Commenced in January 2007
Paper Count: 30075
Cross Signal Identification for PSG Applications
Abstract:The standard investigational method for obstructive sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG), which consists of a simultaneous, usually overnight recording of multiple electro-physiological signals related to sleep and wakefulness. This is an expensive, encumbering and not a readily repeated protocol, and therefore there is need for simpler and easily implemented screening and detection techniques. Identification of apnea/hypopnea events in the screening recordings is the key factor for the diagnosis of OSAS. The analysis of a solely single-lead electrocardiographic (ECG) signal for OSAS diagnosis, which may be done with portable devices, at patient-s home, is the challenge of the last years. A novel artificial neural network (ANN) based approach for feature extraction and automatic identification of respiratory events in ECG signals is presented in this paper. A nonlinear principal component analysis (NLPCA) method was considered for feature extraction and support vector machine for classification/recognition. An alternative representation of the respiratory events by means of Kohonen type neural network is discussed. Our prospective study was based on OSAS patients of the Clinical Hospital of Pneumology from Iaşi, Romania, males and females, as well as on non-OSAS investigated human subjects. Our computed analysis includes a learning phase based on cross signal PSG annotation.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334642Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1191
 B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers", 5th Annual ACM Workshop on COLT, Pittsburgh, PA. ACM Press (1992) 144-152
 C. Grigora┼ƒ, D. Boi┼ƒteanu, and V. Grigora┼ƒ, "Intelligent search in physiological signals database for obstructive sleep apnea correlated with cardiovascular disease records", Revista medico-chirurgicalâ, 111- 2-Supl.2 (2007), 101-104
 R. S. Leung, and T. D. Bradley, "Sleep apnea and cardiovascular disease", Am. J. Respir. Crit. Care Med., 164 (2001), 2147-2165.
 T. Young, M. Platt, J. Dempsey, J. Skatrud, S. Weber, and S. Badr, "The occurrence of sleep-disordered breathing among middle-aged adults", N Engl, J. Med.,vol. 328, pp. 1230-1235, 1993treatment, Respir. Med., 98 (2004), 968-976
 C. Guilleminault, S.J.Connolly, R.Winkle, K.Melvin, and A.Tilkian, "Cyclical variation of the heart rate in sleep apnea syndrome. Mechanisme and usefulness of 24h electrocardiography as a screening technique", Lancet,I (1984), 126-131.
 A. H.Khandoker, C.K. Karmakar, and M. Palaniswami, "Screening OSAS from ECG recordings using SVM", Computers in Cardiology,34(2007), 485-488.
 P. de Chazal, C.Henegan, E. Sheridan, R.rayley, P Nolan, and M.O-Malley, "Automated Processing of the Single Lead ECG for the detection of OSA", IEEE-T.Biomed.Eng., 50-6 (2003), 686-696.
 M.F.Hilton, R.A. Bates, K.R.Godfrey, M.J. Chappell, and R.M.Cayton, "Evaluation of frequency and time-frequency spectral analysis of HRV as a diagnostic marker of the SAS", Med. Biol. Eng. Comput., 37-6 (1999), 760-769.
 C. Grigoras, and A. Lazar, "Hysteretic artificial neural network for EEG data representation", IFMBE Proceedings, 11(2005), pp. Prague, 4450- 4455.
 C. Grigoras, and V. Grigoras, "Classifying neural activity by means of nonlinear principal component analysis representations", Proc. 5th European Symp. on Biomedical Engineering, ESBME2006, Patras, Greece, ( 2006)
 M. A. Kramer, "Nonlinear principal component analysis using autoassociative neural networks", AIChE Journal, 37,(1991), 233-243
 G.Kimeldorf, and G. Wahba, "A correspondence between Bayesian estimation of stochastic processes and smoothing by splines", Annals of Mathematical Statistics, 41-2 (1970), 495-502.