NDLI: Automatic detection of A phases of the Cyclic Alternating Pattern during sleep

Content Provider	IEEE Xplore Digital Library
Author	Mariani, S. Bianchi, A.M. Manfredini, E. Rosso, V. Mendez, M.O. Parrino, L. Matteucci, M. Grassi, A. Cerutti, S. Terzano, M.G.
Copyright Year	2010
Description	Author affiliation: Politecnico di Milano, Dept. of Information Engineering, P.zza Leonardo da Vinci 32, 20133, Milan, Italy (Matteucci, M.) \|\| Politecnico di Milano, Dept. of Biomedical Engineering, P.zza Leonardo da Vinci 32, 20133, Milan, Italy (Mariani, S.; Bianchi, A.M.; Manfredini, E.; Cerutti, S.) \|\| Sleep Disorders Center of “Ospedale Maggiore di Parma”, via Gramsci 14, 43126, Italy (Rosso, V.; Parrino, L.; Grassi, A.; Terzano, M.G.) \|\| Facultad de Ciencias, Diagonal Sur S/N, Zona Universitaria, San Luis Potosi, Mexico (Mendez, M.O.)
Abstract	This study aimed to develop an automatic algorithm to detect the activation phases (A phases) of the Cyclic Alternating Pattern. The sleep EEG microstructure of 4 adult, healthy subjects was scored by a sleep medicine expert. Features were calculated from each of the six EEG bands (low delta, high delta, theta, alpha, sigma and beta), and three additional characteristics were computed: the Hjorth activity in the low delta and high delta bands, and the differential variance of the raw EEG signal. The correlation between couples of features was analyzed to find redundancies for the automatic analysis. The features were used to train an Artificial Neural Network to automatically find the A phases of CAP. The data were divided into training, validation and testing set, and the visual scoring provided by the clinician was used as the desired output. The statistics on the second by second classification show an average sensitivity equal to 76%, specificity equal to 83% and accuracy equal to 82%. The results obtained are encouraging, since an automatic classification of the A phases could benefit the practice in clinics, preventing the physician from the time-consuming activity of visually scoring the sleep microstructure over the whole eight-hour sleep recordings. Moreover, it would provide an objective criterion capable of overcoming the problems of inter-scorer variability.
Starting Page	5085
Ending Page	5088
File Size	1945752
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5626211
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sleep Electroencephalography Artificial neural networks Microstructure Classification algorithms Correlation Accuracy
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
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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