CASPIAN JOURNAL
MANAGEMENT AND HIGH TECHNOLOGIES
Neurodynamic approach for sleep apnea detection
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Read | Devyatykh Dmitriy V., Gerget Olga M., Berestneva Olga G. Neurodynamic approach for sleep apnea detection // Caspian journal : management and high technologies. — 2014. — №4. — pp. 144-156. |
Devyatykh Dmitriy V. - post-graduate student, National Research Tomsk Polytechnic University, 30 Lenin Avenue, Tomsk, 634050, Russian Federation, ddv.edu@gmail.com
Gerget Olga M. - Ph.D. (Engineering), National Research Tomsk Polytechnic University, 30 Lenin Avenue, Tomsk, 634050, Russian Federation, olgagerget@mail.ru
Berestneva Olga G. - D.Sc. (Engineering), Professor, National Research Tomsk Polytechnic University, 30 Lenin Avenue, Tomsk, 634050, Russian Federation, ogb6@yandex.ru
The urgency is based on need for developing algorithms for detecting obstructive sleep apnea episodes in asthma patients. The main aim of the study was developing neural network model for breathing analyses. It will allow recognition of breath patterns and predicting anomalies that may occur. Class of machine learning algorithms includes many models. Widespread feed forward networks are able to efficiently solve task of classification, but are not quite suitable for processing time-series data. The paper describes results of teaching and testing several types of dynamic or recurrent networks: NARX, Elman, distributed and focused time delay. Methods, used in the study, include machine-learning algorithms such as dynamic neural network architectures: focused time-delay network; distributed time-delay network; non-linear autoregressive exogenous model; using Matlab Neural Network Toolbox 2014a software. For the purpose of research we used dataset, that contained 39 recording. Records were obtained by pulmonology department of Third Tomsk City Hospital; typical recordings were 8-10 hours long and included electrocardiography and oronasal airflow. Frequency of these signals was 11Hz. Results are presented as performance of training and testing processes for various types of dynamic neural networks. In terms of classification accuracy the best results were achieved by non-linear autoregressive exogenous model.
Key words: обструктивное сонное апноэ, динамические нейронные сети, рекуррентные нейронные сети, задержка сигнала, обратные связи, машинное обучение, эластичное сопротивление, распознавание образов, предсказание временных рядов, Obstructive sleep apnea, overlap synd