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S. Pudukotai Dinakarrao, A. Jantsch, M. Shafique:
"Computer-aided arrhythmia diagnosis with bio-signal processing: A survey of trends and techniques";
Acm Computing Surveys, 52 (2019), 2; S. 1 - 37.

Signals obtained from a patient, i.e., bio-signals, are utilized to analyze the health of patient. One such bio-signal of paramount importance is the electrocardiogram (ECG), which represents the functioning of theheart. Any abnormal behavior in the ECG signal is an indicative measure of a malfunctioning of the heart,termed anarrhythmia condition. Due to the involved complexities such as lack of human expertise and highprobability to misdiagnose, long-term monitoring based on computer-aided diagnosis (CADiag) is preferred.There exist various CADiag techniques for arrhythmia diagnosis with their own benefits and limitations. Inthis work, we classify the arrhythmia detection approaches that make use of CADiag based on the utilizedtechnique. A vast number of techniques useful for arrhythmia detection, their performances, the involvedcomplexities, and comparison among different variants of same technique and across different techniquesare discussed. The comparison of different techniques in terms of their performance for arrhythmia detectionand its suitability for hardware implementation toward body-wearable devices is discussed in this work

Electrocardiogram (ECG), arrhythmia detection, computer-aided diagno-sis, health-care, machine learning, neural networks, support-vector machine

http://dx.doi.org/10.1145/3297711