A novel deep learning approach for early detection of cardiovascular diseases from ECG signals

Aarthy, ST.; Mazher Iqbal, J.L.

doi:10.1016/j.medengphy.2024.104111

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2024

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Cited by 3 publications

References 20 publications

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Innovative approaches for coronary heart disease management: integrating biomedical sensors, deep learning, and stellate ganglion modulation

Xu,

Yang,

Zhao

et al. 2024

Computer Methods in Biomechanics and Biomedical Engineering

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Innovative approaches for coronary heart disease management: integrating biomedical sensors, deep learning, and stellate ganglion modulation

Xu,

Yang,

Zhao

et al. 2024

Computer Methods in Biomechanics and Biomedical Engineering

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Cardiac Arrhythmia Diagnosis Using Deep Learning: A 1D CNN-GRU Approach with Multiclass SVM and DWT Analysis

Vijay Anand,

et al. 2024

2024 Tenth International Conference on Bio Signals, Images, and Instrumentation (ICBSII)

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Enhanced Discrete Wavelet Transform–Non-Local Means for Multimode Fiber Optic Vibration Signal

Peng,

Yu,

Zhang

et al. 2024

Photonics

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Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy.

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A novel deep learning approach for early detection of cardiovascular diseases from ECG signals

Cited by 3 publications

References 20 publications

Innovative approaches for coronary heart disease management: integrating biomedical sensors, deep learning, and stellate ganglion modulation

Innovative approaches for coronary heart disease management: integrating biomedical sensors, deep learning, and stellate ganglion modulation

Cardiac Arrhythmia Diagnosis Using Deep Learning: A 1D CNN-GRU Approach with Multiclass SVM and DWT Analysis

Enhanced Discrete Wavelet Transform–Non-Local Means for Multimode Fiber Optic Vibration Signal

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