A device for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand/supply related cardiac ischemia is disclosed. The device may have both implanted and external components and can communicate with other user devices such as smartphones and smartwatches for monitoring and alerting in response to detected medically relevant events or states of a patient. The processor is configured to provide event detection based upon various criteria including what is found to be statistically abnormal for a patient or what has been defined by a doctor to be abnormal. A patient's cardiovascular condition can be tracked over time using histogram, trend, and summary information related to heart rate and/or cardiac features such as those measured from the S-T segment of heartbeats. Heartbeats that are elevated but which are below what is defined as high, are used to provide medically relevant detections.

A device for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand/supply related cardiac ischemia is disclosed. The device may have both implanted and external components and can communicate with other user devices such as smartphones and smartwatches for monitoring and alerting in response to detected medically relevant events or states of a patient. The processor is configured to provide event detection based upon various criteria including what is found to be statistically abnormal for a patient or what has been defined by a doctor to be abnormal. A patient's cardiovascular condition can be tracked over time using histogram, trend, and summary information related to heart rate and/or cardiac features such as those measured from the S-T segment of heartbeats. Heartbeats that are elevated but which are below what is defined as high, are used to provide medically relevant detections.

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.

Described herein are methods, devices, and systems that use electrogram (EGM) or electrocardiogram (ECG) data for sleep apnea detection. An apparatus and method detect potential apnea events (an apnea or hypopnea event) using a signal indicative of cardiac electrical activity of a patient's heart, such as an EGM or ECG. Variations in one or more morphological or temporal features of the signal over several cardiac cycles are determined and used to detect a potential apnea event in a measurement period. Checks can then be made for a number of factors which could result in a false detection of an apnea event and if such factors are not present, an apnea event is recorded. Described herein are also methods, devices, and systems for classifying a patient as being asleep or awake, which can be used to selectively enable and disable sleep apnea detection monitoring, as well as in other manners.

An electronic device and a method for predicting a blockage of a coronary artery are provided. The method includes: obtaining multiple pieces of electrocardiogram (ECG) data respectively corresponding to a coronary artery set; generating multiple first probabilities corresponding to the multiple pieces of electrocardiogram data respectively according to the multiple pieces of electrocardiogram data and a first phase model, generating a first determined result according to the multiple first probabilities, and selecting a first data subset corresponding to a first probability subset from the multiple pieces of electrocardiogram data in response to each one in the first data subset of the multiple first probabilities being greater than a first threshold; generating multiple second probabilities corresponding to the first data subset according to the first data subset and a second phase model, and generating a second determined result according to the multiple second probabilities.

An electronic device and a method for predicting a blockage of a coronary artery are provided. The method includes: obtaining multiple pieces of electrocardiogram (ECG) data respectively corresponding to a coronary artery set; generating multiple first probabilities corresponding to the multiple pieces of electrocardiogram data respectively according to the multiple pieces of electrocardiogram data and a first phase model, generating a first determined result according to the multiple first probabilities, and selecting a first data subset corresponding to a first probability subset from the multiple pieces of electrocardiogram data in response to each one in the first data subset of the multiple first probabilities being greater than a first threshold; generating multiple second probabilities corresponding to the first data subset according to the first data subset and a second phase model, and generating a second determined result according to the multiple second probabilities.

An exercise intensity estimation apparatus includes an RS wave calculation unit configured to calculate an RS amplitude from a peak value of an R wave to a peak value of an S wave in an ECG waveform of a target person, a T wave calculation unit configured to calculate an amplitude of a T wave of the ECG waveform, a heart rate calculation unit configured to calculate a heart rate from the ECG waveform, an index calculation unit configured to calculate, as a first index indicating exercise intensity of the target person, the amplitude of the T wave normalized by the RS amplitude, and an index calculation unit configured to calculate, as a second index indicating the exercise intensity of the target person, a value obtained by multiplying the first index by the heart rate.

A device for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand/supply related cardiac ischemia is disclosed. The device may have both implanted and external components and can communicate with other user devices such as smartphones and smartwatches for monitoring and alerting in response to detected medically relevant events or states of a patient. The processor is configured to provide event detection based upon various criteria including what is found to be statistically abnormal for a patient or what has been defined by a doctor to be abnormal. A patient's cardiovascular condition can be tracked over time using histogram, trend, and summary information related to heart rate and/or cardiac features such as those measured from the S-T segment of heartbeats. Heartbeats that are elevated but which are below what is defined as high, are used to provide medically relevant detections.

A system is described for predicting at least one cardiological dysfunction in an individual, having a means for providing an ECG which has a number n of time-synchronized ECG traces, each comprising a chronological sequence of time signals representing a sinus rhythm of the individual's heartbeat, to which at least one P wave, a QRS complex and a T wave can be assigned in chronological order. A selection means selects at least two ECG traces from the n ECG traces, an analysis unit analyses the selected ECG traces as follows: a) determining an isoelectric signal level, b) determining a first point in time chronologically before the QRS complex, c) determining a second point in time chronologically after the first point in time and chronologically before the QRS complex, d) carrying out the determining steps a) to c) for all selected ECG traces, e) determining an earliest first point in time from all the first points in time determined for the respective selected ECG traces and a latest second point in time from all the second points in time determined for the respective selected ECG traces, f) determining a time interval delimited by the earliest first point in time and latest second point in time.