A computer implemented system and method include one or more processors configured to receive a plurality of electrocardiogram (ECG) signals from one or more subcutaneous implantable medical devices (IMDs) and combine at least two of the plurality of ECG signals to form a first composite ECG signal.

A computer implemented system and method include one or more processors configured to receive a plurality of electrocardiogram (ECG) signals from one or more subcutaneous implantable medical devices (IMDs) and combine at least two of the plurality of ECG signals to form a first composite ECG signal.

A computer implemented method is provided that includes, under control of one or more processors of an implantable medical device (IMD), obtaining motion data indicative of a first posture, and determining a first sense setting of the IMD based on the first posture. The method also includes obtaining cardiac activity (CA) signals for a series of beats while applying the first sense setting, obtaining a characteristic of interest (COI) from the CA signals for the series of beats, and calculating a statistical indicator from the COI over the series of beats based on the COI from the CA signals. The method also includes deriving a second sense setting based on the first sense setting and the statistical indicator of the COI.

A computer implemented method is provided that includes, under control of one or more processors of an implantable medical device (IMD), obtaining motion data indicative of a first posture, and determining a first sense setting of the IMD based on the first posture. The method also includes obtaining cardiac activity (CA) signals for a series of beats while applying the first sense setting, obtaining a characteristic of interest (COI) from the CA signals for the series of beats, and calculating a statistical indicator from the COI over the series of beats based on the COI from the CA signals. The method also includes deriving a second sense setting based on the first sense setting and the statistical indicator of the COI.

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.

Disclosed are a method and apparatus for visualizing an electrocardiogram using deep learning.

The present embodiment provides a method and apparatus for visualizing an electrocardiogram, the method and apparatus which analyze an electrocardiogram using a deep learning algorithm for accurate arrhythmia determination as a real-time operation algorithm for monitoring a bedridden patient in order to solve the manpower shortage of medical staff, and then visually output it in real time so that a visual help may be provided for medical staff.

Disclosed are a method and apparatus for visualizing an electrocardiogram using deep learning.

The present embodiment provides a method and apparatus for visualizing an electrocardiogram, the method and apparatus which analyze an electrocardiogram using a deep learning algorithm for accurate arrhythmia determination as a real-time operation algorithm for monitoring a bedridden patient in order to solve the manpower shortage of medical staff, and then visually output it in real time so that a visual help may be provided for medical staff.

An apparatus yields signals that are equivalent to ECG signals and allow determination of a heartbeat interval or heart rate from bio-vibration signals including vibrations derived from heartbeats. An ECG meter acquires ECG signals of a sample, and a piezoelectric sensor acquires bio-vibration signals of the sample simultaneously. The bio-vibration signals include beating vibration signals derived from heartbeats. A learning unit of a prediction modeling apparatus establishes a prediction model by machine learning in which ECG signals are used as teaching data, and model input signals obtained by performing a specified processing on the bio-vibration signals are input. The learning unit delivers the prediction model to a prediction unit of a signal processing apparatus. The prediction model predicts and outputs pECG signals upon input of model input signals obtained by performing a specified processing on bio-vibration signals acquired from a subject under prediction with a piezoelectric sensor.

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.