Abstract
Highly accurate classification of sleep stages is possible based on EEG signals alone. However, reliable and high quality acquisition of these signals in the home environment is difficult. Instead, electrocardiogram (ECG) and Respiratory (Res) signals are easier to record and may offer a practical alternative for home monitoring of sleep. Therefore, automatic sleep staging was performed using ECG, Res (thoracic excursion) and EEG signals from 31 nocturnal recordings of the Sleep Heart Health Study (SHHS) polysomnography Database. Feature vectors were extracted from 0.5 min (standard) epochs of sleep data by time-domain, frequency domain, time-frequency and nonlinear methods and optimized by using the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) method. These features were then classified by using a SVM. Classification based upon EEG features produced a Correct Classification Ratio CCR=0.92. In comparison, features derived from ECG signals alone, that is the combination of Heart Rate Variability (HRV), and ECG-Derived Respiration (EDR) signals produced a CCR=0.54, while those features based on the combination of HRV and (thoracic) Res signals resulted in a CCR=0.57. Overall comparison of the results based on standard epochs of EEG signals with those obtained from 5-minute (long) epochs of cardiorespiratory signals, revealed that acceptable CCR=0.81 and discriminative capacity (Accuracy=89.32%, Specificity=92.88% and Sensitivity=78.64%) were also achievable when using optimal feature sets derived from long epochs of the latter signals in sleep staging. In addition, it was observed that the presence of some artifacts (like bigeminy) in the cardiorespiratory signals reduced the accuracy of automatic sleep staging more than the artifacts that contaminated the EEG signals.
Highlights
Speaking we can categorize 2 types of sleep: Non-Rapid Eye Movement (NREM) sleep, and Rapid Eye Movement (REM) sleep
The Support Vector Machine -Recursive Feature Elimination (SVM-Recursive Feature Elimination (RFE)) ranking method was applied to 34 features extracted from EEG signals
These synchronization ratios could serve as useful features and would inform our future algorithm enhancements in performing automatic sleep staging based on cardiorespiratory signals alone
Summary
Speaking we can categorize 2 types of sleep: Non-Rapid Eye Movement (NREM) sleep, and Rapid Eye Movement (REM) sleep. The NREM sleep can be in turn sub-categorized as Stages 1 through 4, with Stage 1 being the lightest and Stage 4 being the deepest sleep state [1]. In the AASM sleep standards, the NREM stage is sub-grouped into three Stages of N1, N2 and N3 [2]. Polysomnography (PSG) or “multiple recording of physiological signals during sleep“ is widely used as the "gold standard" clinical technique for the evaluation of sleep and diagnosis of its disorders. In PSG signals such as EEG, ECG, EMG, EOG, c 2017 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING. Respiration (Res) and others are recorded simultaneously during sleep. Among these signals, EEG is the most commonly used for sleep staging [3]
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