Improved peaks automatic detection algorithm of noisy quasi-periodic physiological signals.
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| Názov: | Improved peaks automatic detection algorithm of noisy quasi-periodic physiological signals. |
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| Autori: | Chou YX; Department of Electrical Engineering and Automation, Suzhou University of Technology, Suzhou, People's Republic of China., Chou LJ; Department of Mechatronics Engineering, Huaian Senior Vocational & Technical School, Huaian, People's Republic of China., Gu SH; Department of Electrical Engineering and Automation, Suzhou University of Technology, Suzhou, People's Republic of China., Yang HP; Department of Electrical Engineering and Automation, Suzhou University of Technology, Suzhou, People's Republic of China., Liu JC; Department of Electrical Engineering and Automation, Suzhou University of Technology, Suzhou, People's Republic of China., Feng Y; Department of Electrical Engineering and Automation, Suzhou University of Technology, Suzhou, People's Republic of China.; Department of College of Physics and Electronic Engineering, Northeast Petroleum University, Daqing, People's Republic of China. |
| Zdroj: | Biomedical physics & engineering express [Biomed Phys Eng Express] 2025 Oct 27; Vol. 11 (6). Date of Electronic Publication: 2025 Oct 27. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol : IOP Publishing Ltd., [2015]- |
| Výrazy zo slovníka MeSH: | Algorithms* , Signal Processing, Computer-Assisted*, Signal-To-Noise Ratio ; Humans ; Electrocardiography ; Computer Simulation ; Detection Algorithms |
| Abstrakt: | The automatic multiscale-based peak detection (AMPD) method for quasi-periodic signals suffers from high computational complexity, substantial memory requirements, and occasional peak detection failures. To address these limitations, this paper proposes an Improved AMPD (IAMPD) algorithm that significantly enhances computational speed while maintaining strong noise robustness. The improvement in efficiency is attained through a frequency-informed scale constraint that narrows the search space using prior knowledge of the signal's frequency range, greatly reducing redundant computations. This is combined with an optimized computational process that integrates redundant operations and minimizes intermediate caching. Extensive evaluations on both simulated signals with varying signal-to-noise ratios (SNRs) and real physiological data demonstrate the superior performance of IAMPD. The algorithm achieves a speedup of over 160 times compared to the original AMPD while maintaining perfect detection performance across all evaluation metrics: Sensitivity ( Se ), Positive Predictivity (+ P ), and F1-score ( F 1), each consistently attaining 100% even at 0 dB SNR, thereby outperforming other benchmark methods.Importantly, IAMPD preserves the parameter-free advantage of AMPD, requiring only an estimated frequency range. Experimental results confirm its effectiveness in accurately detecting peaks in various physiological signals including heart sounds, electrocardiogram (ECG), pulse, and respiratory signals, making it well-suited for real-time applications. (© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.) |
| Contributed Indexing: | Keywords: automatic peak detection; few adjustment parameters; quasi-periodic physiological signals; strong noise immunity |
| Entry Date(s): | Date Created: 20251014 Date Completed: 20251027 Latest Revision: 20251027 |
| Update Code: | 20251028 |
| DOI: | 10.1088/2057-1976/ae12f9 |
| PMID: | 41086823 |
| Databáza: | MEDLINE |
Nájsť tento článok vo Web of Science | Abstrakt: | The automatic multiscale-based peak detection (AMPD) method for quasi-periodic signals suffers from high computational complexity, substantial memory requirements, and occasional peak detection failures. To address these limitations, this paper proposes an Improved AMPD (IAMPD) algorithm that significantly enhances computational speed while maintaining strong noise robustness. The improvement in efficiency is attained through a frequency-informed scale constraint that narrows the search space using prior knowledge of the signal's frequency range, greatly reducing redundant computations. This is combined with an optimized computational process that integrates redundant operations and minimizes intermediate caching. Extensive evaluations on both simulated signals with varying signal-to-noise ratios (SNRs) and real physiological data demonstrate the superior performance of IAMPD. The algorithm achieves a speedup of over 160 times compared to the original AMPD while maintaining perfect detection performance across all evaluation metrics: Sensitivity ( Se ), Positive Predictivity (+ P ), and F1-score ( F 1), each consistently attaining 100% even at 0 dB SNR, thereby outperforming other benchmark methods.Importantly, IAMPD preserves the parameter-free advantage of AMPD, requiring only an estimated frequency range. Experimental results confirm its effectiveness in accurately detecting peaks in various physiological signals including heart sounds, electrocardiogram (ECG), pulse, and respiratory signals, making it well-suited for real-time applications.<br /> (© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.) |
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| ISSN: | 2057-1976 |
| DOI: | 10.1088/2057-1976/ae12f9 |