Crucial features from CWT analysis of single lead EEG signal to detect sleep arousal

Sleep arousal, characterized by emergence of light sleep or partial wakefulness, often indicates underlying physical disorders, and its detection is crucial for effective patient treatment. While the detection of arousals using multiple signals can be effective, the dependencies on multiple electrodes impose burden on patients. To resolve this issue, some effective features estimated from single-lead electroencephalography (EEG) signals were proposed to detect sleep arousal. Normalized and filtered EEG signals were segmented into 7-s frames, and scalograms were estimated using continuous wavelet transform (CWT). Scalograms and local properties such as frequency, bandwidth, band energy, band energy ratio, maxima, and regularity were derived from the coefficients of CWT. Final classification features were generated using statistical analyses. The most effective features, estimated by correlation coefficients andp-values, were subjected to an artificial neural network to evaluate the performance of the features. The maximum classification performances (86.72% accuracy, 89.26% sensitivity, 86.55% specificity, and 94.87% AUC) were achieved with 100 features. However, sixty specific features were selected from a total of 182 classification features, yielding nearly the same performance as the maximum. Finally, only 14 features were identified as making a pronounced contribution to arousal detection. These findings highlighted the potential of a feature-efficient single-channel EEG-based approach for reliable sleep arousal detection. The proposed framework can be integrated into patient monitoring systems, such as apnea detection modules, to provide a more comprehensive tool for sleep disorder management.