Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs’ labels (prediction) in the contextual output. While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways. First, it leverages the dependency among consecutive sleep epochs while surpassing the problems experienced with the common classification schemes. Second, even with a single model, the framework has the capacity to produce multiple decisions, which are essential in obtaining a good performance as in ensemble-of-models methods, with very little induced computational overhead. Probabilistic aggregation techniques are then proposed to leverage the availability of multiple decisions. To illustrate the efficacy of the proposed framework, we conducted experiments on two public datasets: Sleep-EDF Expanded (Sleep-EDF), which consists of 20 subjects, and Montreal Archive of Sleep Studies (MASS) dataset, which consists of 200 subjects. The proposed framework yields an overall classification accuracy of 82.3% and 83.6%, respectively. We also show that the proposed framework not only is superior to the baselines based on the common classification schemes but also outperforms existing deep-learning approaches. To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. This framework provides avenues for further studies of different neural-network architectures for automatic sleep staging.

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Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

In this paper, we propose an automatic sleep-scoring method combining multiscale entropy (MSE) and autoregressive (AR) models for single-channel EEG and to assess the performance of the method comparatively with manual scoring based on full polysomnograms. This is the first time that MSE has ever been applied to sleep scoring. All-night polysomnograms from 20 healthy individuals were scored using the Rechtschaffen and Kales rules. The developed method analyzed the EEG signals of C3-A2 for sleep staging. The results of automatic and manual scorings were compared on an epoch-by-epoch basis. A total of 8480 30-s sleep EEG epochs were measured and used for performance evaluation. The epoch-by-epoch comparison was made by classifying the EEG epochs into five states (Wake/REM/S1/S2/SWS) by the proposed method and manual scoring. The overall sensitivity and kappa coefficient of MSE alone are 76.9% and 0.65, respectively. Moreover, the overall sensitivity and kappa coefficient of our proposed method of integrating MSE, AR models, and a smoothing process can reach the sensitivity level of 88.1% and 0.81, respectively. Our results show that MSE is a useful and representative feature for sleep staging. It has high accuracy and good home-care applicability because a single EEG channel is used for sleep staging.

Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models

An ensemble system for automatic sleep stage classification using single channel EEG signal

http://karimjerbi.com/PDF/Lajnef_etal_JNeurosciMethods_2015.pdf

Kristína Šušmáková

Papers

Discrimination ability of individual measures used in sleep stages classification