Cited by 91
Chien Hsin University of Science and Technology
Publishes on EEG and Brain-Computer Interfaces, ECG Monitoring and Analysis, Non-Invasive Vital Sign Monitoring. 4 papers and 103 citations.
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In this study, “Fuzzy C-Means Method (FCMM)” is applied for classifying the cardiac arrhythmia on ECG signals, the FCMM consists of three main stages: (i) QRS extraction stage for detecting QRS waveform using the Difference Operation Method; (ii) qualitative features stage for qualitative feature selection using the Range-Overlaps Method on ECG signals; (iii) Fuzzy C-Means algorithm is used to determine the cardiac arrhythmia for the patient. The FCMM can accurately classify the normal heartbeats (NORM) and abnormal heartbeats. Abnormal heartbeats include Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Ventricular Premature Contractions (VPC) and Atrial Premature Contractions (APC). The experiments show that the sensitivities were 98.28%, 90.35%, 86.97%, 92.19%, and 94.86% for NORM, LBBB, RBBB, VPC and APC, respectively. The total classification accuracy was approximately 93.57%.
This study proposes a simple and effective method, termed Principal Component Analysis (PCA) method, to analyze ECG signals for effectively determining the heartbeat case. This method is easily performed and does not require complex mathematic computations. The average time required for processing a 30-minute long of ECG data is less than 1 minute, and the required maximum memory is only about 10 MB. The ECG records available in the MIT-BIH arrhythmia database are utilized to illustrate the effectiveness of the proposed method. The experiment results show the total classification accuracy was approximately 90.85%.