The Research of Arrhythmia Algorithm Based on Fuzzy Neural Network

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.

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A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Yang

Bai

et al. 2015

BME

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Abstract. Premature ventricular contraction (PVC) is one of the most serious arrhythmias. Without early diagnosis and proper treatment, PVC can result in significant complications. In this paper, a novel feature extraction method based on a sparse auto-encoder (SAE) and softmax regression (SR) classifier was used to differentiate PVCs from other common Non-PVC rhythms, including normal sinus (N), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contraction (APC), and paced beat (PB) rhythms. The proposed method was analyzed using 40 ECG records obtained from the MIT-BIH Arrhythmia Database. The proposed method exhibited an overall accuracy of 99.4%, with a PVC recognition sensitivity and positive predictability of 97.9% and 91.8%, respectively.

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The Research of Arrhythmia Algorithm Based on Fuzzy Neural Network

Cited by 3 publications

References 5 publications

A novel method for diagnosing premature ventricular contraction beat based on chaos theory

A novel method for diagnosing premature ventricular contraction beat based on chaos theory

Computer-aided Arrhythmia Diagnosis with Bio-signal Processing

A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

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