nach oben

Journal of Medical Systems

Erschienen in:

01.08.2011 | Original Paper

New Automated Detection Method of OSA Based on Artificial Neural Networks Using P-Wave Shape and Time Changes

verfasst von: Khaldon Lweesy, Luay Fraiwan, Natheer Khasawneh, Hartmut Dickhaus

Erschienen in: Journal of Medical Systems | Ausgabe 4/2011

Einloggen, um Zugang zu erhalten

Abstract

This paper describes a new method for automatic detection of obstructive sleep apnea (OSA) based on artificial neural networks (ANN) using regular electrocardiogram (ECG) recordings. ECG signals were pre-processed and segmented to extract the P-waves; then three P-wave features were extracted: the P-wave duration (T _p), the P-wave dispersion (P _d), and the time interval from the peak of the P-wave to the R-wave (T _pr). Combinations of the three features were used as features for classification using ANN. For each feature combination studied, 70% of the input data was used for training the ANN, 15% for validating, and 15% for testing the results. Perfect agreement between expert’s scores and the ANN scores was achieved when the ANN was applied on T _p, P _d, and T _pr taken together, while substantial agreements were achieved when applying the ANN on the feature combinations T _p and P _d, and T _p and T _pr.

Penzel, T., McNames, J., de Chazal, P., Raymond, B., Murray, A., and Moody, G., Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Med. Biol. Eng. Comput. 40:402–407, 2002.CrossRef

de Chazal, P., Heneghan, C., Sheridan, E., Reilly, R., Nolan, P., and O’Malley, M., Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans. Biomed. Eng. 50(6):686–696, 2003.CrossRef

Boudaoud, S., Rix, H., Meste, O., Heneghan, C., O’Brien, C., Corrected integral shape averaging applied to obstructive sleep apnea detection from the electrocardiogram. EURASIP J. Adv. Signal. Process. 2007:32570, (2007).

Shamsuzzaman, A., Gersh, B., and Somers, V., Obstructive sleep apnea implications for cardiac and vascular disease. JAMA. 290(14):1906–1914, 2003.CrossRef

Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., and Badr, S., The occurrence of sleep-disordered breathing among middle-aged adults. N. Engl. J. Med. 328:1230–1235, 1993.CrossRef

Young, T., Evans, L., Finn, L., and Palta, M., Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women. Sleep. 20:705–706, 1997.

Boudaoud, S., Heneghan, C., Rix, H., Meste, O., and O’Brien, C., P-wave shape changes observed in the surface electrocardiogram of subjects with obstructive sleep apnoea. Comput. Cardiol. 32:359–362, 2005.CrossRef

Peker, Y., Kraiczi, H., Hedner, J., Loth, S., Johansson, A., and Bende, M., An independent association between obstructive sleep apnoea and coronary artery disease. Eur. Respir. J. 13:179–184, 1999.CrossRef

Peppard, P., Young, T., Palta, M., and Skatrud, J., Prospective study of the association between sleep-disordered breathing and hypertension. N. Engl. J. Med. 342:1378–1384, 2000.CrossRef

10.

Yaggi, H., Concato, J., Kernan, W., Lichtman, J., Brass, L., and Mohsenin, V., Obstructive sleep apnea as a risk factor for stroke and death. N. Engl. J. Med. 353:2034–2041, 2005.CrossRef

11.

Yumino, D., Tsurumi, Y., Takagi, A., Suzuki, K., and Kasanuki, H., Impact of obstructive sleep apnea on clinical and angiographic outcomes following percutaneous coronary intervention in patients with acute coronary syndrome. Am. J. Cardiol. 99(1):26–30, 2007.CrossRef

12.

Boudaoud, S., Rix, H., Blanc, J., Cornilly, J., and Meste, O., Integrated shape averaging applied to AF detection. Comput. Cardiol. 30:125–128, 2003.

13.

Boudaoud, S., Meste, O., and Rix, H., Curve registration for study of P-wave morphing during exercise. Comput. Cardiol. 31:125–128, 2004.

14.

Carlson, J., Johansson, R., and Olsson, S., Classification of electrocardiographic P-Wave morphology. IEEE Trans. Biomed. Eng. 4:401–405, 2001.CrossRef

15.

