Abstract
In the past several studies have evaluated the human sleep onset (wake to sleep transition) using the electroencephalographic (EEG) measurements. This paper has evaluated the detection accuracy of sleep stages for multiple features based on the EEG alpha activity, during SO in healthy, insomniac and schizophrenic patients. The features include topographic features such as Directed Transfer Function, Full frequency DTF, Welch Coherence, Minimum Variance Distortionless Response Coherence and Partial Directed Coherence. Sleep stages Wake, NREM (Non-rapid Eye Movement) stages 1 and 2 were classified using Artificial Neural Networks (ANN) classifier and evaluated using classification accuracy. The results suggest that using topographic set of features yield an agreement of 81.3 % with the whole database classification of human expert.
Original language | English |
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Title of host publication | IEEE Biomedical Circuits and Systems Conference |
Subtitle of host publication | Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Number of pages | 4 |
ISBN (Electronic) | 9781479972333 |
DOIs | |
Publication status | Published - 7 Dec 2015 |
Externally published | Yes |
Event | 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 - Atlanta, United States Duration: 22 Oct 2015 → 24 Oct 2015 |
Publication series
Name | IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings |
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Conference
Conference | 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 |
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Country/Territory | United States |
City | Atlanta |
Period | 22/10/15 → 24/10/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.