Analysis of swallowing sounds using hidden Markov models.
In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11...
| Published in: | Medical & Biological Engineering & Computing Vol. 46; no. 4; pp. 307 - 315 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Apr2008
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105747486&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105747486 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2008 vid: 46 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105747486 NLM18000695 2009890391 NLM18000695 105747486 ppf: 307 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Analysis of swallowing sounds using hidden Markov models. aug: au: Aboofazeli M Moussavi Z Aboofazeli, Mohammad Moussavi, Zahra affil: Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg MB R3T 5V6, Canada sug: subj: Deglutition Disorders Diagnosis Deglutition Physiology Probability Auscultation Methods Deglutition Disorders Physiopathology Models, Biological Sound Spectrography ab: In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11 dysphagic subjects were studied. The signals were divided into sequences of 25 ms segments each of which were represented by seven features. The sequences of features were modeled by HMMs. Trained HMMs were used for segmentation of the swallowing sounds into three distinct phases, i.e., initial quiet period, initial discrete sounds (IDS) and bolus transit sounds (BTS). Among the seven features, accuracy of segmentation by the HMM based on multi-scale product of wavelet coefficients was higher than that of the other HMMs and the linear prediction coefficient (LPC)-based HMM showed the weakest performance. In addition, HMMs were used for classification of the swallowing sounds of healthy subjects and dysphagic patients. Classification accuracy of different HMM configurations was investigated. When we increased the number of states of the HMMs from 4 to 8, the classification error gradually decreased. In most cases, classification error for N=9 was higher than that of N=8. Among the seven features used, root mean square (RMS) and waveform fractal dimension (WFD) showed the best performance in the HMM-based classification of swallowing sounds. When the sequences of the features of IDS segment were modeled separately, the accuracy reached up to 85.5%. As a second stage classification, a screening algorithm was used which correctly classified all the subjects but one healthy subject when RMS was used as characteristic feature of the swallowing sounds and the number of states was set to N=8. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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