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Impact of Compression Ratio and Reconstruction Methods on ECG Classification for E-Health Gadgets: A Preliminary Study

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AI 2018: Advances in Artificial Intelligence (AI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11320))

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Abstract

In IoT applications, it is often necessary to achieve an optimal trade-off between data compression and data quality. This study investigates the effect of Compressed Sensing and reconstruction algorithms on ECG arrhythmia detection using SVM classifiers. To neutralise the mutual effect of compression and reconstruction algorithms on one another, we consider each reconstruction algorithms with various compression ratios and vice versa. The employed reconstruction algorithms are Basis Pursuit (BP) and Orthogonal Matching Pursuit (OMP). We employ two steps: (a) identifying proper compression ratio that withholds essential information of ECG signals, (b) assessing the impact of two reconstruction algorithms and their exactness on quality of classification. The findings of this study are threefold: (a) Remarkably, the SVM classifier requires few samples to detect ECG arrhythmia. (b) The results indicate for compression ratios up to around 1:7 ECG signals are recovered then classified with the same quality for both algorithms. However, by increasing compression ratio BP outperforms OMP in terms of ECG arrhythmia detection. (c) Negative correlation between compression ratio and signal quality is observed, that is intuitive enough to realise the trade-off between them.

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Authors and Affiliations

  1. Information Science Department, University of Otago, Dunedin, New Zealand

    Sophie Zareei & Jeremiah D. Deng

Authors
  1. Sophie Zareei
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  2. Jeremiah D. Deng
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Correspondence to Sophie Zareei .

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Editors and Affiliations

  1. University of Canterbury, Christchurch, New Zealand

    Tanja Mitrovic

  2. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Bing Xue

  3. RMIT University, Melbourne, VIC, Australia

    Xiaodong Li

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Zareei, S., Deng, J.D. (2018). Impact of Compression Ratio and Reconstruction Methods on ECG Classification for E-Health Gadgets: A Preliminary Study. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_9

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  • DOI: https://doi.org/10.1007/978-3-030-03991-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03990-5

  • Online ISBN: 978-3-030-03991-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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