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 proceedingConference contributionpeer-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 languageEnglish
Title of host publicationIEEE Biomedical Circuits and Systems Conference
Subtitle of host publicationEngineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781479972333
DOIs
Publication statusPublished - 7 Dec 2015
Externally publishedYes
Event11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 - Atlanta, United States
Duration: 22 Oct 201524 Oct 2015

Publication series

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

Conference

Conference11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015
Country/TerritoryUnited States
CityAtlanta
Period22/10/1524/10/15

Keywords

  • Automatic Detection
  • Random Forest
  • Sleep Spindles
  • Supervised Learning

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