Dogan, A., Acar, G., Gedikli, O., Ozaydin, M., Nazli, C., Altinbas, A., and Ergene, O., A comparison of P-wave duration and dispersion in patients with short-term and long-term atrial fibrillation. J. Electrocardiol. 36(3):251–255, 2003.CrossRef

16.

Can, I., Aytemir, K., Demir Deniz, A., Ciftci, O., Tokgozoglu, L., Oto, A., and Sahin, A., P-wave duration and dispersion in patients with obstructive sleep apnea. Int. J. Cardiol. 133:e85–e89, 2009.CrossRef

17.

Benzadón, M., Ortega, D., Thierer, J., Torcivia, R., Aldunate, L., de Lima, A., Navia, D., Dorsa, A., Rossi, A., and Trivi, M., Comparison of the amplitude of the P-wave from intracardiac electrocardiogram obtained by means of a central venous catheter filled with saline solution to that obtained via esophageal electrocardiogram. Am. J. Cardiol. 98:978–981, 2006.CrossRef

18.

Turgut, O., Tandogan, I., Yilmaz, M., Yalta, K., Aydin, O., Association of P wave duration and dispersion with the risk for atrial, fibrillation: practical considerations in the setting, of coronary artery disease. Int. J. Cardiol. in press, 2009.

19.

Tsikouris, J., Kluger, J., Song, J., and White, C., Changes in P-wave dispersion and P-wave duration after open heart surgery are associated with the peak incidence of atrial fibrillation. Heart Lung. 30(6):466–471, 2001.CrossRef

20.

Reynolds, E., Seda, G., Ware, J., Vinik, A., Risk, M., and Fishback, N., Autonomic function in sleep apnea patients: increased heart rate variability except during REM sleep in obese patients. Sleep Breath. 11:53–60, 2007.CrossRef

21.

Svozil, D., Kvasnicka, V., and Pospichal, J., Introduction to multi-layer feed-forward neural networks. Chemometr. Intell. Lab. Sys. 39:43–62, 1997.CrossRef

22.

Fausett, L., Fundamentals of neural networks: architectures, algorithms and applications. Prentice Hall, Upper Saddle River, 1994.MATH

23.

Kiymik, M., Akin, M., and Subasi, A., Automatic recognition of alertness level by using wavelet transform and artificial neural network. J. Neurosci. Methods. 139(2):231–240, 2004.CrossRef

24.

Lalitha, V., and Eswaran, C., Automated detection of anesthetic depth levels using chaotic features with artificial neural networks. J. Med. Syst. 31(6):445–452, 2007.CrossRef

25.

Yildiz, A., Akin, M., Poyraz, M., and Kirbas, G., Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Syst. Appl. 36(4):7390–7399, 2009.CrossRef

26.

Cohen, J., A coefficient of agreement for nominal scales. Edu. Physiol. Meas. 20:37–46, 1960.

27.

Sim, J., and Wrigth, C., The Kappa statistics in reliability studies: use, interpretation, and sample size requirements. Phys. Ther. 85(3):257–268, 2005.

Titel: New Automated Detection Method of OSA Based on Artificial Neural Networks Using P-Wave Shape and Time Changes
verfasst von: Khaldon Lweesy
Luay Fraiwan
Natheer Khasawneh
Hartmut Dickhaus
Publikationsdatum: 01.08.2011
Verlag: Springer US
Erschienen in: Journal of Medical Systems / Ausgabe 4/2011
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-009-9409-z

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 4/2011

Technology Enabled Knowledge Exchange: Development of a Conceptual Framework

Critical Elements and Lessons Learnt from the Implementation of an RFID-enabled Healthcare Management System in a Medical Organization

Polarization Sensitive Subcutaneous and Muscular Imaging Based on Common Path Optical Coherence Tomography Using Near Infrared Source

Detecting Sleep Apnea by Heart Rate Variability Analysis: Assessing the Validity of Databases and Algorithms

Computer Based Synchronization Analysis on Sleep EEG in Insomnia

Classifying Epilepsy Diseases Using Artificial Neural Networks and Genetic Algorithm