Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

Abstract

To improve applicability of automatic sleep staging an efficient subjectindependent method is proposed with application in sleep-wake detection and in multiclass sleep staging (awake, non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep). In turn, NREM is further divided into three stages denoted here by N1, N2, and N3. To assess the method, polysomnographic (PSG) records of 40 patients from our ISRUC-Sleep dataset, which was scored by an expert clinician in the central hospital of Coimbra, are used. To find the best combination of PSG signals for automatic sleep staging, six electroencephalographic (EEG), two electrooculographic (EOG), and one electromyographic (EMG) channels are analyzed. An extensive set of feature extraction techniques are applied, covering temporal, frequency and timefrequency domains. The maximum overlap wavelet transform (MODWT), a shift invariant transform, was used to extract the features in time-frequency domain. The extracted feature set is transformed and normalized to reduce the effect of extreme values of features. The most discriminative features are selected through a two-step method composed by a manual selection step based on features' histogram analysis followed by an automatic feature selector. The selected feature set is classified using support vector machines (SVMs). The system achieved the best performance by combining 6 channels (C3, C4, O1, left EOG (LOC), right EOG (ROC) and chin EMG (X1)) for sleep-wake detection, and 9 channels (C3, C4, O1, O2, F3, F4, LOC, ROC, X1) for multiclass sleep staging.

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

“…SFSM methods are divided into two main strategies of floating search methods: Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS). Principally, the SFS algorithm starts with an empty feature subset and iteratively inserts one feature that maximizes an objective function [8,44]. In contrast, the SBS algorithm works in a similar manner but it starts with a full feature subset and consecutively removes one feature that minimizes an objective function.…”

“…SFFS is an iterative algorithm that adds a new feature of previously selected features and eliminates a feature from the subset of already selected features using the SFS algorithm. With a slight variation, the SBFS appends a previously deleted feature to the subset of selected features and deletes a feature from the subset of the already selected features in the SBS algorithm [8]. Table 3 summarizes a number of ASSC schemes that use the different classification algorithms shown in Figure 9.…”

“…WT uses functions that are localized in both time and frequency scales [8,33,129]. Because of its flexible way to represent the time-frequency domain of a signal, WT is suitable for non-stationary signal analysis [126].…”

“…In this case, dimensionality reduction and/or feature selection algorithms are used to find the most discriminative features and to keep the dimensionality of features as low as possible [8]. These algorithms have been developed in fields including machine learning and pattern recognition.…”

“…Sleep is the primary function of the brain and plays an essential role in an individual's performance, learning ability and physical movement [1][2][3][4][5][6][7][8][9]. Sleep is a reversible state in which the eyes are closed and several nervous system centers are inactive.…”