Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates

Abstract

This work presents a new methodology for automated sleep stage identification in neonates based on the time frequency distribution of single electroencephalogram (EEG) recording and artificial neural networks (ANN). Wigner-Ville distribution (WVD), Hilbert-Hough spectrum (HHS) and continuous wavelet transform (CWT) time frequency distributions were used to represent the EEG signal from which features were extracted using time frequency entropy. The classification of features was done using feed forward back-propagation ANN. The system was trained and tested using data taken from neonates of post-conceptual age of 40 weeks for both preterm (14 recordings) and fullterm (15 recordings). The identification of sleep stages was successfully implemented and the classification based on the WVD outperformed the approaches based on CWT and HHS. The accuracy and kappa coefficient were found to be 0.84 and 0.65 respectively for the fullterm neonates' recordings and 0.74 and 0.50 respectively for preterm neonates' recordings.

Newborn sleep stage identification using multiscale entropy

“…The results from the proposed method achieve accuracy levels comparable to previous studies in the literature. Fraiwan et al [24] reported accuracy was in the range of 80-90 %. They used time frequency analysis to extract features from the EEG recording.…”

Unobtrusive sleep state measurements in preterm infants – A review

“…However, their feature Modality fusion for a single sensor (e.g. EEG) is already in use for preterm infants to improve monitoring robustness and also specifically for preterm infant sleep analysis; for example, by using classic feature extraction and machine learning tools (93). To date, extensive modality fusion on the sensor level, for example, the fusion of EEG and body movement information, has not been performed in preterm infant sleep analysis.…”

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

“…The results of the drowsiness detection approach indicated a high accuracy and precision of 98.01% and 97.91%, respectively. Fraiwan et al [29] developed a methodology for automatic sleep stage scoring based on extracted entropy features of the Wigner-Ville Distribution (WVD), Hilbert-Hough Spectrum (HHS) and Continuous Wavelet Transform (CWT) using a single EEG channel and ANN. The classification accuracy of the WVD was 84%, which outperformed the other approaches using the other features in their work.…”

“…In biomedical signal processing, it is crucial to determine the noise, artifacts and any trends present in the raw signals so that their influence in the feature extraction stage can be minimized [29,30,46,49,50]. EEG recordings have a wide variety of artifacts, some having a technical origin and others having a physiological origin mixed together with the brain signal [41,45,57,108,129].…”

“…WT is a well-known form of the time-frequency distribution and has been widely deployed in signal processing and analysis of many fields over the past two decades [29,30,107]. WT uses functions that are localized in both time and frequency scales [8,33,129].…”