Using domain adaptation for classification of healthy and disease conditions from mobile-captured images of standard 12-lead electrocardiograms.

12-lead electrocardiogram (ECG) recordings can be collected in any clinic and the interpretation is performed by a clinician. Modern machine learning tools may make them automatable. However, a large fraction of 12-lead ECG data is still available in printed paper or image only and comes in various formats.

Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms.

Standard 12-lead electrocardiography (ECG) is used as the primary clinical tool to diagnose changes in heart function. The value of automated 12-lead ECG diagnostic approaches lies in their ability to screen the general population and to provide a second opinion for doctors. Yet, the clinical utility of automated ECG interpretations remains limited.

An SVM approach for identifying atrial fibrillation.

Objectives: We designed an automated algorithm to classify short electrocardiogram (ECG) strips into four categories: normal rhythm, atrial fibrillation, noisy segment, or other rhythm disturbances.

Approach: The algorithm is based on identification of the R peak and recognition of the other ECG waves. Time-frequency domain features, the average and variability of the intra-beat temporal interval, and the average beat morphology were also calculated.

Novel Method to Efficiently Create an mHealth App: Implementation of a Real-Time Electrocardiogram R Peak Detector.

Background: In parallel to the introduction of mobile communication devices with high computational power and internet connectivity, high-quality and low-cost health sensors have also become available. However, although the technology does exist, no clinical mobile system has been developed to monitor the R peaks from electrocardiogram recordings in real time with low false positive and low false negative detection. Implementation of a robust electrocardiogram R peak detector for various arrhythmogenic events has been hampered by the lack of an efficient design that will conserve battery power without reducing algorithm complexity or ease of implementation.