUM>, such as a body band (e.g., wearable band with embedded electronics that may include a shoulder strap), or a headband that has sensors integrated therein.

<FIG> is a block diagram of an example configuration of a sensing device <NUM>, which may be an example of sensing devices <NUM> of <FIG> and/or sensing devices <NUM> of <FIG>. In the illustrated example, and similar as described with respect to sensing device 230B of <FIG>, sensing device <NUM> may take the form of a Reveal LINQ® Insertable Cardiac Monitor (ICM), available from Medtronic plc, of Dublin, Ireland. In other examples, patient device <NUM> may take the form of any of the sensing devices described herein, such as those described with respect to <FIG>, or other types of sensing devices.

<FIG> is a block diagram of external device <NUM>. which may be an example of external device <NUM> of <FIG>. With reference to both <FIG> and <FIG>, the following elements may have at least the same configuration and function as similar elements as described with respect to <FIG>. For example, communication circuitry <NUM> and communication circuitry <NUM> may have the same or similar functionality as communication circuitry <NUM>. For example, memory <NUM> and memory <NUM> may have the same or similar functionality as memory <NUM>. Although one or more algorithms <NUM> are illustrated as stored within memory <NUM>, memory <NUM> may be configured to store other information, such as any information described herein. Similarly, memory <NUM> may be configured to store any information described herein.

Processing circuitry <NUM> and processing circuitry <NUM> may have the same or similar functionality as processing circuitry <NUM>. Processing circuitry <NUM> is illustrated as including GPU <NUM>, which may have the same or similar functionality as GPU <NUM> (except as noted herein, in some examples), and CPU <NUM>, which may have the same or similar functionality as CPU <NUM>. Power source <NUM> and power source <NUM> may have the same or similar functionality as power source <NUM>. User interface <NUM> may have the same or similar functionality as user interface <NUM>, or any other interface described herein.

Sensing circuitry <NUM> and sensing circuitry <NUM> may have the same or similar functionality as sensing circuitry <NUM>. One or more sensors <NUM> and one or more sensors <NUM> may take the form of sensors described, for example, with respect to patient devices <NUM> of the example of <FIG>. In some examples, such a sensor may comprise an accelerometer, an optical sensor, a pressure sensor, an air or blood flow sensor, a temperature sensor, or an air quality sensor. One or more electrodes <NUM> may have the same or similar functionality as sense electrodes <NUM>, e.g., sensing circuitry <NUM> may be configured to sense a physiological signal of patient <NUM>, such as a cardiac signal, via the electrodes. In some examples, the cardiac signal comprises information, such as about an R-R interval, an amplitude of an R-wave, a QRS width, or an R-R interval variability. In an example, inputs to the machine learning algorithm include such data from the cardiac signal.

In some examples, external device <NUM> and/or sensing device <NUM> may be configured to detect information about the environment of patient <NUM>. For example, external device <NUM> and sensing device <NUM> may be configured to determine an air temperature and/quality in the environment patient <NUM> is in.

In some examples, memory <NUM> of sensing device is configured to store, and processing circuitry <NUM> execute, a tachyarrhythmia detection algorithm. The processing circuitry <NUM> may store in memory <NUM> indications of whether tachyarrhythmia was detected at various times. Processing circuitry <NUM> of sensing device <NUM> may provide such indications to apparatus <NUM> via communication circuitry <NUM>. As described herein, GPU <NUM> of apparatus <NUM> may use these indications of the determination of tachyarrhythmia by sensing device <NUM> as inputs for the machine learning algorithm, for assistance in classifying a current patient state, and/or as feedback for adaptation of the machine learning algorithm.

Rather than continuous or periodic communication, apparatus <NUM> and sensing device <NUM> may communicate as master and slave, respectively, in a master/slave relationship. Communication according to a master/slave relationship may limit the communication-related consumption of power source <NUM> of sensing device <NUM> relative to other, more frequent communication schemes. In such a relationship, apparatus <NUM> requests data from sensing device <NUM> for use by machine learning algorithm only when needed, e.g., when there is a preliminary indication that the patient's state may be not normal based on the cardiac signal (and possibly other data) sensed by apparatus <NUM>.

Algorithms <NUM> stored in memory <NUM> of external device <NUM> (as illustrated in <FIG>) may comprise one or more machine learning algorithms, and GPU <NUM> may be configured to implement the machine learning algorithms. In some examples. GPU <NUM> is more computationally capable then GPU <NUM> of apparatus <NUM>. The greater computational capability may be due to a variety of factors, such as number of cores or clock speed. Size of processing circuitry, controlling power consumption, and heat may be of lesser concern in the case of external device <NUM> then apparatus <NUM>, particularly where apparatus <NUM> is wearable by patient <NUM>.

