Computer-aided analysis of gait rhythm fluctuations in amyotrophic lateral sclerosis.
Deterioration of motor neurons due to amyotrophic lateral sclerosis (ALS) would affect the strides from one gait cycle to the next. Computer-assisted techniques are useful for gait analysis, and also have high potential in quantitatively monitoring the pathological progression. In this paper, we app...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 11; pp. 1165 - 1172 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2009
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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=104907570&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104907570 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2009 vid: 47 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104907570 NLM19707807 2010453656 10.1007/s11517-009-0527-z NLM19707807 104907570 ppf: 1165 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Computer-aided analysis of gait rhythm fluctuations in amyotrophic lateral sclerosis. aug: au: Wu Y Krishnan S Wu, Yunfeng Krishnan, Sridhar affil: Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada sug: subj: Amyotrophic Lateral Sclerosis Pathology Gait Analysis Methods Gait Classification Rehabilitation Science Human P-Value ROC Curve Signal Processing, Computer Assisted Validation Studies ab: Deterioration of motor neurons due to amyotrophic lateral sclerosis (ALS) would affect the strides from one gait cycle to the next. Computer-assisted techniques are useful for gait analysis, and also have high potential in quantitatively monitoring the pathological progression. In this paper, we applied the signal turns count method to measure the fluctuations in the swing-interval time series recorded from 16 healthy control subjects and 13 patients with ALS. The swing-interval turns count (SWITC) parameter derived with the threshold of 0.06 s presented a significant difference (p < 0.001) between the healthy control subjects and ALS patients. Besides the SWITC, we also computed the averaged stride interval (ASI), which is usually longer in the patient with ALS (p < 0.0001), to characterize the gait patterns of ALS patients. In the pattern classification experiments, the Fisher's linear discriminant analysis (FLDA) and the least squares support vector machine (LS-SVM), both input with the SWITC and ASI features, were evaluated using the leave-one-out cross-validation method. The results showed that the LS-SVM with sigmoid kernels was able to provide a classification accurate rate of 89.66% and an area of 0.9629 under the receiver operating characteristic (ROC) curve, which were superior to those obtained with the linear classifier in the form of FLDA. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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