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ECG signal processing and KNN classifier-based abnormality detection by VH-doctor for remote cardiac healthcare monitoring

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Abstract

With the invent of medical expert systems, the demand for efficient innovative techniques in signal processing to detect abnormalities is ever increasing for identifying heart-related problems. The major objective of this research is to offer medical services to people in remote villages at low cost. People in villages and remote areas do not have a facility to get treated by a medical expert. This research provides them an opportunity to get medical advice through the virtual environment called the VH-doctor machine. It is a virtual environment heart doctor and reduces the human effort in testing and treating of heart diseases at the initial stages. The patients are treated and diagnosed only with the help of machines but not human effort. Biomedical sensors, ARM processor and FPGA are used to detect, test, analyze and display normal or abnormal cases. In this research, ECG signal processing, feature extraction and KNN classifier are performed and achieve the highest accuracy of 99% better than other machine learning algorithms.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology, Chennai, India

    B. Venkataramanaiah

  2. Department of Electronics and Communication Engineering, College of Engineering, Guindy, Anna University, Chennai, India

    J. Kamala

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  1. B. Venkataramanaiah
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  2. J. Kamala
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Correspondence to B. Venkataramanaiah.

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Communicated by V. Loia.

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Venkataramanaiah, B., Kamala, J. ECG signal processing and KNN classifier-based abnormality detection by VH-doctor for remote cardiac healthcare monitoring. Soft Comput 24, 17457–17466 (2020). https://doi.org/10.1007/s00500-020-05191-1

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