GPU <NUM> may be configured to implement a relatively more computationally intense version of the machine learning algorithm than GPU <NUM>, which may implement a "lite" version of the machine learning algorithm. In some examples, GPU <NUM> updates the machine learning algorithm differently than GPU <NUM>. For example, GPU <NUM> may update the machine learning algorithm based on population data and, in doing so, update the machine learning algorithm based on a significantly greater number of data sets than GPU <NUM>. CPU <NUM> may control communication circuitry <NUM> to send the determined updates to apparatus <NUM>, and GPU <NUM> may update its instance of the machine learning algorithm as indicated by external device <NUM>.

The machine learning algorithm implemented by GPU <NUM> of apparatus <NUM> may be updated in a variety of ways. For example, GPU <NUM> may automatically update the machine learning algorithm based on the data it receives and considers. In some cases, the learning may be reinforced by feedback, such as from a user or an independent determination from another device, e.g., sensing device <NUM>. Additionally. GPU <NUM> may update the machine learning algorithm as indicated by the updates determined by GPU <NUM> when considering population data. In this manner, the machine learning algorithm implemented by GPU <NUM> is updated based on population data. In some examples, the population-based updates to the machine learning algorithm may include new weights on variables applied to normal variance factors for different populations. In general, the updates may also include updates to the graph structure of the algorithm, or updates to the parameters/variables considered by the algorithm, the latter of which may be more intense and determined by GPU <NUM>.

CPU <NUM> may receive, via communication circuitry <NUM> data sets from a number of different apparatuses <NUM> of a number of different patients <NUM>. CPU <NUM> may also receive or otherwise determine information indicate into which patient population(s) the received data should be sorted. GPU <NUM> may implement a number of population-specific machine learning algorithms, and apply the data for a given population to the algorithm for the population in order to determine updates for the machine learning algorithms of apparatuses <NUM> of patients in those populations.

Different populations may have different characteristics. For example, each population may be defined by a unique combination of values for a plurality of characteristics that distinguish the populations. Example characteristics include age, gender, location, body mass index (BMI), weight, blood pressure (e.g., ranges such as low, ideal, pre-high or borderline high, and high), respiration, glucose level, history of adverse medical events, and presence of other comorbid medical conditions.

The normal and/or not normal ranges for various parameters or variables that are inputs to the machine learning algorithm may vary between different patient populations. For example, the resting heart rate for bradycardia is expected to increase with age, and the resting normal heart rate is expected increase with BMI and blood pressure. Adapting the machine learning algorithm based on data sets of patients with characteristics similar to each other may increase the ability of the algorithm to correctly characterize the state of a given patient within the population.

<FIG> is a flowchart illustrating an example technique that may be implemented by a defibrillation apparatus <NUM> to apply a machine learning algorithm to data and determine whether to deliver therapy. For example, apparatus <NUM> receives data (<NUM>). The data includes a cardiac signal sensed by apparatus <NUM>. The data may also include data from sensing devices, such as another cardiac or other physiological signal, or data derived therefrom.

Apparatus <NUM> applies a machine learning algorithm to the data to characterize the data, and thus one or more states of the patient, as normal or not normal (<NUM>). In some examples, the machine learning algorithm may indicate whether a treatable tachyarrhythmia state is not normal. e.g., indicating the presence of fibrillation or another shockable tachyarrhythmia. In some examples, the machine learning algorithm may additionally or alternatively indicate whether the state for one or more other conditions, e.g., comorbidities, of the patient is normal or not normal, such as whether an episode or worsening of COPD or another comorbidity is present or predicted. Apparatus <NUM> decides whether or not to provide a therapy and, in some cases, which of a plurality of therapies to apply, based on the characterization, e.g., in response to certain not normal characterizations (<NUM>). The therapy may be defibrillation shock. If the characterization is predicted treatable tachyarrhythmia, the therapy may be cardiac pacing or another therapy configured to prevent a predicted tachyarrhythmia.

Apparatus <NUM> also updates the machine learning algorithm based on the considered data (<NUM>). For example, a GPU <NUM> may update the machine learning algorithm autonomously and/or based on feedback from a user or another device, e.g., sensing device <NUM>. As described herein, GPU <NUM> may also update the machine learning algorithm based on population data, e.g., according to population-based updates received from external device <NUM>. Population-based updates may occur less frequently than autonomous and reinforced updates.

