A system and method for modeling and visualizing ST segment morphology in an ECG. Many cardiac conditions show ST-elevation in ECG data and may be misdiagnosed as a consequence. The exemplary embodiments model a segment in the ECG with a curve and extract features from the curve to discriminate between the cardiac conditions, including STEMI.

Conventionally, Atrial Fibrillation (AF) has been detected using atrial analyses which is vulnerable to background noise. Again there is a dependency on statistical features which are extracted from R-R intervals of long ECG recordings. The present disclosure addresses AF detection from single lead short ECG recordings of less than one minute wherein automatic detection of P-R and P-Q intervals is difficult, which introduces error in feature computing from the segregated intervals and compromises the performance of the classifier. In the present disclosure, a Recurrent Neural Network (RNN) based architecture comprising two Long Short Term Memory (LSTM) networks is provided for temporal analysis of R-R intervals and P wave regions in an ECG signal respectively. Output sates of the two LSTM networks are merged at a dense layer along with a set of hand-crafted statistical features to create a composite feature set for classification of the AF.

Methods and systems are provided for automatically diagnosing an electrocardiogram (ECG) using a hybrid system comprising a rule-based system and one or more deep neural networks. In one embodiment, by mapping ECG data to a plurality of features using a convolutional neural network, mapping the plurality of features to a preliminary diagnosis using a decision network, and determining a diagnosis based on the ECG data and the preliminary diagnosis using the rule-based system, a more accurate diagnosis may be determined. In another example, by incorporating both a rule-based system and one or more deep neural networks into the hybrid system, the hybrid system may be more easily adapted for use in various contexts/communities, as the one or more deep learning networks may be trained using context/community specific ECG data.

The invention relates to an active implantable medical device comprising a processing unit able to be alternately operated during a predetermined period of activity and on standby during a standby period in a cyclical manner, and means for acquiring data relating to physiological and/or physical activity. The device also comprises means for calculating a frequency analysis of the data acquired, said calculating means being capable of successively perform part of the frequency analysis during periods of activity of the processing unit.

The invention relates to an active implantable medical device comprising a processing unit able to be alternately operated during a predetermined period of activity and on standby during a standby period in a cyclical manner, and means for acquiring data relating to physiological and/or physical activity. The device also comprises means for calculating a frequency analysis of the data acquired, said calculating means being capable of successively perform part of the frequency analysis during periods of activity of the processing unit.

A method of obtaining and analyzing ECG data from a patient or group of patients is disclosed. The ECG data is obtained from the patient at an acquisition device. Once the ECG data is obtained, the ECG data is transmitted to an analysis server that is operated by an analysis provider and is located remote from the location of the acquisition device. Along with the ECG data, acquisition parameters are transmitted to the analysis server. At the analysis server, one of a plurality of algorithms is selected to analyze the ECG data. If an abnormality is detected, the patient information is directed to a healthcare provider who can then contact the patient to schedule an appointment. Based upon the referral, a referral fee can be transferred from the healthcare provider to the analysis provider. The patient can be prompted to provide additional information and selections that dictate the level of analysis generated.

A method of obtaining and analyzing ECG data from a patient or group of patients is disclosed. The ECG data is obtained from the patient at an acquisition device. Once the ECG data is obtained, the ECG data is transmitted to an analysis server that is operated by an analysis provider and is located remote from the location of the acquisition device. Along with the ECG data, acquisition parameters are transmitted to the analysis server. At the analysis server, one of a plurality of algorithms is selected to analyze the ECG data. If an abnormality is detected, the patient information is directed to a healthcare provider who can then contact the patient to schedule an appointment. Based upon the referral, a referral fee can be transferred from the healthcare provider to the analysis provider. The patient can be prompted to provide additional information and selections that dictate the level of analysis generated.

Technologies and implementations related to facilitating grouping of electrocardiogram (ECG) signals of a heart of a person (e.g., patient) wearing a wearable medical device (WMD). The ECG signals may be acquired during various times (e.g., during a normal rhythm of the heart and/or during an event of the rhythm of the heart) including various times of activity of the person (e.g., sleeping, awake, active, inactive, etc.). The ECG signals may be received and analyzed to determine if the ECG signals may be indicative of an event associated with a heart of the person or not.

Technologies and implementations related to facilitating grouping of electrocardiogram (ECG) signals of a heart of a person (e.g., patient) wearing a wearable medical device (WMD). The ECG signals may be acquired during various times (e.g., during a normal rhythm of the heart and/or during an event of the rhythm of the heart) including various times of activity of the person (e.g., sleeping, awake, active, inactive, etc.). The ECG signals may be received and analyzed to determine if the ECG signals may be indicative of an event associated with a heart of the person or not.

A medical device is configured to sense a cardiac electrical signal and determine from the cardiac electrical signal at least one of a maximum peak amplitude of a positive slope of the cardiac electrical signal and a maximum peak time interval from a pacing pulse to the maximum peak amplitude. The device is configured to determine a capture type of the pacing pulse based on at least one or both of the maximum peak amplitude and the maximum peak time interval.