Human Sleep Scoring Based on K-Nearest Neighbors

Shahnawaz Qureshi,Seppo Karrila,Sirirut Vanichayobon

doi:10.3906/elk-1805-12

Shahnawaz Qureshi, Seppo Karrila + Show 1 more

Open Access

https://doi.org/10.3906/elk-1805-12

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Abstract

Human sleep is one of the essential indicators that gauge the overall health and well-being. Presently, it is common for people to face issues related to sleep. Various biomedical signals including electroencephalogram (EEG), electrooculography (EMG), and electrooculography (EOG) are utilized in the diagnosis and during the treatment of sleep disorder cases. An automatic classification to diagnose sleep problems can help in the analysis of sleep EEG data. In this current study, an effort is made to classify the sleep stages from a single EEG channel (C4-A1) based on K-nearest neighbors (K-NN) with three alternative distance metrics. The Euclidean distance is the most commonly used distance measure in K-NN, and no prior study of sleep EEG data has inspected the classification performance of K-NN with various distance measures. Therefore, this study aimed to investigate whether the distance function affects the performance of K-NN in the classification of sleep data. Euclidean, Manhattan and Chebyshev distance measures were individually tested with K-NN classification, and their performances were compared based on accuracy, sensitivity, specificity, F-measure, Kappa statistic and computation time for both Rechtschaffen & Kales and American Academy of Sleep Medicine standard labelings of the sleep stages. The experimental results show that the Manhattan distance function with K = 5 was the best choice for classification of the sleep stages, achieving 98.46 % and 98.77 % correct rates for the two labelings with comparatively rapid computations.

Highlights

Sleep is an integral part of our life, and it plays a pivotal role in our daily activities
To access the analysis among sleep stages, epoch by epoch analysis was performed by using sensitivity, specificity, and F-measure
Results and discussion for Manhattan distance with K = 5 The MD metric with K = 5 was employed with the true labels from Rechtschaffen & Kales (R&K) and Academy of Sleep Medicine (AASM) standards, and this outperformed the results with the other distance alternatives in terms of sensitivity, specificity, F-measure and computation time

Summary

Introduction

Sleep is an integral part of our life, and it plays a pivotal role in our daily activities. Sleep analysis by monitoring the activities of the human by electroencephalogram (EEG), electromyography (EMG), and electrooculography (EOG) helps in identifying sleep-related problems [2]. The EEG method is considered one of the best techniques to record the human brain activity [3,4,5]. Sleep comprises a sequence of distinct physiological stages that can be distinguished from EEG related features. Neuroscientists have identified different types of brain signals such as alpha, beta, delta, and theta, associated with the sleep stages. According to Rechtschaffen & Kales (R&K) rule [6], there are six different stages of sleep namely: awake (W), non-rapid eye movement (N-REM) stage, N-REM stage 2, N-REM stage 3, N-REM stage 4, and the rapid eye movement (REM).

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