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Signal Adaptive Threshold for ECG Signal Compression Using False Discovery Rate Approach.

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Title: Signal Adaptive Threshold for ECG Signal Compression Using False Discovery Rate Approach.
Authors: Rajankar, Supriya1 (AUTHOR), Rajankar, Omprakash2 (AUTHOR) online.omrajankar@gmail.com, Talbar, Sanjay3 (AUTHOR), Raut, Vrushali1 (AUTHOR)
Source: Circuits, Systems & Signal Processing. Aug2024, Vol. 43 Issue 8, p5065-5089. 25p.
Subjects: Run-length encoding, Energy consumption, Codecs, Electrocardiography, Probability theory, False discovery rate
Abstract: This study proposes a noise-insensitive signal adaptive threshold for ECG compression that overcomes restrictions in earlier methods. The method employs a wavelet-domain adaptive threshold based on false discovery rate (FDR) measurement, which links hypothesis testing to thresholding. The FDR error control technique determines the false discovery threshold (FDT) based on the signal by computing and ranking the probability of each detail coefficient. Here, the Benjamini–Hochberg (BH) process is employed for implementation. The denoised ECG data are further compressed using Huffman coding and run length encoding (RLE). The method is suitable for thresholding and provides a noise-insensitive threshold. The reconstructed signal quality is outstanding, and the technique achieves a compression that is comparable to that of conventional codecs. The quality of the signal is evaluated by the mean structural similarity index (mSSIM), which is found to be almost one, suggesting a highly similar reconstructed ECG to the original ECG. Additionally, it is noted that the suggested method produces a lower PRD value, indicating improved reconstruction quality. [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: Signal Adaptive Threshold for ECG Signal Compression Using False Discovery Rate Approach.
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  Data: <searchLink fieldCode="DE" term="%22Run-length+encoding%22">Run-length encoding</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Codecs%22">Codecs</searchLink><br /><searchLink fieldCode="DE" term="%22Electrocardiography%22">Electrocardiography</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22False+discovery+rate%22">False discovery rate</searchLink>
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  Data: This study proposes a noise-insensitive signal adaptive threshold for ECG compression that overcomes restrictions in earlier methods. The method employs a wavelet-domain adaptive threshold based on false discovery rate (FDR) measurement, which links hypothesis testing to thresholding. The FDR error control technique determines the false discovery threshold (FDT) based on the signal by computing and ranking the probability of each detail coefficient. Here, the Benjamini–Hochberg (BH) process is employed for implementation. The denoised ECG data are further compressed using Huffman coding and run length encoding (RLE). The method is suitable for thresholding and provides a noise-insensitive threshold. The reconstructed signal quality is outstanding, and the technique achieves a compression that is comparable to that of conventional codecs. The quality of the signal is evaluated by the mean structural similarity index (mSSIM), which is found to be almost one, suggesting a highly similar reconstructed ECG to the original ECG. Additionally, it is noted that the suggested method produces a lower PRD value, indicating improved reconstruction quality. [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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        Value: 10.1007/s00034-024-02673-7
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Energy consumption
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      – SubjectFull: Codecs
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      – SubjectFull: Electrocardiography
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      – SubjectFull: Probability theory
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      – SubjectFull: False discovery rate
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            – D: 01
              M: 08
              Text: Aug2024
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