<FIG> is a flowchart illustrating an example technique that may be implemented by a defibrillation apparatus <NUM> (referred to as the WAED in the figure) to apply a machine learning algorithm to data, including data received from one or more sensing devices <NUM>, and determine whether to deliver therapy. According to the example of <FIG>, GPU <NUM> applies the machine learning algorithm to the cardiac signal sensed by sensing circuitry <NUM> via electrodes <NUM> (<NUM>). Based on the application of the machine learning algorithm to the cardiac signal, GPU <NUM> determines whether a preliminary characterization of the patient state is normal (<NUM>). If the preliminary characterization is normal (YES of <NUM>), GPU <NUM> continues to apply the algorithm to a new data set derived from the cardiac signal sensed by apparatus <NUM> (<NUM>).

If the preliminary characterization is not normal (NO of <NUM>), CPU <NUM> receives data from one or more sensing devices <NUM> via communication circuitry <NUM> (<NUM>). For example, apparatus <NUM>, acting as a master in a master/slave relationship, may initiate a communication session with one or more sensing devices <NUM> to command the sensing devices to provide the data. The data may include a cardiac signal or other physiological or environmental signal sensed by the sensing device, data derived therefrom, and/or determinations made by the sensing device based on the signals/data.

GPU <NUM> applies the machine learning algorithm to the cardiac signal and the data from sensing device(s) <NUM> (<NUM>). Based on this application of the algorithm, GPU <NUM> characterizes the state, e.g., the treatable tachyarrhythmia state as normal or not normal (<NUM>). CPU <NUM> determines whether to provide a therapy to patient <NUM> based on the characterization (<NUM>). If CPU <NUM> determines that therapy should be delivered (YES of <NUM>), CPU <NUM> may control therapy delivery circuitry <NUM> to deliver therapy. e.g., pacing or defibrillation via electrodes <NUM> (<NUM>). In some examples, CPU <NUM> may take other actions based on the characterization, such as changing a sensing vector, or setting of an adjustable filter or amplifier to ameliorate a source of noise based on characterization of the signal as noisy. Although not illustrated in <FIG>, GPU <NUM> may update the machine learning algorithm, e.g., based on the characterization of the data and, in some cases, feedback received from users or other devices regarding the characterization of the data, such as whether the patient canceled the delivery of therapy.

Although the examples of <FIG> and <FIG> include delivery of therapy, e.g., a defibrillation shock, in response to a not normal characterization, therapy is not necessarily delivered in all examples according to the techniques of this disclosure. In some examples, the machine learning algorithm makes determinations of the state of the patient without apparatus responsively delivering therapy. Such determinations may be recorded in memory of the apparatus, transmitted to an external device via a network, and/or presented to a user via a user interface. Determinations of patient state without therapy delivery may occur, for example, during a reinforced learning phase of the machine learning algorithm.

<FIG> is a flowchart illustrating an example technique that may be implemented by a defibrillation apparatus <NUM> to apply a machine learning algorithm to data to characterize a patient state as normal or not normal. According to the example of <FIG>, GPU <NUM> applies the machine learning algorithm to data (<NUM>), and determines whether the data indicates a normal state (<NUM>). The machine learning algorithm may employ Bayesian, random forest, decision tree, linear regression, deep learning, neural network and/or dimensionality reduction techniques, as examples. In some examples, the machine learning algorithm employs a Bayes network with different weights for different factors (e.g., variables) derivable from the ECG and other data. In some examples, the spectrums of values represented by heat maps, such as those described with respect to <FIG>, may be used to relate the weights to some output. If the data indicates a normal state (YES of <NUM>). GPU <NUM> returns a normal characterization. e.g., to CPU <NUM> (<NUM>). In some examples, the machine learning algorithm is probabilistic, and configured to indicate a not normal state if the probability of the normal state is below a certain percentage, such as <NUM>%, <NUM>%, <NUM>%, or any range between any of these values. These values are examples, and other, e.g., lower, percentages may be used in some examples. Sensitivity and specificity of the machine learning algorithm may increase over time as the algorithm learns based on how many learning cycles and the size of the population from which data for learning is derived. In some examples, the probability may initially have a first value, such as <NUM>%, but increase to another value, such as <NUM>% or more, over time.

Example variables that may be derived from ECG data and considered by the machine learning algorithm include R-wave amplitude, R-R interval length, R-R interval variability, QRS width, or the slope, area under curve, or other morphological features of a signal formed from values of R-wave amplitude, R-R interval length, R-R interval variability, or QRS width over time. In some examples, a rolling window (e.g., of three minutes or some other length) of values of each variable is considered. In some examples, a window of normal training set data for a variable will have values that satisfy a boundary condition at least a threshold amount (e.g., percentage) of time during the window, such as <NUM>%. Examples of normal and not normal data sets and associated boundary conditions for different variables are discussed below with respect to <FIG>.

