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ECG Signal Compression Based on Optimization of Wavelet Parameters and Threshold Levels Using Evolutionary Techniques.

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Title: ECG Signal Compression Based on Optimization of Wavelet Parameters and Threshold Levels Using Evolutionary Techniques.
Authors: Singhai, Paridhi1 (AUTHOR), Kumar, Anil1 (AUTHOR) anilkdee@gmail.com, Ateek, A.1 (AUTHOR), Ansari, Irshad Ahmad1 (AUTHOR), Singh, G. K.2 (AUTHOR), Lee, Heung No3 (AUTHOR)
Source: Circuits, Systems & Signal Processing. Jun2023, Vol. 42 Issue 6, p3509-3537. 29p.
Subjects: Optimization algorithms, Run-length encoding, Discrete wavelet transforms, Electrocardiography, Mathematical optimization, Data compression
Abstract: The ECG (electrocardiogram) signals are an indicator of the electrical activity of the heart. Given its noninvasive nature ECG are an extremely popular medium for heart checkups. With the advent of modern technology, the world is moving toward a connected environment, and with the availability of wearable devices, there is an exponential increase in the transmission and storage of ECG and other physiological signals. It becomes necessary to compress the ECG signals for storage and transmission. Therefore, this paper presents an ECG compression algorithm based on discrete wavelet transform (DWT) and several nature-inspired optimization techniques. The ECG compression method uses optimization techniques to find the optimal values of wavelet design parameters and optimal threshold levels. In the proposed work, DWT is used to decompose the signal into sub-bands, and coefficients are obtained. Then, threshold values for each sub-band are selected using the optimization algorithms. After thresholding, the coefficients are further compressed using the modified run-length encoding (MRLE). The proposed work shows promising results and the original signal features are well preserved after reconstruction. The performance of this algorithm is tested by calculating different parameters such as percentage root-mean-square difference (PRD), quality score (QS), signal-to-noise ratio (SNR), and compression ratio (CR). This method is capable of providing a higher compression ratio with minimum distortion in ECG signal. [ABSTRACT FROM AUTHOR]
Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Circuits%2C+Systems+%26+Signal+Processing%22">Circuits, Systems & Signal Processing</searchLink>. Jun2023, Vol. 42 Issue 6, p3509-3537. 29p.
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  Data: The ECG (electrocardiogram) signals are an indicator of the electrical activity of the heart. Given its noninvasive nature ECG are an extremely popular medium for heart checkups. With the advent of modern technology, the world is moving toward a connected environment, and with the availability of wearable devices, there is an exponential increase in the transmission and storage of ECG and other physiological signals. It becomes necessary to compress the ECG signals for storage and transmission. Therefore, this paper presents an ECG compression algorithm based on discrete wavelet transform (DWT) and several nature-inspired optimization techniques. The ECG compression method uses optimization techniques to find the optimal values of wavelet design parameters and optimal threshold levels. In the proposed work, DWT is used to decompose the signal into sub-bands, and coefficients are obtained. Then, threshold values for each sub-band are selected using the optimization algorithms. After thresholding, the coefficients are further compressed using the modified run-length encoding (MRLE). The proposed work shows promising results and the original signal features are well preserved after reconstruction. The performance of this algorithm is tested by calculating different parameters such as percentage root-mean-square difference (PRD), quality score (QS), signal-to-noise ratio (SNR), and compression ratio (CR). This method is capable of providing a higher compression ratio with minimum distortion in ECG signal. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2023
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