Sleep onset detection with multiple EEG alpha-band features: Comparison between healthy, insomniac and schizophrenic patients

Chamila Dissanayaka; Dean Cvetkovic; Chanakya Reddy Patti; Sobhan Salari Shahrbabaki; Beena Ahmed; Claudia Schilling; Michael Schredl

doi:10.1109/BioCAS.2015.7348362

Sleep onset detection with multiple EEG alpha-band features: Comparison between healthy, insomniac and schizophrenic patients

Chamila Dissanayaka, Dean Cvetkovic, Chanakya Reddy Patti, Sobhan Salari Shahrbabaki, Beena Ahmed, Claudia Schilling, Michael Schredl

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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
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
Number of pages	4
ISBN (Electronic)	9781479972333
DOIs	https://doi.org/10.1109/BioCAS.2015.7348362
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

Conference

Conference	11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015
Country/Territory	United States
City	Atlanta
Period	22/10/15 → 24/10/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

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10.1109/BioCAS.2015.7348362Licence: In Copyright

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Dissanayaka, C., Cvetkovic, D., Patti, C. R., Shahrbabaki, S. S., Ahmed, B., Schilling, C., & Schredl, M. (2015). Sleep onset detection with multiple EEG alpha-band features: Comparison between healthy, insomniac and schizophrenic patients. In IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings Article 7348362 (IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BioCAS.2015.7348362

Dissanayaka, Chamila ; Cvetkovic, Dean ; Patti, Chanakya Reddy et al. / Sleep onset detection with multiple EEG alpha-band features : Comparison between healthy, insomniac and schizophrenic patients. IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings. Institute of Electrical and Electronics Engineers, 2015. (IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings).

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title = "Sleep onset detection with multiple EEG alpha-band features: Comparison between healthy, insomniac and schizophrenic patients",

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.",

author = "Chamila Dissanayaka and Dean Cvetkovic and Patti, {Chanakya Reddy} and Shahrbabaki, {Sobhan Salari} and Beena Ahmed and Claudia Schilling and Michael Schredl",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 ; Conference date: 22-10-2015 Through 24-10-2015",

year = "2015",

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doi = "10.1109/BioCAS.2015.7348362",

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Dissanayaka, C, Cvetkovic, D, Patti, CR, Shahrbabaki, SS, Ahmed, B, Schilling, C & Schredl, M 2015, Sleep onset detection with multiple EEG alpha-band features: Comparison between healthy, insomniac and schizophrenic patients. in IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings., 7348362, IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings, Institute of Electrical and Electronics Engineers, 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015, Atlanta, United States, 22/10/15. https://doi.org/10.1109/BioCAS.2015.7348362

Sleep onset detection with multiple EEG alpha-band features: Comparison between healthy, insomniac and schizophrenic patients. / Dissanayaka, Chamila; Cvetkovic, Dean; Patti, Chanakya Reddy et al.
IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings. Institute of Electrical and Electronics Engineers, 2015. 7348362 (IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Sleep onset detection with multiple EEG alpha-band features

T2 - 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015

AU - Dissanayaka, Chamila

AU - Cvetkovic, Dean

AU - Patti, Chanakya Reddy

AU - Shahrbabaki, Sobhan Salari

AU - Ahmed, Beena

AU - Schilling, Claudia

AU - Schredl, Michael

PY - 2015/12/7

Y1 - 2015/12/7

N2 - 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.

AB - 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.

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Dissanayaka C, Cvetkovic D, Patti CR, Shahrbabaki SS, Ahmed B, Schilling C et al. Sleep onset detection with multiple EEG alpha-band features: Comparison between healthy, insomniac and schizophrenic patients. In IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings. Institute of Electrical and Electronics Engineers. 2015. 7348362. (IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings). doi: 10.1109/BioCAS.2015.7348362

Sleep onset detection with multiple EEG alpha-band features: Comparison between healthy, insomniac and schizophrenic patients

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