Single Channel EEG Based Insomnia Identification Without Sleep Stage Annotations

TL;DR

This study introduces a novel EEG-based method for insomnia detection using a single channel without sleep stage annotations, achieving high accuracy and simplifying sleep monitoring.

Contribution

It presents a new approach combining spectral and temporal features with machine learning for insomnia detection without sleep stage labels.

Findings

Achieved 97.85% accuracy with 1D-CNN classifier.

Selected optimal features using correlation and statistical tests.

Validated on 50 insomnia patients and 50 healthy subjects.

Abstract

This paper proposes a new approach to identifying patients with insomnia using a single EEG channel, without the need for sleep stage annotation. Data preprocessing, feature extraction, feature selection, and classification techniques are used to automatically detect insomnia based on features extracted from spectral and temporal domains, including relative power in the delta, sigma, beta and gamma bands, total power, absolute slow wave power, power ratios, mean, zero crossing rate, mobility, and complexity. A Pearson correlation coefficient, t-test, p-value, and two rules are used to select the optimal set of features for accurately classifying insomnia patients and rejecting negatively affecting features. Classification schemes including a general artificial neural network, convolutional neural network, and support vector machine are applied to the optimal feature set to distinguish between insomnia patients and healthy subjects. The performance of the model is validated using 50 insomnia patients and 50 healthy subjects, with the Fp2 channel and 1D-CNN classifier achieving the highest accuracy and Cohen's kappa coefficient at 97.85% and 94.15%, respectively. The developed model has the potential to simplify current sleep monitoring systems and enable in-home ambulatory monitoring.