In some examples, if the characterization is not normal (NO of <NUM>), the algorithm considers the data (and in some cases additional data) to determine a not normal sub-characterization (<NUM>). The algorithm structure for determining the not normal sub-characterization may be a fault decision tree. Possible not normal sub-characterizations include bradycardia, treatable tachyarrhythmia, non-treatable tachyarrhythmia (e.g., atrial fibrillation or another supra-ventricular tachyarrhythmia), syncope, <NUM> Hertz noise, motion artifacts, loss of signal, electrode peeling, or high R-R interval variability, e.g., due to atrial fibrillation.

<FIG> is a flowchart illustrating an example technique that may be implemented by an external device <NUM> to determine updates for machine learning algorithms for different populations based on population data. According to the example of <FIG>, external device <NUM> receives data from a plurality of different apparatuses of a plurality of different patients (<NUM>). External device <NUM>, e.g., CPU <NUM>, sorts that data into a plurality of different patient populations (<NUM>). As described herein, different patient populations may be distinguished from each other based on one or more of age, gender, location, obesity classification, weight or body mass index (BMI), blood pressure, respiration, glucose level, medical history, presence of other comorbid medical conditions, such as COPD or diabetes, type and/or model of defibrillation apparatus, and type(s) and/or model(s) of sensing devices available. Based on the sorted data, GPU <NUM> determines updates for different machine learning algorithms for the different patient populations (<NUM>). As described herein, updates to the machine learning algorithm may include new weights on variables, updates to the graph structure of the algorithm, or updates to the parameters/variables considered by the algorithm, as examples. External device <NUM>, e.g., CPU <NUM> and communication circuitry <NUM>. communicate the updates for each population to the various apparatuses in that population (<NUM>). The updates may be provided to the defibrillation apparatus via any wired or wireless connection, e.g., via the Internet, and in some cases via one of the sensing (slave) devices acting as an intermediary.

<FIG> is a flowchart illustrating different learning phases of a machine learning algorithm. Initially, the machine learning algorithm may be designed and trained, e.g., using one or more external devices <NUM>. The initial training may include application of numerous validated data sets to the algorithm, e.g., data sets validated by a domain expert as having a particular classification (<NUM>). In some examples, the validated data sets are data sets from a population having particular characteristics, and the machine learning algorithm is intended for use in an apparatus for patient within that population.

When apparatus <NUM> is in use by patient, the machine learning algorithm may undergo a period of reinforced learning (<NUM>). During reinforced learning, apparatus <NUM> may receive feedback from a user via user interface <NUM> and/or from another device (e.g., sensing device <NUM>) regarding whether the determination of state by the machine learning algorithm was correct. In some examples, new data (including new patterns in, and relationships, between variables) available from the patient (or a population of similar patients) may reviewed and classified by one or more physicians for additional reinforced learning by the machine learning algorithm. After some time, e.g., after reinforced learning has achieved a desired level of sensitivity and specificity in the algorithm, manual intervention may no longer be required. The machine learning algorithm may continue autonomous learning (<NUM>).

<FIG> is a flowchart illustrating an example technique for configuring a tachyarrhythmia detection algorithm of an implantable cardioverter defibrillator (ICD) for a patient based on data previously evaluated by a machine learning algorithm of a defibrillation apparatus <NUM> of the patient. The example technique of <FIG> may be performed by external device <NUM> and/or one or more other computing devices.

According to the example of <FIG>, patient <NUM> is indicated for implantation of an ICD, at which time patient <NUM> will likely stop using apparatus <NUM> (<NUM>). External device <NUM> retrieves data sets characterized as not normal and, in some cases, tachyarrhythmia, by the machine learning algorithm of apparatus <NUM> of patient <NUM> (<NUM>). External device <NUM> processes the data sets to identify features in the data that are detectable by the tachyarrhythmia detection algorithm of the ICD (<NUM>). The tachyarrhythmia algorithm of the ICD may not include a GPU or machine learning algorithm, and may be not be able to detect tachyarrhythmia in the same manner as apparatus <NUM>. As an example of identification of features the data sets detectable by the ICD. external device <NUM> may partition an ECG waveform (or sequence or signal of variable values derived from the ECG waveform) of a certain length from a data set classified by apparatus <NUM> into a plurality of shorter sequential waveforms or smaller sequential sets of values of a variable. External device <NUM> may identify features in the sequential waveforms or value sets that the ICD may detect in sequence (rather than in parallel as may have been done by GPU <NUM>) to detect a tachyarrhythmia. External device <NUM> configures the ICD tachyarrhythmia detection algorithm based on the identified features (<NUM>).

