Application of random forest classifier for automatic sleep spindle detection

Chanakya Reddy Patti; Sobhan Salari Shahrbabaki; Chamila Dissanayaka; Dean Cvetkovic

doi:10.1109/BioCAS.2015.7348373

Application of random forest classifier for automatic sleep spindle detection

Chanakya Reddy Patti, Sobhan Salari Shahrbabaki, Chamila Dissanayaka, Dean Cvetkovic

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

10 Citations (Scopus)

Abstract

Sleep spindle detection using supervised learning methods such as Artificial Neural Networks and Support Vector Machines had been researched in the past. Supervised learning methods such as the above are prone to overfitting problems. In this research paper, we explore the detection of sleep spindles using the Random Forest classifier which is known to over fit data to a much lower extent when compared to other supervised classifiers. The classifier was developed using data from 3 subjects and it was tested on data from 12 subjects from the MASS database. A sensitivity of 71.2% and a specificity of 96.73% was achieved using the random forest classifier.

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 Inc.
Number of pages	4
ISBN (Electronic)	9781479972333
DOIs	https://doi.org/10.1109/BioCAS.2015.7348373
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

Keywords

Automatic Detection
Random Forest
Sleep Spindles
Supervised Learning

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10.1109/BioCAS.2015.7348373Licence: In Copyright

Cite this

Patti, C. R., Shahrbabaki, S. S., Dissanayaka, C., & Cvetkovic, D. (2015). Application of random forest classifier for automatic sleep spindle detection. In IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings [7348373] (IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BioCAS.2015.7348373

Patti, Chanakya Reddy ; Shahrbabaki, Sobhan Salari ; Dissanayaka, Chamila et al. / Application of random forest classifier for automatic sleep spindle detection. IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. (IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings).

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abstract = "Sleep spindle detection using supervised learning methods such as Artificial Neural Networks and Support Vector Machines had been researched in the past. Supervised learning methods such as the above are prone to overfitting problems. In this research paper, we explore the detection of sleep spindles using the Random Forest classifier which is known to over fit data to a much lower extent when compared to other supervised classifiers. The classifier was developed using data from 3 subjects and it was tested on data from 12 subjects from the MASS database. A sensitivity of 71.2% and a specificity of 96.73% was achieved using the random forest classifier.",

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author = "Patti, {Chanakya Reddy} and Shahrbabaki, {Sobhan Salari} and Chamila Dissanayaka and Dean Cvetkovic",

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doi = "10.1109/BioCAS.2015.7348373",

language = "English",

series = "IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings",

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Patti, CR, Shahrbabaki, SS, Dissanayaka, C & Cvetkovic, D 2015, Application of random forest classifier for automatic sleep spindle detection. in IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings., 7348373, IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015, Atlanta, United States, 22/10/15. https://doi.org/10.1109/BioCAS.2015.7348373

Application of random forest classifier for automatic sleep spindle detection. / Patti, Chanakya Reddy; Shahrbabaki, Sobhan Salari; Dissanayaka, Chamila et al.

IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. 7348373 (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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AU - Patti, Chanakya Reddy

AU - Shahrbabaki, Sobhan Salari

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AB - Sleep spindle detection using supervised learning methods such as Artificial Neural Networks and Support Vector Machines had been researched in the past. Supervised learning methods such as the above are prone to overfitting problems. In this research paper, we explore the detection of sleep spindles using the Random Forest classifier which is known to over fit data to a much lower extent when compared to other supervised classifiers. The classifier was developed using data from 3 subjects and it was tested on data from 12 subjects from the MASS database. A sensitivity of 71.2% and a specificity of 96.73% was achieved using the random forest classifier.

KW - Automatic Detection

KW - Random Forest

KW - Sleep Spindles

KW - Supervised Learning

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M3 - Conference contribution

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T3 - IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings

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Patti CR, Shahrbabaki SS, Dissanayaka C, Cvetkovic D. Application of random forest classifier for automatic sleep spindle detection. In IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. 7348373. (IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings). doi: 10.1109/BioCAS.2015.7348373

Application of random forest classifier for automatic sleep spindle detection

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