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.
[1] 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
[2] C. Grigoraş, D. Boişteanu, and V. Grigoraş, "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
[3] R. S. Leung, and T. D. Bradley, "Sleep apnea and cardiovascular
disease", Am. J. Respir. Crit. Care Med., 164 (2001), 2147-2165.
[4] 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
[5] 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.
[6] A. H.Khandoker, C.K. Karmakar, and M. Palaniswami, "Screening
OSAS from ECG recordings using SVM", Computers in
Cardiology,34(2007), 485-488.
[7] 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.
[8] 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.
[9] C. Grigoras, and A. Lazar, "Hysteretic artificial neural network for EEG
data representation", IFMBE Proceedings, 11(2005), pp. Prague, 4450-
4455.
[10] 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)
[11] M. A. Kramer, "Nonlinear principal component analysis using autoassociative
neural networks", AIChE Journal, 37,(1991), 233-243
[12] 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.
[1] 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
[2] C. Grigoraş, D. Boişteanu, and V. Grigoraş, "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
[3] R. S. Leung, and T. D. Bradley, "Sleep apnea and cardiovascular
disease", Am. J. Respir. Crit. Care Med., 164 (2001), 2147-2165.
[4] 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
[5] 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.
[6] A. H.Khandoker, C.K. Karmakar, and M. Palaniswami, "Screening
OSAS from ECG recordings using SVM", Computers in
Cardiology,34(2007), 485-488.
[7] 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.
[8] 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.
[9] C. Grigoras, and A. Lazar, "Hysteretic artificial neural network for EEG
data representation", IFMBE Proceedings, 11(2005), pp. Prague, 4450-
4455.
[10] 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)
[11] M. A. Kramer, "Nonlinear principal component analysis using autoassociative
neural networks", AIChE Journal, 37,(1991), 233-243
[12] 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.
@article{"International Journal of Medical, Medicine and Health Sciences:56987", author = "Carmen Grigoraş and Victor Grigoraş and Daniela Boişteanu", title = "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.", keywords = "Artificial neural networks, feature extraction,obstructive sleep apnea syndrome, pattern recognition, signalprocessing.", volume = "2", number = "8", pages = "282-5", }