<FIG> is a diagram illustrating an example technique for parsing data evaluated by a machine learning algorithm to configure a tachyarrhythmia detection algorithm of an implantable cardioverter defibrillator. In particular, <FIG> illustrates a heat map of two minutes of consecutive R-wave amplitude values. In the heat map, the R-wave amplitude values are binned within the <NUM> minute interval in which the R-wave occurred, and within one of ten <NUM> mV amplitude ranges between <NUM> and <NUM> mV. In this example heat map, each <NUM> minute interval included six R-waves and, accordingly, six R-wave amplitude values.

The R-wave amplitude values in the heat map of <FIG> may represent a set of consecutive R-wave amplitude values that would be characterized by a machine learning algorithm as not normal - particularly the sequence of R-wave amplitude values in region <NUM> of the heat map where the amplitudes were more variable. Although data considered by a machine learning algorithm is presented herein the in the form of heat maps, consideration of data by a machine learning algorithm according to the techniques described herein does not necessarily involve conversion of the data into a heat map format. Rather, data is presented in heat map format for ease of illustration of certain features of the data.

As illustrated by <FIG>, processing circuitry, e.g., processing circuitry <NUM> of external device <NUM>, may parse the consecutive amplitude values represented by region <NUM> into shorter segments represented by bins <NUM>-<NUM>. The processing circuitry may further determine a feature of the data identifiable by the ICD based on the parsed data segments. For example, based on the data represented by bins <NUM>-<NUM>, the processing circuitry may determine a threshold level of R-wave amplitude variability above which a not normal state. e.g., a treatable tachyarrhythmia state, is indicated or predicted. The threshold level of variability determined from the segments represented by bins <NUM>-<NUM> may, as examples, be a threshold amount of variability over a particular period of time, a threshold amount of variability in each of N consecutive time periods, and/or a threshold amount of variability in M of N consecutive time periods. The threshold level of variability may be programmed into the ICD for the particular patient and used by the ICD, e.g., with other tachyarrhythmia detection or discrimination techniques, to identify treatable tachyarrhythmia.

Processing circuitry. processing circuitry <NUM> of apparatus <NUM>, may use an inverse of the parsing technique illustrated by <FIG> to merge together shorter segments of data, e.g., from sensing devices <NUM>, into a larger data set, e.g., a trend-based curve for application to the machine learning algorithm. In some examples, the length of a segment of consecutive data values stored by sensing devices <NUM> and transmitted to apparatus <NUM> may be limited, e.g., due to memory, communication, or other capabilities of the sensing devices <NUM>. When apparatus <NUM> receives data from sensing devices <NUM>, e.g., as requested according to the master-slave relationship, the shorter segments may be timestamped, and the processing circuitry may combine the shorter segments in order to form a larger data set for application to the machine learning algorithm. In some examples, any known data padding techniques may be applied to the data received from sensing device <NUM> to produce a data set having sufficient values for consideration by the machine learning algorithm.

<FIG> is a flowchart illustrating an example technique for applying a machine learning algorithm of a defibrillation apparatus <NUM> to data to determine whether to provide an indication of chronic obstructive pulmonary disease. Although apparatus <NUM> may primarily function to detect and treat tachyarrhythmia, GPU <NUM>, the ability to collect diverse data from sensing devices <NUM>, and the ability to adapt the machine learning algorithm based on population data collected by external device <NUM> may make apparatus <NUM> a platform to additionally or alternatively determine whether patient <NUM> is experiencing a variety of comorbid conditions or disorders. One example comorbidity that may be evaluated by apparatus <NUM> is COPD.

According to the example of <FIG>, apparatus <NUM> receives spirometer data and environmental data from one or more sensing devices <NUM>, e.g., a spirometer and an environmental sensor (<NUM>). GPU <NUM> applies the machine learning algorithm (or distinct portion thereof for evaluating COPD) to the data (<NUM>). Example data that may be evaluated for COPD is described below with respect to <FIG>. The machine learning algorithm characterizes the data (<NUM>). In some examples, GPU <NUM> compares each of the spirometer and environmental data to one or more thresholds, and determines a COPD state of the patient using a decision tree based on whether a threshold, or particular combinations of thresholds for different data, are met. CPU <NUM> determines whether to provide an indication of COPD for patient <NUM>, e.g., via user interface <NUM> or to external device <NUM> via a network, based on the characterization (<NUM>). GPU <NUM> updates the machine learning algorithm based on the data and the characterization (<NUM>).

