Various computer-aided diagnosis techniques for arrhythmia detection were reviewed and compared in terms of their performance and suitability for hardware implementation in body-wearable devices.
This review summarizes various computer-aided diagnosis techniques for ECG-based arrhythmia detection and their suitability for wearable devices.
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 the heart. Any abnormal behavior in the ECG signal is an indicative measure of a malfunctioning of the heart, termed an arrhythmia condition . Due to the involved complexities such as lack of human expertise and high probability 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. In this work, we classify the arrhythmia detection approaches that make use of CADiag based on the utilized technique. A vast number of techniques useful for arrhythmia detection, their performances, the involved complexities, and comparison among different variants of same technique and across different techniques are discussed. The comparison of different techniques in terms of their performance for arrhythmia detection and its suitability for hardware implementation toward body-wearable devices is discussed in this work.
Dinakarrao et al. (Wed,) conducted a review in Arrhythmia. Computer-aided diagnosis (CADiag) techniques was evaluated. Various computer-aided diagnosis techniques for arrhythmia detection were reviewed and compared in terms of their performance and suitability for hardware implementation in body-wearable devices.