کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10345466 698322 2005 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: A feasibility study
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: A feasibility study
چکیده انگلیسی
An artificial neural network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting sleep spindles in band-pass filtered electroencephalograms (EEG), without feature extraction. Following optimum classification schemes, the sensitivity of the network ranges from 79.2% to 87.5%, while the false positive rate ranges from 3.8% to 15.5%. Furthermore, due to the operation of the ANN on time-domain EEG data, there is agreement with visual assessment concerning temporal resolution. Specifically, the total inter-spindle interval duration and the total duration of spindles are calculated with 99% and 92% accuracy, respectively. Therefore, the present method may be suitable for investigations of the dynamics among successive inter-spindle intervals, which could provide information on the role of spindles in the sleep process, and for studies of pharmacological effects on sleep structure, as revealed by the modification of total spindle duration.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods and Programs in Biomedicine - Volume 78, Issue 3, June 2005, Pages 191-207
نویسندگان
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