<FIG> is a diagram illustrating data from a cardiac signal that a machine learning algorithm would learn to classify as normal. e.g., the treatable tachyarrhythmia state is normal or not treatable tachyarrhythmia, according to an example of the techniques of this disclosure. More particularly, <FIG> is a heat map plot of occurrences of combinations of R-R interval and R-wave amplitude in a normal data set, with each beat binned within one of the <NUM> mV amplitude bins between <NUM> and <NUM> mV and one of the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds. Region <NUM> illustrates the distribution of such values in a normal data set. In particular, region <NUM> includes consistent R-wave amplitudes within a <NUM> mV range including <NUM> mV, and beats distributed as shown in within the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds, with the greatest number of beats having R-R interval lengths between about <NUM> and <NUM> seconds. R-wave amplitude, R-R interval. R-wave amplitude as a function of R-R interval, and a distribution or variability of R-wave amplitude as a function of R-R interval are examples of variables that may be considered by a machine learning algorithm to characterize data for a patient.

<FIG> are diagrams illustrating data from cardiac signals that a machine learning algorithm would learn to classify as not normal according to examples of the techniques of this disclosure. For example, <FIG> is a heat map plot of occurrences of combinations of R-R interval and R-wave amplitude in a not normal data set and, more particularly, a data set that would be classified as bradycardia, with each beat binned within one of the <NUM> mV amplitude bins between <NUM> and <NUM> mV and one of the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds. Region <NUM> illustrates the distribution of such values in bradycardia set. In particular, region <NUM> includes some variation in R-wave amplitudes, and beats distributed as shown in within the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds, with the greatest number of beats having R-R interval lengths between about <NUM> and <NUM> seconds. The pattern of R-R interval and R-wave amplitude value combinations associated with bradycardia may, as illustrated by region <NUM>, be dispersed or checkered and shifted towards larger R-R interval values (e.g., right in <FIG>) relative to the normal data set illustrated by <FIG>.

As another example, <FIG> is a heat map plot of occurrences of combinations of R-R interval and R-wave amplitude in a not normal data set and, more particularly, a data set that would be classified as tachyarrhythmia, e.g., treatable tachyarrhythmia, with each beat binned within one of the <NUM> mV amplitude bins between <NUM> and <NUM> mV and one of the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds. Region <NUM> illustrates the distribution of such values in a tachyarrhythmia data set. In particular, region <NUM> includes significant variation in R-wave amplitudes between <NUM> and <NUM> mV, particularly at shorter R-R interval lengths, and a significant number of beats with R-R interval lengths between <NUM> and <NUM> seconds, in contrast to the normal data set of <FIG> with most beats having R-R interval lengths greater than <NUM> seconds. The pattern illustrated by <FIG> may be described as including a significant grouping of beats having shorter than normal R-R interval lengths and higher than normal R-wave amplitude variability, e.g., on the left side of the heat map of <FIG>.

Although <FIG> illustrates a data set that would be classified as tachyarrhythmia generally, data sets may have distinguishing characteristics, e.g., evident in heat maps, that would allow classification of particular types of treatable or not treatable tachyarrhythmias, such as ventricular fibrillation, ventricular tachycardia, or supra-ventricular tachycardia. Different patterns of groupings of beats for such different tachyarrhythmias may be evident, for example, in heat maps that plot discrete variables derived from the ECG, such as R-R interval length and R-wave amplitude, relative to waveform morphological characteristics, such as slope, area under curve, or values from a transform or turning point algorithm.

As another example, <FIG> is a heat map plot of occurrences of combinations of R-R interval and R-wave amplitude in a not normal data set and, more particularly, a data set that would be classified as syncope, which is characterized by sudden rate and amplitude variation followed by a flat line in the cardiac signal. In the example of <FIG>, each beat is binned within one of the <NUM> mV amplitude bins between <NUM> and <NUM> mV and one of the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds. Region <NUM> illustrates the distribution of such values in a syncope data set. In particular, region <NUM> includes significant variation in R-wave amplitudes, and two distinct clusters of beats respectively having R-R interval lengths between <NUM> and <NUM> seconds and greater than <NUM> seconds. The syncopal beats with significantly longer R-R interval lengths also have lower R-wave amplitudes.

As another example, <FIG> is a heat map plot of occurrences of combinations of R-R interval and R-wave amplitude in a not normal data set and, more particularly, a data set that would be classified as <NUM> Hertz noise, which is characterized by a slight sine wave like variation in R-wave amplitude. In the example of <FIG>. each beat is binned within one of the <NUM> mV amplitude bins between <NUM> and <NUM> mV and one of the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds. Region <NUM> illustrates the distribution of such values in a <NUM> Hertz noise data set, which is characterized by a pattern of greater R-wave amplitude variability than the normal data set of <FIG>, but R-R interval lengths consistent with those of the normal data set.

