Sleep Apnea Detection Based on Dynamic Neural Networks

One of widespread breath disruption that takes place during sleep is apnea, during this anomaly people are not able to get enough oxygen. The article describes method for breathing analyses that is based on neural network that allows recognition of breath patterns and predicting anomalies that may occur. Class of machine learning algorithms includes lots of models, widespread feed forward networks are able to 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.

[1]  W. Orr,et al.  Sleep apnea, hypersomnolence, and upper airway obstruction secondary to adenotonsillar enlargement. , 1977, Archives of otolaryngology.

[2]  Eric Laciar,et al.  Sleep apnea detection based on spectral analysis of three ECG - derived respiratory signals , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Martin A. Riedmiller,et al.  Advanced supervised learning in multi-layer perceptrons — From backpropagation to adaptive learning algorithms , 1994 .

[4]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[5]  C. Lee Giles,et al.  Extracting and Learning an Unknown Grammar with Recurrent Neural Networks , 1991, NIPS.

[6]  Sandiway Fong,et al.  Natural Language Grammatical Inference with Recurrent Neural Networks , 2000, IEEE Trans. Knowl. Data Eng..

[7]  Necmettin Sezgin,et al.  Classification of Sleep Apnea through Sub-band Energy of Abdominal Effort Signal Using Wavelets + Neural Networks , 2010, Journal of Medical Systems.

[8]  Bonnie K. Lind,et al.  Relation of sleep-disordered breathing to cardiovascular disease risk factors: the Sleep Heart Health Study. , 2001, American journal of epidemiology.

[9]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[10]  Comparison of the ANN based Classification Accuracy for Real Time Sleep Apnea Detection Methods , 2012, BioMed 2012.

[11]  F. Ebrahimi,et al.  Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  V. Moret-Bonillo,et al.  Intelligent diagnosis of sleep apnea syndrome , 2004, IEEE Engineering in Medicine and Biology Magazine.