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ECG signal analysis using CWT, spectrogram and autoregressive technique

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Abstract

The cardiovascular system is a combination of the heart, blood and blood vessels. Cardiovascular diseases (CVD) are a key factor behind casualties worldwide among both women and men. About 9.4 million deaths occur due to high Blood Pressure (BP) only, out of which 51% deaths are due to strokes and 45% deaths are due to coronary heart diseases. The Electrocardiogram (ECG) represents the heart health condition of the subject, (patient) since it is acquired through electrical conduction, which appears in terms of P-QRS-T waves. But analysis of these waves is very tedious due to the existence of different noises/artifacts. Computer Aided Diagnosis (CAD) system is required in practical medical scenario for better and automated ECG signal analysis and to compensate for human errors. In general, implementation of a CAD system for ECG signal analysis requires; preprocessing, feature extraction and classification. In the existing literature, some authors have used time domain techniques which yield good performance for cleaned ECG signals i.e., without noise/artifact. Some authors have used frequency domain techniques later, but they suffer from the problem of spectral leakage making them unsuitable for real time/pathological datasets. The existing techniques from both these domains are not able to effectively analyze nonlinear behavior of ECG signals. These limitations have motivated this work where Continuous Wavelet Transform (CWT), Spectrogram and Autoregressive (AR) technique are used collectively for interpreting nonlinear and non-stationary features of the ECG signals. In this paper, both Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia database (MB Ar DB) and Real-time database (RT DB) have been used. Performance of the proposed method is compared with that of the previous studies on the basis of sensitivity (SE) and detection rate (D.R). The proposed technique yields SE of 99.90%, D.R of 99.81% & SE of 99.77%, D.R of 99.87% for MB Ar DB and RT DB, respectively. Therefore, the proposed technique showcases the possibility of an encouraging diagnostic tool for further improving the present situation of health informatics in cardiology labs/hospitals.

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Abbreviations

CVD:

Cardiovascular Disease

STFT:

Short-time Fourier Transform

CWT:

Continuous Wavelet Transform

MB Ar DB:

Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia Database

RT DB:

Real-time Database

CAD:

Computer Aided Diagnosis

ECG:

Electrocardiogram

SGDF:

Savitzky–Golay Digital Filtering

KNN:

K-Nearest Neighbor

AR:

Autoregressive

TFA:

Time–Frequency Analysis

EDM:

Euclidean Distance Metric

PSD:

Power Spectral Density

SE:

Sensitivity

DR:

Detection Rate

TP:

True Positive

FP:

False Positive

FN:

False Negative

WHO:

World Health Organization

SNR:

Signal to Noise Ratio

RMSE:

Root Mean Square Error

PRD:

Percent Root Mean Square Difference

ACC:

Accuracy

SPE:

Specificity

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, UP, India

    Varun Gupta

  2. Department of Electrical Engineering, National Institute of Technology, Kurukshetra, India

    Monika Mittal

  3. Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, India

    Vikas Mittal

  4. Department of Education, K.L.P.G. College, Meerut, UP, India

    Anshu Gupta

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Gupta, V., Mittal, M., Mittal, V. et al. ECG signal analysis using CWT, spectrogram and autoregressive technique. Iran J Comput Sci 4, 265–280 (2021). https://doi.org/10.1007/s42044-021-00080-8

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  • DOI: https://doi.org/10.1007/s42044-021-00080-8

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