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ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform

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Abstract

Electrocardiogram (ECG) is used to record the electrical activity of the heart. The ECG signal being non-stationary in nature, makes the analysis and interpretation of the signal very difficult. Hence accurate analysis of ECG signal with a powerful tool like discrete wavelet transform (DWT) becomes imperative. In this paper, ECG signal is denoised to remove the artifacts and analyzed using Wavelet Transform to detect the QRS complex and arrhythmia. This work is implemented in MATLAB software for MIT/BIH Arrhythmia database and yields the sensitivity of 99.85 %, positive predictivity of 99.92 % and detection error rate of 0.221 % with wavelet transform. It is also inferred that DWT outperforms principle component analysis technique in detection of ECG signal.

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Authors and Affiliations

  1. Shaheed Bhagat Singh State Technical Campus, Ferozepur, Punjab, India

    Inderbir Kaur & Rajni Rajni

  2. Sant Longowal Institute of Engineering and Technology, Longowal, Sangrur, Punjab, India

    Anupma Marwaha

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  1. Inderbir Kaur
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  2. Rajni Rajni
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  3. Anupma Marwaha
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Correspondence to Inderbir Kaur.

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Kaur, I., Rajni, R. & Marwaha, A. ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform. J. Inst. Eng. India Ser. B 97, 499–507 (2016). https://doi.org/10.1007/s40031-016-0247-3

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  • DOI: https://doi.org/10.1007/s40031-016-0247-3

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