A solution for co-frequency and low SNR problems in heart rate estimation based on photoplethysmography signals.
In order to realize high-accuracy heart rate (HR) estimation based on photoplethysmography (PPG) under the scenes of low signal-to-noise ratio (SNR) and co-frequency caused by motion artifacts (MAs), this paper presents a novel framework integrating two-stage variational mode decomposition (VMD) den...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 12; pp. 3419 - 3434 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160112195&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160112195 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2022 vid: 60 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160112195 160112195 NLM36190610 10.1007/s11517-022-02678-x NLM36190610 160112195 ppf: 3419 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A solution for co-frequency and low SNR problems in heart rate estimation based on photoplethysmography signals. aug: au: Zhao, Jiaqi Chen, Xiang Zhang, Xu Chen, Xun affil: Dept Elect Sci & Technol, University of Science and Technology of China, 230027, Hefei, Anhui, China sug: subj: Plethysmography Methods Signal Processing, Computer Assisted Sensitivity and Specificity Heart Rate Physiology Artifacts Algorithms Short Portable Mental Status Questionnaire Scales Exercise of Self-Care Agency Scale ab: In order to realize high-accuracy heart rate (HR) estimation based on photoplethysmography (PPG) under the scenes of low signal-to-noise ratio (SNR) and co-frequency caused by motion artifacts (MAs), this paper presents a novel framework integrating two-stage variational mode decomposition (VMD) denoising method, noise compensation technology, and hidden Markov model (HMM)-based tracking algorithm. The two-stage VMD denoising method is designed to separate the HR signal from MA under low SNR scene. The noise compensation technology is applied to solve the problem of co-frequency. HMM-based HR tracking method is adopted to obtain the global optimization performance of HR estimation. The effectiveness and superiority of the proposed framework in solving problems of low SNR and co-frequency associated with motion artifacts have been verified by the HR estimation experiments carried out on three public high-SNR PPG databases (ISPC, BAMI I, BAMI II) and a self-built low-SNR database (WeData). Compared with the two classical frameworks namely joint sparse spectrum reconstruction (JOSS) and convolutional neural network-long short-term memory network (CNN-LSTM), the proposed framework obtains the lowest HR estimation errors (0.94 beats per minute (BPM) and 1.81 BPM respectively) on both BAMI 2 with the highest SNR (0.40 dB) and WeData with the lowest SNR (- 9.07 dB). For the low-SNR database Wedata, the average absolute error (AAE) decreases by more than 21 BPM. The research result of this study provides a solution for the realization of high-accuracy PPG-based HR estimation in exercise scenarios. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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