As another example, <FIG> is a heat map plot of occurrences of combinations of R-R interval and R-wave amplitude in a not normal data set and, more particularly, a data set that would be classified as signal loss, e.g., due to one or more electrodes failing, being removed, or otherwise becoming inoperable, which is characterized by noise associated with loss of and reconnection to the signal source. In the example of <FIG>, each beat is binned within one of the <NUM> mV amplitude bins between <NUM> and <NUM> mV and one of the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds. Region <NUM> illustrates the distribution of such values in a signal loss data set, including distinct clusters of different R-wave amplitudes associated with loss and normal data, but R-R interval lengths consistent with those of the normal data set.

As another example, <FIG> is a heat map plot of occurrences of combinations of R-R interval and R-wave amplitude in a not normal data set and, more particularly, a data set that would be classified as electrode peeling off, which is characterized by gradual reduction in amplitude until signal loss. In the example of <FIG>, each beat is binned within one of the <NUM> mV amplitude bins between <NUM> and <NUM> mV and one of the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds. Region <NUM> illustrates the distribution of such values in a signal loss data set, including a significant number of beats with lower R-wave amplitude and longer R-R interval lengths (e.g., due to missed beats) associated with signal loss.

As another example, <FIG> is a heat map plot of occurrences of combinations of R-R interval and R-wave amplitude in a not normal data set and, more particularly, a data set that would be classified as R-R interval variability, e.g., due to atrial fibrillation. In the example of <FIG>, each beat is binned within one of the <NUM> mV amplitude bins between <NUM> and <NUM> mV and one of the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds. Region <NUM> illustrates the distribution of such values in a R-R interval variability data set, including rapid and relatively large variation of the time interval between R-wave peaks and their amplitude, e.g., due to sporadic conduction of the rapid atrial depolarizations of atrial fibrillation. The pattern illustrated by region <NUM> includes significant numbers of beats throughout a wider R-R interval range than the normal data set of <FIG>.

<FIG> are diagrams illustrating R-R interval slope data that a machine learning algorithm would learn to classify as normal and not normal, respectively, according to examples of the techniques of this disclosure. In particular, <FIG> illustrate R-R interval slope values measured for consecutive beats during a two-minute period, with each beat binned in the <NUM> minute bin in which it occurred, and the mean or median R-R interval having a length of X1. As illustrated in <FIG>, the normal data set has consistent R-R interval slope values within <NUM>*X1 range including X1. As illustrated in <FIG>, the not normal data set includes significantly greater variation in R-R interval slope over time, with R-R interval values between <NUM>*X <NUM> and <NUM>*X1.

<FIG> is a diagram illustrating data from cardiac signals of patients in different populations that a machine learning algorithm would learn to classify as not normal according to examples of the techniques of this disclosure. In the example of <FIG>, each beat is binned within one of the <NUM> mV amplitude bins between <NUM> and <NUM> mV and one of the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds. More particularly, region <NUM> illustrates the distribution of a bradycardia data set in a relatively younger patient, while region <NUM> illustrates the distribution of a bradycardia data set in a relatively older, e.g., elderly, patient. With increase age, a slightly elevated resting heart rate is expected, and the bradycardia data for the older patient similarly includes shorter R-R intervals. Consistent with this expectation, the pattern of beats illustrated by region <NUM> is shifted to shorter R-R intervals (e.g., left in <FIG>) relative to region <NUM>. To account for these differences between patient populations, a machine learning algorithm may be adapted using data sets from patients in a population having characteristics matching the patient.

<FIG> are diagrams illustrating data form cardiac signals of patients in different populations that a machine learning algorithm would learn to classify as normal according to examples of the techniques of this disclosure. <FIG> are heat map plots of occurrences of combinations of R-R interval and R-wave amplitude in the data. In the examples of <FIG>, each beat is binned within one of the <NUM> mV amplitude bins between <NUM> and <NUM> mV and one of the <NUM> second R-R interval length bins between <NUM> and <NUM> seconds (although <FIG> only illustrates the portion of the heat map from <NUM> to <NUM> seconds).

For example, region <NUM> of <FIG> illustrates the distribution of data for an average or normal BMI patient, while region <NUM> illustrates the distribution of the data for an increased BMI patient. Increased resting heart rate is expected with increased BMI, which is reflected in the, e.g., about <NUM> second, relative shift in the location of the most populous value combination between regions <NUM> and <NUM>. In particular, region <NUM> includes <NUM> beats with an R-R interval within a range from about <NUM> seconds to about <NUM> seconds, while region <NUM> includes <NUM> beats with an R-R interval within a range from about <NUM> second to about <NUM> seconds. Similarly, regions <NUM> and <NUM> in <FIG> illustrate the data distributions for a male and female, respectively, with females expected to have about a <NUM>-<NUM> bpm greater resting rate than males. Also, <FIG> illustrates data in region <NUM> that would be expected from a patient having higher blood pressure. <FIG> also illustrates region <NUM> where it would be expected that data of a patient having lower blood pressure would occur in the heat map. In this manner. <FIG> illustrates how increased blood pressure relates to increased heart rate, and how each are co-factors for the other. Again, to account for these differences between patient populations, a machine learning algorithm may be adapted using data sets from patients in a population having characteristics matching the patient. The characteristics that may distinguish patient populations include gender, age, BMI, weight, and blood pressure. Further, the machine learning algorithm may use the expected difference in data illustrated by regions <NUM> and <NUM> to determine the state of a comorbidity related to blood pressure for a patient.

