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Abstract

Heart rate is constantly changing under the influence of many control signals, as manifested by heart rate variability (HRV). HRV is a nonstationary, irregularly sampled signal, the spectrum of which reveals distinct bands of high, low, very low and ultra-low frequencies (HF, LF, VLF, ULF). VLF and ULF components are the least understood, and their analysis requires HRV records lasting many hours. Moreover, there are still no well-established methods for the reliable extraction of these components. The aim of this work was to select, implement and compare methods which can solve this problem. The performance of multiband filtering (MBF), empirical mode decomposition and the short-time Fourier transform was tested, using synthetic HRV as the ground truth for methods evaluation as well as real data of three patients selected from 25 polysomnographic records with a clear HF component in their spectrograms. The study provided new insights into the components of long-term HRV, including the character of its amplitude and frequency modulation obtained with the Hilbert transform. In addition, the reliability of the extracted HF, LF, VLF and ULF waveforms was demonstrated, and MBF turned out to be the most accurate method, though the signal is strongly nonstationary. The possibility of isolating such waveforms is of great importance both in physiology and pathophysiology, as well as in the automation of medical diagnostics based on HRV.
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Authors and Affiliations

Krzysztof Adamczyk
1
Adam G. Polak
1

  1. Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, B. Prusa Str. 53/55, 50-317 Wrocław, Poland
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Abstract

Sleep apnea syndrome is a common sleep disorder. Detection of apnea and differentiation of its type: obstructive (OSA), central (CSA) or mixed is important in the context of treatment methods, however, it typically requires a great deal of technical and human resources. The aim of this research was to propose a quasi-optimal procedure for processing single-channel electroencephalograms (EEG) from overnight recordings, maximizing the accuracy of automatic apnea or hypopnea detection, as well as distinguishing between the OSA and CSA types. The proposed methodology consisted in processing the EEG signals divided into epochs, with the selection of the best methods at the stages of preprocessing, extraction and selection of features, and classification. Normal breathing was unmistakably distinguished from apnea by the k-nearest neighbors (kNN) and an artificial neural network (ANN), and with 99.98% accuracy by the support vector machine (SVM). The average accuracy of multinomial classification was: 82.29%, 83.26%, and 82.25% for the kNN, SVM and ANN, respectively. The sensitivity and precision of OSA and CSA detection ranged from 55 to 66%, and the misclassification cases concerned only the apnea type.
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Authors and Affiliations

Monika A. Prucnal
1
Adam G. Polak
1

  1. Department of Electronic and Photonic Metrology, Faculty of Electronics, Photonics and Microsystems, Wroclaw University of Science and Technology, Wroclaw, Poland
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Abstract

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.

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Authors and Affiliations

Monika Prucnal
Adam G. Polak
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Abstract

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.

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Authors and Affiliations

Monika A. Prucnal
Adam G. Polak
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Abstract

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.

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Authors and Affiliations

Janusz Mroczka
Adam Polak
Grzegorz Głomb
Tomasz Guszkowski
Ireneusz Jabłoński
Bogdan Kasprzak
Janusz Pękala
Andrzej Stępień
Zbigniew Świerczyński

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