A system and method for determining an arrhythmia risk are provided and include memory to store specific executable instructions and a machine learning (ML) model trained to predict an arrhythmia with a characteristic of interest (COI) that exhibits a non-physiologic behavior. One or more processors are configured to execute the specific executable instructions to obtain CA signals collected by an implantable medical device (IMD), wherein the COI exhibits a physiologic behavior and apply the ML model to the CA signals to identify a risk factor that a patient will experience the arrhythmia at a future point in time even though the COI in the CA signals, exhibits a physiologic behavior.

A system and method for determining an arrhythmia risk are provided and include memory to store specific executable instructions and a machine learning (ML) model trained to predict an arrhythmia with a characteristic of interest (COI) that exhibits a non-physiologic behavior. One or more processors are configured to execute the specific executable instructions to obtain CA signals collected by an implantable medical device (IMD), wherein the COI exhibits a physiologic behavior and apply the ML model to the CA signals to identify a risk factor that a patient will experience the arrhythmia at a future point in time even though the COI in the CA signals, exhibits a physiologic behavior.

A wearable device includes: a biological information acquisition device that acquires biological information of a subject; a biological information storage device that stores biological information; an abnormality determination device that determines whether or not there is an abnormality and an abnormality level in the biological information of the subject; a notification destination determination device that, if an abnormality has occurred in the biological information of the subject, acquires notification destination information that corresponds to the abnormality level that was determined by the abnormality determination device; a notification destination database in which notification destination information for use when an abnormality has occurred in the biological information of the subject is stored for each abnormality level in advance; and a notification device that transmits information that includes the abnormality level and the determination result of the abnormality determination device to a notification destination device indicated by the information.

A cardiac medical system, such as an implantable cardioverter defibrillator (ICD) system, receives a cardiac electrical signal by and senses cardiac events when the signal crosses an R-wave sensing threshold. The system determines at least one sensed event parameter from the cardiac electrical signal for consecutive cardiac events sensed by the sensing circuit and compares the sensed event parameters to P-wave oversensing criteria. The system detects P-wave oversensing in response to the sensed event parameters meeting the P-wave oversensing criteria; and adjusts at least one of an R-wave sensing control parameter or a therapy delivery control parameter in response to detecting the P-wave oversensing.

A subcutaneous insertable cardiac monitor (ICM) for use in performing long term electrocardiographic (ECG) monitoring is disclosed. The length of the monitoring performed by the ICM is extended, potentially for a life time of the patient, and the functionality of the ICM is enhanced, including enhancing the rate at which data can be offloaded from the ICM, by including an internal energy harvesting module in the ICM. The energy harvesting module harvests energy from outside the ICM, and provides the harvested energy for powering the circuitry of the ICM, either directly or by recharging a power cell within the ICM. As the circuitry of the ICM requires a low amount of electrical power, the harvested energy can be sufficient to support the functioning of the ICM even when the electrical power stored on the ICM at the time of implantation runs out.

To provide a pain evaluation device, a pain evaluation method, and a non-transitory storage medium storing a pain evaluation program that can evaluate the degree of pain in each individual. A control unit includes: an electrocardiographic waveform acquisition unit configured to acquire electrocardiographic waveform data of a user; and a pain determination unit configured to determine, based on a comparison between first electrocardiographic waveform data obtained by the electrocardiographic waveform acquisition unit in a state where no physical load is applied to the body of the user and second electrocardiographic waveform data obtained by the electrocardiographic waveform acquisition unit in a state where a load is applied to the body of the user, a degree of pain sensed by the user in a state where the load is applied.

An electrocardiogram information dynamic monitoring method and dynamic monitoring system. The method includes a dynamic monitoring device receiving monitoring reference data input by a user or issued by a server; the data collection on a tested object so as to obtain electrocardiogram data of the tested object; the characteristic identification on the electrocardiogram data so as to obtain characteristic signals of the electrocardiogram data, implementing cardiac activity classification on the electrocardiogram data according to the characteristic signals, obtaining cardiac activity classification information according to electrocardiogram basic rule reference data, and generating electrocardiogram event data, wherein the electrocardiogram event data comprises device ID information of the dynamic monitoring device; the dynamic monitoring device determining corresponding electrocardiogram event information according to the electrocardiogram event data, and determining whether the electrocardiogram event information is electrocardiogram abnormality event information; and outputting alarm information when the electrocardiogram event information is electrocardiogram abnormality event information.

An electrocardiogram information dynamic monitoring method and dynamic monitoring system. The method includes a dynamic monitoring device receiving monitoring reference data input by a user or issued by a server; the data collection on a tested object so as to obtain electrocardiogram data of the tested object; the characteristic identification on the electrocardiogram data so as to obtain characteristic signals of the electrocardiogram data, implementing cardiac activity classification on the electrocardiogram data according to the characteristic signals, obtaining cardiac activity classification information according to electrocardiogram basic rule reference data, and generating electrocardiogram event data, wherein the electrocardiogram event data comprises device ID information of the dynamic monitoring device; the dynamic monitoring device determining corresponding electrocardiogram event information according to the electrocardiogram event data, and determining whether the electrocardiogram event information is electrocardiogram abnormality event information; and outputting alarm information when the electrocardiogram event information is electrocardiogram abnormality event information.

A system and a method for identifying a patient with a threshold number of distinct ECG abnormalities. The system and the method include an ECG monitoring device; a server; a database; a network; a memory containing machine readable medium comprising a machine executable code having stored thereon instructions for identifying patients with a threshold number of distinct ECG abnormalities; and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to: receive an ECG data output from the ECG monitoring device; process the ECG data output to identify abnormalities in the ECG data; and analyze the abnormalities in the ECG data in order to output an indication of whether the patient has depressed LVEF, wherein the ECG monitoring device, the server, the database, the memory, and the processor are coupled to the network via communication links.

A system and a method for identifying a patient with a threshold number of distinct ECG abnormalities. The system and the method include an ECG monitoring device; a server; a database; a network; a memory containing machine readable medium comprising a machine executable code having stored thereon instructions for identifying patients with a threshold number of distinct ECG abnormalities; and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to: receive an ECG data output from the ECG monitoring device; process the ECG data output to identify abnormalities in the ECG data; and analyze the abnormalities in the ECG data in order to output an indication of whether the patient has depressed LVEF, wherein the ECG monitoring device, the server, the database, the memory, and the processor are coupled to the network via communication links.