A1 Journal article (refereed), original research

Sleep Spindle Detection and Prediction Using a Mixture of Time Series and Chaotic Features


Open Access publication

Publication Details
Authors: Hekmatmanesh Amin, Mikaeili Mohammad, Sadeghniiat-Haghighi Khosrow, Wu Huapeng, Handroos Heikki, Martinek Radek, Nazeran Homer
Publisher: VSB-Technical University of Ostrava
Publication year: 2017
Language: English
Related Journal or Series Information: Advances in Electrical and Electronic Engineering
Journal acronym: AEEE
Volume number: 15
Issue number: 3
Start page: 435
End page: 447
Number of pages: 13
ISSN: 1336-1376
eISSN: 1804-3119
JUFO-Level of this publication: 0
Open Access: Open Access publication

Abstract

It is well established that sleep spindles (bursts of oscillatory brain electrical activity) are significant indicators of learning, memory and some disease states. Therefore, many attempts have been made
to detect these hallmark patterns automatically. In this pilot investigation, we paid special attention to nonlinear chaotic features of EEG signals (in combination with linear features) to investigate the detection and prediction of sleep spindles. These nonlinear features included: Higuchi’s, Katz’s and Sevcik’s Fractal Dimensions, as well as the Largest Lyapunov Exponent and Kolmogorov’s Entropy. It was shown that the intensity map of various nonlinear features derived from the constructive interference of spindle signals could improve the detection of the sleep spindles. It was also observed that the prediction of sleep spindles could be facilitated by means of the analysis of these maps. Two well-known classifiers, namely the Multi-Layer Perceptron (MLP) and the K-Nearest Neighbor (KNN) were used to distinguish between spindle and non-spindle patterns. The MLP classifier produced a high discriminative capacity (accuracy = 94:93 %, sensitivity = 94:31 % and specificity = 95:28 %) with significant robustness (accuracy ranging from 91:33 % to 94:93 %, sensitivity varying from 91:20 % to 94:31 %, and specificity extending from 89:79 % to 95:28 %) in separating spindles from non-spindles. This classifier also generated the best results in predicting sleep spindles based on chaotic features. In addition, the MLP was used to find out the best time window for predicting the sleep spindles, with the experimental results reaching 97:96 % accuracy.


Last updated on 2018-19-10 at 07:55