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Classification and Compression of ECG Signal for Holter Device

Classification and Compression of ECG Signal for Holter Device

Chandan Kumar Jha, Maheshkumar H. Kolekar
Copyright: © 2018 |Pages: 18
ISBN13: 9781522528296|ISBN10: 1522528296|EISBN13: 9781522528302
DOI: 10.4018/978-1-5225-2829-6.ch004
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MLA

Jha, Chandan Kumar, and Maheshkumar H. Kolekar. "Classification and Compression of ECG Signal for Holter Device." Biomedical Signal and Image Processing in Patient Care, edited by Maheshkumar H. Kolekar and Vinod Kumar, IGI Global, 2018, pp. 46-63. https://doi.org/10.4018/978-1-5225-2829-6.ch004

APA

Jha, C. K. & Kolekar, M. H. (2018). Classification and Compression of ECG Signal for Holter Device. In M. Kolekar & V. Kumar (Eds.), Biomedical Signal and Image Processing in Patient Care (pp. 46-63). IGI Global. https://doi.org/10.4018/978-1-5225-2829-6.ch004

Chicago

Jha, Chandan Kumar, and Maheshkumar H. Kolekar. "Classification and Compression of ECG Signal for Holter Device." In Biomedical Signal and Image Processing in Patient Care, edited by Maheshkumar H. Kolekar and Vinod Kumar, 46-63. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2829-6.ch004

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Abstract

ECG signal processing for holter monitoring of heart patients is still exploratory. Many signal processing techniques have been evolved for classification and compression of ECG signal. Despite an increase in research in this area, many challenges remain in designing an efficient classification and compression algorithm for ECG signal. These challenges include classification accuracy, good compression ratio with acceptable diagnostic quality etc. This chapter addresses a classification and a compression algorithm based on discrete wavelet transform. Classification algorithm uses discrete wavelet transform based feature to classify abnormal heart beat from ECG signal. Support vector machine is used as a classifier to detect abnormal heartbeat. The compression algorithm utilizes discrete wavelet transform and run-length encoding as a compression tool. Proposed classification and compression algorithms can be employed in monitoring of cardiac patients using holter device.

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