ECG signal analysis using wavelet coherence and s-transform for classification of cardiovascular diseases

Saksham Agarwal; Vigneshram Krishnamoorthy; Sawon Pratiher

doi:10.1109/ICACCI.2016.7732481

ECG signal analysis using wavelet coherence and s-transform for classification of cardiovascular diseases

Saksham Agarwal, Vigneshram Krishnamoorthy, Sawon Pratiher

Research output: Contribution to conference › Paper › peer-review

Abstract

The spontaneous classification of cardiovascular diseases is a challenging task and can be made more feasible with proper ECG fluctuation analysis. In this contribution we perform a qualitative analysis of the ECG data using complex Gaussian wavelets to investigate the multi-scale, self similar behaviour and deviation via phase plots of the wavelet cross spectrum of the ECG signals. We further analyze ECG signals using S transform to overcome the limitations of continuous wavelet transform and make the results more consistent and reliable. The results obtained are promising and the inferences drawn to aid in disease classification using the ECG signals are also discussed.

Original language	English (US)
Pages	2765-2770
DOIs	https://doi.org/10.1109/ICACCI.2016.7732481
State	Published - Nov 3 2016
Externally published	Yes
Event	2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) - Jaipur Duration: Sep 21 2016 → Sep 24 2016

Conference

Conference	2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Period	9/21/16 → 9/24/16

Keywords

ECG Analysis
Wavelet Transform
S-Transform

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10.1109/ICACCI.2016.7732481

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ECG signal analysis using wavelet coherence and s-transform for classification of cardiovascular diseases. / Agarwal, Saksham; Krishnamoorthy, Vigneshram; Pratiher, Sawon.
2016. 2765-2770 Paper presented at 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

Research output: Contribution to conference › Paper › peer-review

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abstract = "The spontaneous classification of cardiovascular diseases is a challenging task and can be made more feasible with proper ECG fluctuation analysis. In this contribution we perform a qualitative analysis of the ECG data using complex Gaussian wavelets to investigate the multi-scale, self similar behaviour and deviation via phase plots of the wavelet cross spectrum of the ECG signals. We further analyze ECG signals using S transform to overcome the limitations of continuous wavelet transform and make the results more consistent and reliable. The results obtained are promising and the inferences drawn to aid in disease classification using the ECG signals are also discussed.",

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AU - Krishnamoorthy, Vigneshram

AU - Pratiher, Sawon

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N2 - The spontaneous classification of cardiovascular diseases is a challenging task and can be made more feasible with proper ECG fluctuation analysis. In this contribution we perform a qualitative analysis of the ECG data using complex Gaussian wavelets to investigate the multi-scale, self similar behaviour and deviation via phase plots of the wavelet cross spectrum of the ECG signals. We further analyze ECG signals using S transform to overcome the limitations of continuous wavelet transform and make the results more consistent and reliable. The results obtained are promising and the inferences drawn to aid in disease classification using the ECG signals are also discussed.

AB - The spontaneous classification of cardiovascular diseases is a challenging task and can be made more feasible with proper ECG fluctuation analysis. In this contribution we perform a qualitative analysis of the ECG data using complex Gaussian wavelets to investigate the multi-scale, self similar behaviour and deviation via phase plots of the wavelet cross spectrum of the ECG signals. We further analyze ECG signals using S transform to overcome the limitations of continuous wavelet transform and make the results more consistent and reliable. The results obtained are promising and the inferences drawn to aid in disease classification using the ECG signals are also discussed.

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ER -

ECG signal analysis using wavelet coherence and s-transform for classification of cardiovascular diseases

Abstract

Conference

Keywords

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