Application of random forest classifier for automatic sleep spindle detection | IEEE Conference Publication | IEEE Xplore

Application of random forest classifier for automatic sleep spindle detection


Abstract:

Sleep spindle detection using supervised learning methods such as Artificial Neural Networks and Support Vector Machines had been researched in the past. Supervised learn...Show More

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.
Date of Conference: 22-24 October 2015
Date Added to IEEE Xplore: 07 December 2015
ISBN Information:
Conference Location: Atlanta, GA, USA

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