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A Novel Approach to Perform ECG Signal Identification and Segmentation Based on PanTompkins and Hamilton-Tompkins Algorithm
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| Author:
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J.REXY , P.VELMANI, T.C.RAJAKUMAR
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| Abstract:
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Heart disease is the common cause of increase in death ratio in this decade. Irregular functioning of the heart
leads to heart disease and there are different forms of heart diseases. An ElectroCardioGram (ECG) is a basic
diagnosis test which plays a vital role to find out the heart’s rhythm and electrical activity. ECG signal is the
primary core which can be analyzed to identify various heart diseases.ECG Signal Segmentation is the basic
step to be followed before extracting useful features from the ECG Signal. This paper deals with segmenting
the ECG signal to extract the features and classify various heart diseases in the initial stage.ECG signal is the
primary input which is to be segmented for clear feature extraction. A noisy ECG signal is primarily filtered.
Pan-Tompkins and Hamilton-Tompkins (improved version of Pan Tompkins) exists for segmenting the ECG
Signals. Hence this paper proposes a novel hybrid methodology of Pan- Tompkins and Hamilton-Tompkins.
This paper is an attempt to apply the existing methodologies to Massachusetts Institute of Technology-Beth
Israel Hospital (MIT-BIH) Noise stress test Database ECG signals and analyze performance metrics such as
specificity, sensitivity, accuracy, mean square error and peak signal to noise ratio. Improving the performance
metrics will lead to novel version of signal segmentation methodology. The proposed methodology produces
improved performance metrics and it segments the given ECG Signal in a clear manner which will make way
for better feature extraction and classification process. The implementation process has been carried out using
Matlab software environment.
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| Keyword:
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ECG Signal, Segmentation, Pan-Tompkins, Hamilton-Tompkins
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2021.13.01.695
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| Download:
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