<FIG> is a diagram illustrating data from a spirometer signal of a patient that a machine learning algorithm would learn to classify as normal according to examples of the techniques of this disclosure. In particular, <FIG> illustrates a heat map of values of volume over time during exhalation into a spirometer. Region <NUM> illustrates a distribution of such values that would be classified as normal.

Values that may be determined from a spirometer signal include forced exhalation volume in one second (FEV1) and forced vital capacity (FVC), which is the amount of air which can be forcibly exhaled from the lungs after taking the deepest breath possible. FVC is essentially equivalent lung capacity, and may be calculated as an integral or area under the curve of a signal from a spirometer during such an exhalation. FEV1 may similarly be calculated, but based only on the first second of the signal. The ratio of FEV <NUM> to FVC may also be determined. Normal values for these parameters are FEV1 and FVC greater than <NUM>%, and FEV <NUM>/FVC greater than <NUM>%.

<FIG> is a diagram illustrating data from a spirometer signal of a patient that a machine learning algorithm would learn to classify as not normal according to examples of the techniques of this disclosure. In particular, <FIG> illustrates a heat map of the values of volume over time during exhalation into a spirometer. Region <NUM> illustrates a distribution of such values that would be classified as not normal. The distribution of region <NUM> includes lower volume values than region <NUM> of <FIG>, and correlates to an FVC of about <NUM>% and an FEV1 of about <NUM>%, with a ratio of about <NUM>%. Generally, COPD will demonstrate reduced FEV1 and FVC below <NUM>%, and FEV <NUM>/FVC below <NUM>%.

<FIG> is a diagram illustrating data from a spirometer signal of an elderly patient that a machine learning algorithm would learn to classify as normal according to examples of the techniques of this disclosure. In particular, <FIG> illustrates a heat map of values of volume over time during exhalation into a spirometer. Region <NUM> illustrates a distribution of such values that would be classified as normal in an elderly patient. The distribution of region <NUM> correlates to an FEV1/FVC ratio of about <NUM>%. To account for such differences in spirometer difference between populations with different characteristics, such as age, a machine learning algorithm for classifying COPD may be adapted based on population-specific data sets, as described herein.

<FIG> is a diagram illustrating data form spirometer signals in varying air quality conditions, e.g.. with varying air particle counts. Certain ranges of particle counts are unhealthy for sensitive patients, such as those with COPD, while particle counts above this range are necessary to be considered unhealthy for an otherwise healthy patient. Generally, though, increased particle count may have an impact on lung volume of a normal person, as well as correlating with COPD. Region <NUM> illustrates a distribution of spirometer data with fluctuating particle count, characterized by greater variance and rapid changes. Fluctuations in particle count may correlate to fluctuations in FEV1, FVC, and FEV <NUM>/FVC. A machine learning algorithm for classifying COPD may consider particle count with other data, such as spirometer data, to account for such fluctuations.

In some examples, machine learning as described herein, is an aspect of artificial intelligence. The systems and techniques described herein may include artificial intelligence, which includes reasoning, natural language processing, machine learning, and planning. In general machine learning may include iterative learning cycles, such as supervised learning, unsupervised learning, reinforced learning, and deep learning networks.

For example, various aspects of the described techniques may be implemented within one or more processors, such as fixed function processing circuitry and/or programmable processing circuitry, including one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components.

Claim 1:
An apparatus configured to be worn by a patient (<NUM>) for cardiac defibrillation, the apparatus comprising:
sensing electrodes (<NUM>) configured to sense a cardiac signal of the patient;
defibrillation electrodes;
therapy delivery circuitry (<NUM>) configured to deliver defibrillation therapy to the patient via the defibrillation electrodes;
communication circuitry (<NUM>) configured to receive data of at least one physiological signal of the patient from at least one sensing device (<NUM>) separate from the apparatus;
a memory (<NUM>) configured to store the data, the cardiac signal, and a machine learning algorithm; and
processing circuitry (<NUM>) configured to:
apply the machine learning algorithm to the data and the cardiac signal to probabilistically determine at least one state of the patient; and
determine whether to control delivery of the defibrillation therapy based on the at least one probabilistically-determined state of the patient.