Real-Time Cardiac Arrhythmia Classification Using Memristor Neuromorphic Computing System.

Cardiac arrhythmia is known to be one of the most common causes of death worldwide. Therefore, development of efficient arrhythmia detection techniques is essential to save patients' lives. In this paper, we introduce a new real-time cardiac arrhythmia classification using memristor neuromorphic computing system for classification of 5 different beat types.

Image features of spectral correlation function for arrhythmia classification.

Recently, computerized arrhythmia classification tools have been intensively used to aid physicians to recognize different irregular heartbeats. In this paper, we introduce arrhythmia CAD system exploiting cyclostationary signal analysis through estimation of the spectral correlation function for 5 different beat types. Two experiments were performed.

Arrhythmia classification based on novel distance series transform of phase space trajectories.

Cardiac arrhythmia is a serious disorder in heart electrical activity that may have fatal consequences especially if not detected early. This motivated the development of automated arrhythmia detection systems that can early detect and accurately recognize arrhythmias thus significantly improving the chances of patient survival. In this paper, we propose an improved arrhythmia detection system particularly designed to identify five different types based on nonlinear dynamical modeling of electrocardiogram signals.