NDLI: Support vector regression correlates single-sweep evoked brain potentials to gastrointestinal symptoms in diabetes mellitus patients

Content Provider	IEEE Xplore Digital Library
Author	Graversen, C. Frokjaer, J.B. Brock, C. Drewes, A.M. Farina, D.
Copyright Year	2012
Description	Author affiliation: Dept. of Gastroenterology & Hepatology, Aalborg Hosp., Aalborg, Denmark (Drewes, A.M.) \|\| Dept. of Gastroenterology & Radiol., Aalborg Hosp., Aalborg, Denmark (Graversen, C.) \|\| Dept. of Radiol., Aalborg Hosp., Aalborg, Denmark (Frokjaer, J.B.) \|\| Dept. of Neurorehabilitation Eng., Georg-August Univ., Gottingen, Germany (Farina, D.) \|\| Dept. of Gastroenterology, Aalborg Hosp., Aalborg, Denmark (Brock, C.)
Abstract	Diabetes mellitus (DM) is a multi-factorial and complex disease causing autonomic neuropathy and gastrointestinal symptoms in some patients. The neural mechanisms behind these symptoms are poorly understood, but it is believed that both peripheral and central mechanisms are involved. To gain further knowledge of the central mechanisms, the aim of this study was to identify biomarkers for the altered brain activity in type-1 DM patients compared to healthy volunteers (HV), and to correlate the obtained biomarkers to clinical patient scores. The study included 14 DM patients and 15 HV, with brain activity recorded as multi-channel electroencephalography evoked brain potentials (EPs) elicited by painful electrical stimulations in the esophagus. The single-sweep EPs were decomposed by an optimized discrete wavelet transform (DWT), and averaged for each channel. The DWT features from the DM patients were discriminated from the HV by a support vector machine (SVM) applied in regression mode. For the optimal DWT, the discriminative features were extracted and the SVM regression value representing the overall alteration of the EP was correlated to the clinical scores. A classification performance of 86.2% (P=0.01) was obtained by applying a majority voting scheme to the 5 best performing channels. The biomarker was identified as decreased theta band activity. The regression value was correlated to symptoms reported by the patients (P=0.04). The methodology is an improvement of the present approach to study central mechanisms in diabetes mellitus, and may provide a future application for a clinical tool to optimize treatment in individual patients.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	5242
Ending Page	5245
File Size	642343
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6347176
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Support vector machines Pain Diabetes Feature extraction Biomarkers Discrete wavelet transforms
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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