A new detection method for EMG activity monitoring.
This paper introduces a new approach for electromyography (EMG) activity monitoring based on an improved version of the adaptive linear energy detector (ALED), a widely used technique in voice activity detection. More precisely, we propose a modified ALED technique (named M-ALED) to improve the meth...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 2; pp. 319 - 335 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2020
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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=141513918&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141513918 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2020 vid: 58 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141513918 141513918 NLM31848976 10.1007/s11517-019-02048-0 NLM31848976 141513918 ppf: 319 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A new detection method for EMG activity monitoring. aug: au: Bengacemi, Hichem Abed-Meraim, Karim Buttelli, Olivier Ouldali, Abdelaziz Mesloub, Ammar affil: Laboratoire Traitement du Signal, Ecole Militaire Polytechnique, Algiers, Algeria sug: subj: Electromyography Methods Monitoring, Physiologic Methods Signal Processing, Computer Assisted Algorithms Probability Computer Simulation Parkinson Disease Physiopathology Clinical Assessment Tools ab: This paper introduces a new approach for electromyography (EMG) activity monitoring based on an improved version of the adaptive linear energy detector (ALED), a widely used technique in voice activity detection. More precisely, we propose a modified ALED technique (named M-ALED) to improve the method's robustness with respect to noise. To achieve this objective, M-ALED relies on the Teager-Kaiser operator for signal pre-conditioning to increase the SNR and uses the order statistics to gain robustness against the signal's impulsiveness. We propose again to exploit the order statistics for the initial signal baseline estimation to deal with the cases where such information is unavailable. Finally, since M-ALED detects the signal's activity at the frame level, we propose in a second stage to refine this detection (at the sample level) by using a constant false alarm rate (CFAR) approach leading to the fine M-ALED (FM-ALED) solution. The performance of FM-ALED is assessed via real and synthetic EMG signal recordings and the obtained results highlight its effectiveness as compared with the state-of-the-art methods (it reduces the mean error probability by a factor close to 2). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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