Signal selection for sleep apnea classification
Pages 661 - 671
Abstract
This paper presents a method for signals and features selection when classifying sleep apnea. This study uses a novel hierarchical parallel particle swarm optimization structure as proposed by the authors previously. In this structure, the swarms are separated into 'masters' and 'slaves' and access to global information is restricted according to their types. This method is used to classify sleep apneic events into apnea or hypopnea. In this study, ten different biosignals are used as the inputs for the system albeit with different features. The most important signals are subsequently determined based on their contribution to classification of the sleep apneic events. The classification method consists of three main parts which are: feature generation, signal selection, and data reduction based on PSO-SVM, and the final classifier. This study can be useful for selecting the best subset of input signals for classification of sleep apneic events, by attention to the trade of between more accuracy of higher number of input signals and more comfortable of less signals for the patient.
References
[1]
Guilleminault, C., van den Hoed, J., Mitler, M.: Overview of the sleep apnea syndromes. In: Guilleminault, C., Dement, W.C. (eds.) Sleep Apnea Syndromes, pp. 1-12. Alan R Liss, New York (1978)
[2]
Flemons, W. W., et al.: Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research. Sleep 22(5), 667-689 (1999)
[3]
Chokroverty, S., et al.: Sleep deprivation and sleepiness. In: Sleep Disorders Medicine, 3rd edn., pp. 22-28. W. B. Saunders, Philadelphia (2009)
[4]
Chokroverty, S.: Overview of sleep & sleep disorders. Indian Journal of Medical Research 131(2), 126-140 (2010)
[5]
Ball, E. M., et al.: Diagnosis and treatment of sleep apnea within the community - The Walla Walla project. Archives of Internal Medicine 157(4), 419-424 (1997)
[6]
Kryger, M. H., et al.: Utilization of health care services in patients with severe obstructive sleep apnea. Sleep 19(9), S111-S116 (1996)
[7]
Stradling, J. R., Crosby, J. H.: Relation between systemic hypertension and sleep hypoxemia or snoring- analysis in 748 men drawn from general-practice. British Medical Journal 300(6717), 75-78 (1990)
[8]
Hoffstein, V.: Snoring. In: Kryger, M. H., Roth, T., Dement, W.C. (eds.) Principles and Practice of Sleep Medicine, pp. 813-826. Saunders, Philadelphia (2000)
[9]
Penzel, T., et al.: Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Medical & Biological Engineering & Computing 40(4), 402-407 (2002)
[10]
Kryger, M. H.: Management of obstractive sleep-apnea. Clinics in Chest Medicine 13(3), 481-492 (1992)
[11]
Cabrero-Canosa, M., Hernandez-Pereira, E., Moret-Bonillo, V.: Intelligent diagnosis of sleep apnea syndrome. IEEE Engineering in Medicine and Biology Magazine 23(2), 72-81 (2004)
[12]
Cabrero-Canosa, M., et al.: An intelligent system for the detection and interpretation of sleep apneas. Expert Systems with Applications 24(4), 335-349 (2003)
[13]
de Chazal, P., et al.: Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Transactions on Biomedical Engineering 50(6), 686-696 (2003)
[14]
Maali, Y., Al-Jumaily, A.: Genetic Fuzzy Approach for detecting Sleep Apnea/Hypopnea Syndrome. In: 2011 3rd International Conference on Machine Learning and Computing, ICMLC 2011 (2011)
[15]
Maali, Y., Al-Jumaily, A.: Automated detecting sleep apnea syndrome: A novel system based on genetic SVM. In: 2011 11th International Conference on Hybrid Intelligent Systems, HIS (2011)
[16]
Yashar, M., Adel, A.-J.: A Novel Partially Connected Cooperative Parallel PSO-SVM Algorithm Study Based on Sleep Apnea Detection. IEEE Congress on Evolutionary Computation 2012, Brisbane, Australia (Accepted in, 2012)
[17]
Schluter, T., Conrad, S.: An approach for automatic sleep stage scoring and apneahypopnea detection. Frontiers of Computer Science 6(2), 230-241 (2012)
[18]
Aksahin, M., et al.: Artificial Apnea Classification with Quantitative Sleep EEG Synchronization. Journal of Medical Systems 36(1), 139-144 (2012)
[19]
Guijarro-Berdinas, B., Hernandez-Pereira, E., Peteiro-Barral, D.: A mixture of experts for classifying sleep apneas. Expert Systems with Applications 39(8), 7084-7092 (2012)
[20]
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995 (1995)
[21]
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: 1995 IEEE International Conference on Neural Networks Proceedings, vol. 1-6, pp. 1942-1948 (1995)
[22]
Fan, S. K. S., Chang, J. M.: Dynamic multi-swarm particle swarm optimizer using parallel PC cluster systems for global optimization of large-scale multimodal functions. Engineering Optimization 42(5), 431-451 (2010)
[23]
Kiatpanichagij, K., Afzulpurkar, N.: Use of supervised discretization with PCA in wavelet packet transformation-based surface electromyrogram classification. Biomedical Signal Processing and Control 4(2), 127-138 (2009)
[24]
Ebrahimi, F., et al.: Automatic Sleep Stage Classification Based on EEG Signals by Using Neural Networks and Wavelet Packet Coefficients. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1-8, pp. 1151-1154 (2008)
[25]
Ebrahimi, F., et al.: Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008 (2008)
[26]
Kempfner, J., et al.: Automatic REM Sleep Detection Associated with Idiopathic REM Sleep Behavior Disorder. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6063-6066 (2011)
[27]
Gubbi, J., Khandoker, A., Palaniswami, M.: Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals. Journal of Clinical Monitoring and Computing 26(1), 1-11 (2012)
[28]
Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999 (1999)
[29]
Engelbrecht, A. P.: Fundamentals of Computational Swarm Intelligence. Wiley (2005)
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- Univ. of Western Sydney: University of Western Sydney
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- University of New South Wales
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Publication History
Published: 04 December 2012
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