Sleep syndromes onset detection based on automatic sleep staging algorithm

TL;DR

This paper introduces a new method combining signal processing and deep learning to predict sleep syndromes early by classifying sleep stages from EEG data, demonstrating high accuracy and potential for daily sleep disorder management.

Contribution

A novel approach integrating FFT and deep convolutional LSTM for automated sleep stage classification to predict sleep syndromes early.

Findings

Achieved 86.43% accuracy in sleep stage classification

High recall of 93.32% indicates effective syndrome prediction

F1-score of 89.12 demonstrates balanced precision and recall.

Abstract

In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes, including restless leg syndrome, insomnia, based on an algorithm that is comprised of two modules. A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-frequency information, and a deep convolutional LSTM neural network is trained for sleep stage classification. Automating sleep stages detection from EEG data offers great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is proposed which combines the best of signal processing and statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code evaluation showed impressive results, reaching an accuracy of 86.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss of 0.09.