EEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD), discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in the automatic classification of sleep stages by an artificial neural network (ANN). 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM), were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA). Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons) were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD) appeared to be the most effective feature extraction method in the analysed problem.
Electroencephalogram (EEG) is one of biomedical signals measured during all-night polysomnography to diagnose sleep disorders, including sleep apnoea. Usually two central EEG channels (C3-A2 and C4- A1) are recorded, but typically only one of them are used. The purpose of this work was to compare discriminative features characterizing normal breathing, as well as obstructive and central sleep apnoeas derived from these central EEG channels. The same methodology of feature extraction and selection was applied separately for the both synchronous signals. The features were extracted by combined discrete wavelet and Hilbert transforms. Afterwards, the statistical indexes were calculated and the features were selected using the analysis of variance and multivariate regression. According to the obtained results, there is a partial difference in information contained in the EEG signals carried by C3-A2 and C4-A1 EEG channels, so data from the both channels should be preferably used together for automatic sleep apnoea detection and differentiation.
Telemedicine is one of the most innovative and promising applications of technology in contemporary medicine. Telemedical systems, a sort of distributed measurement systems, are used for continuous or periodic monitoring of human vital signals in the environment of living. This approach has several advantages in comparison to traditional medical care: e.g. patients experience fewer hospitalizations, emergency room visits, lost time from work, the costs of treatment are reduced, and the quality of life is improved. Currently, chronic respiratory diseases comprise one of the most serious public health problems. Simultaneously patients suffering from these diseases are well suitable for home monitoring. This paper describes the design and technical realization of a telemedical system that has been developed as a platform suitable for monitoring patients with chronic pulmonary diseases and fitted to Polish conditions. The paper focuses on the system's architecture, included medical tests, adopted hardware and software, and preliminary internal evaluation. The performed tests demonstrated good overall performance of the system. At present further work goes on to put it into practice.