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2020 | OriginalPaper | Chapter

Automatic Detection of Sleep Spindles Using Time Domain Features

Authors : Ghania Fatima, Omar Farooq, Shikha Singh

Published in: Advances in Data and Information Sciences

Publisher: Springer Singapore

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Abstract

Sleep spindles are one of the unique rhythmic activities observed in sleep electroencephalogram (EEG). Detecting sleep spindles visually by sleep spindles is a difficult task as high skills and efforts are required. In this study, a methodology for detecting sleep spindles automatically has been proposed using band-pass filtering. Time domain features (energy and entropy) are used for classification. The extracted features have been used as inputs to Linear, Quadratic, and Mahalanobis classifier for spindle detection. Results show that the proposed method yields best results when using a Mahalanobis Classifier. The accuracy, sensitivity, and specificity recorded are 91.11%, 84.86%, and 89.73%, respectively. The sensitivity obtained in this study is more than most of the work done in sleep spindles detection using the same dataset.

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Metadata
Title
Automatic Detection of Sleep Spindles Using Time Domain Features
Authors
Ghania Fatima
Omar Farooq
Shikha Singh
Copyright Year
2020
Publisher
Springer Singapore
Book
Advances in Data and Information Sciences

Print ISBN: 978-981-15-0693-2

Electronic ISBN: 978-981-15-0694-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-0694-9_51