ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications

• Published in: IEEE access Vol. 6; pp. 9767 - 9773
• Main Authors: Venkatesan, C., Karthigaikumar, P., Paul, Anand, Satheeskumaran, S., Kumar, R.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filter; Adaptive filters; arrhythmic beat classification; Classification; Classifiers; Discrete Wavelet Transform; ECG preprocessing; Electrocardiography; Expert systems; Feature extraction; Health care; Heart rate variability; IIR filters; Machine learning; Portable equipment; Preprocessing; Signal classification; Signal processing; Support vector machines; SVM classifier; Wavelet transforms; White noise
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2794346

Medical expert systems are part of the portable and smart healthcare monitoring devices used in day-to-day life. Arrhythmic beat classification is mainly used in electrocardiogram (ECG) abnormality detection for identifying heart related problems. In this paper, ECG signal preprocessing and support vector machine-based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, SVM classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers.