Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Journal of Medical Systems
Published in: Journal of Medical Systems 1/2016

01-01-2016 | Transactional Processing Systems

An Intelligent Phonocardiography for Automated Screening of Pediatric Heart Diseases

Authors: Amir A. Sepehri, Armen Kocharian, Azin Janani, Arash Gharehbaghi

Published in: Journal of Medical Systems | Issue 1/2016

Login to get access

Abstract

This paper presents a robust device for automated screening of pediatric heart diseases based on our unique processing method in murmur characterization; the Arash-Band method. The present study modifies the Arash-Band method and employs output of the modified method in conjunction with the two other original techniques to extract indicative feature vectors for the screening. The extracted feature vectors are classified by using the support vector machine method. Results show that the proposed modifications significantly enhances performance of the Arash-Band in terms of the both accuracy and sensitivity as the corresponding effect sizes are sufficiently large. The proposed algorithm has been incorporated into an Android-based tablet to constitute an intelligent phonocardiogram with the automatic screening capability. In order to obtain confidence interval of the accuracy and sensitivity, an inferable statistical test is applied on our database containing the phonocardiogram signals recorded from 263 of the referrals to a hospital. The expected value of the accuracy/sensitivity is estimated to be 87.45 % / 87.29 % with a 95 % confidence interval of (80.19 % – 92.47 %) / (76.01 % – 95.78 %) exhibiting superior performance than a pediatric cardiologist who relies on conventional or even computer-assisted auscultation.
Literature
1.
Ahlstrom, C., Höglund, K., Hult, P., Häggström, J., Kvart, C., and Ask, P., Assessing aortic stenosis using sample entropy of the phonocardiographic signal in dogs. IEEE Trans Biomed Eng 55:2107–2109, 2008.CrossRef
2.
3.
Cho, G. Y., Lee, S. J., and Lee, T. R., An optimized compression algorithm for real-time ecg data transmission in wireless network of medical information systems. J Med Syst 39:161, 2015.PubMedCrossRef
4.
Cortes, C., and Vapnik, V., Support vector networks. Mach Learn 20:273–297, 1995.
5.
de Vos, J. P., and Blanckenberg, M. M., Automated pediatric auscultation. IEEE Trans Biomed Eng 54:244–252, 2007.PubMedCrossRef
6.
Debjais, F., Durand, L. G., Ouo, Z., and Guarlo, R., Time-frequency analysis of heart murmurs. part ii: optimization of time-frequency representations and performance evaluation. Med Biol Eng Comput 35:480–485, 1997.CrossRef
7.
DeGroff, C. G., Bhatikar, S., Hertzberg, J., Shandas, R., Valdes-Cruz, L., and Mahajan, R. L., Artificial neural network–based method of screening heart murmurs in children. Circulation 103:2711–2716, 2001.PubMedCrossRef
8.
Dutoit, T., and Marques, F., Applied Signal Processing. New York: Springer Science, 2009.CrossRef
9.
Efron, B., and Tibshirani R. J., An Introduction to the Bootstrap. NY: CHAPMAN & HALL/CRC, 1993.CrossRef
10.
Gavrovska, A., Bogdanovic, V., Reljina, I., and Reljin, B., Automatic heart sound detection in pediatric patients without electrocardiogram reference via pseudo-affine wigner–ville distribution and haar wavelet lifting. Comput Meth Prog Biomed 113:515–528, 2014.CrossRef
11.
Gharehbaghi, A., Dutoit, T., Sepehri, A., Hult, P., and Ask, P., An automatic tool for pediatric heart sounds segmentation. In: Proc. Computing in Cardiology, Vol. 38, pp. 37–40 2011.
12.
Gharehbaghi, A., Dutoit, T., Ask, P., and Sörnmo, L., Detection of systolic ejection click using time growing neural network. Med Eng Phys 36:477–483, 2014.PubMedCrossRef
13.
Gharehbaghi, A., Ask, P., and Babic, A., A pattern recognition framework for detecting dynamic changes on cyclic time series. Pattern Recogn 48(3):696–708, 2015a.CrossRef
14.
Gharehbaghi, A., Ask, P., Lindén, M., and Babic, A., A novel model for screening aortic stenosis using phonocardiogram. In: 16th Nordic-Baltic Conference on Biomedical Engineering, Springer International Publishing, IFMBE Proceedings, Vol. 48, pp. 48–51, 2015b.
15.
Gharehbaghi, A., Ask, P., Nylander, E., Janeröt-Sjoberg, B., Ekman, I., Lindén, M., and Babic, A., A hybrid model for diagnosing sever aortic stenosis in asymptomatic patients using phonocardiogram. In: World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada, Springer International Publishing, IFMBE Proceedings, Vol. 51, pp. 1006–1009, 2015c.
16.
Gharehbaghi, A., Borga, M., Sjöberg, B. J., and Ask, P., A novel method for discrimination between innocent and pathological heart murmurs. Med Eng Phys 37(7):674–682, 2015d.PubMedCrossRef
17.
Gharehbaghi, A., Dutoit, T., Sepehri, A. A., Kocharian, A., and Lindén, M., A novel method for screening children with isolated bicuspid aortic valve. Cardiovasc Eng Technol 7:1–11, 2015e.
18.
Gharehbaghi, A., Ekman, I., Ask, P., Nylander, E., and Janerot-Sjoberg, B., Assessment of aortic valve stenosis severity using intelligent phonocardiography. Int J Cardiol 198:58–60, 2015f.PubMedCrossRef
19.
Gharehbaghi, A., Sepehri, A. A., Kocharian, A., and Lindén, M., An intelligent method for discrimination between aortic and pulmonary stenosis using phonocardiogram. In: World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada, IFMBE Proceedings, Vol. 51, pp. 1010–1013, 2015g.
20.
Hollander, M., and Wolfe, D. A., Nonparametric Statistical Methods. NJ: John Wiley, 1999.
21.
Jacob, C. STATISTICAL POWER ANALYSIS for the BEHAVIORAL SCIENCES. NJ: Lawrence Erlbaum Associates, 1988.
22.
Jarque, C. M., and Bera, A. K., A test for normality of observations and regression residuals. Int Stat Rev 55:163–170, 1987.CrossRef
23.
Kecman, V. Learning and Soft Computing: Support Vector Machine, Neural Networks and Fuzzy Logic Models. Cambridge: MIT Press, 2002.
24.
Kumar, V., Cotran, R. S., Robbins, S. L., Basic Pathology: W.B. Saunders Company, 1997.
25.
Martis, R. J., Krishnan, M. M. R., Chakraborty, C., Pal, S., Sarkar, D., Mandana, K. M., and Ray, A. K., Automated screening of arrhythmia using wavelet based machine learning techniques. J Med Syst 36:677–688, 2012.PubMedCrossRef
26.
Martín, J. A. C., Martiínez-Peérez, B., de la Torre-Diíez, I., and López-Coronado, M., Economic impact assessment from the use of a mobile app for the self-management of heart diseases by patients with heart failure in a spanish region. J Med Syst 38:96, 2014.CrossRef
27.
Çomak, E., and Arslan, A., A biomedical decision support system using ls-svm classifier with an efficient and new parameter regularization procedure for diagnosis of heart valve diseases. J Med Syst 36:549–556, 2012.PubMedCrossRef
28.
O’Toole, J. D., Reddy, P. S., Curtiss, E. I., and Shaver, J. A., The mechanism of splitting of the second heart sound in atrial septal defect. Circulation 56:1047–1053, 1977.PubMedCrossRef
29.
Pollonini, L., Rajan, N. O., Xu, S., Madala, S., and Dacso, C. C., A novel handheld device for use in remote patient monitoring of heart failure patients–design and preliminary validation on healthy subjects. J Med Syst 36:653–659, 2012.PubMedCrossRef
30.
Quiceno-Manrique, A. F., Godino-Llorente, J. I., Blanco-Velasco, M., and Castellanos-Dominguez, G., Selection of dynamic features based on time–frequency representations for heart murmur detection from phonocardiographic signals. Ann Biomed Eng 38:118–137, 2010.PubMedCrossRef
31.
Rakovic, P., Sejdic, E., Stankovic, L. J., and Jiang, J., Time-frequency signal processing approaches with applications to heart sound analysis. In: Proc. Comput. Cardiol., Vol. 33, pp. 197–200, 2006.
32.
Sekiya, T., Watanable, A., and Saito, M., The use of modified constellation graph method for computer-aided classification of congenital heart diseases. IEEE Trans Biomed Eng 38:814–820, 1991.PubMedCrossRef
33.
Sepehri, A. A., Hancq, J., Dutoit, T., Gharehbaghi, A., Kocharian, A., and Kiani, A., Computerized screening of children congenital heart diseases. Comput Meth Prog Biomed 92:186–192, 2008.CrossRef
34.
Sepehri, A. A., Gharehbaghi, A., Dutoit, T., Kocharian, A., and Kiani, A., A novel method for pediatric heart sound segmentation without using the ECG. Comput Meth Prog Biomed 99:43–48, 2010.CrossRef
35.
Sörnmo, L., and Laguna, P., Bioelectrical Signal Processing in Cardiac and Neurological Applications. Amsterdam: Elsevier (Academic Press), 2005.
36.
kyung Suh, M., Chen, C. A., Woodbridge, J., Tu, M. K., Jung In Kim, A. N., Evangelista, L. S., and Sarrafzadeh, M., A remote patient monitoring system for congestive heart failure. J Med Syst 35:1165–1179, 2011.CrossRef
37.
Tilkian, A. G., Understanding Heart Sounds and Murmurs. Philadelphia: W.B. Sanders Company, 1984.
38.
Tseng, K. C., Hsu, C. L., and Chuang, Y. H., Designing an intelligent health monitoring system and exploring user acceptance for the elderly. J Med Syst 37:9967, 2013.PubMedCrossRef
39.
Ullah, S., Higgins, H., Braem, B., Latre, B., Blondia, C., Moerman, I., Saleem, S., Rahman, Z., and Kwak, K. S., A comprehensive survey of wireless body area networks. J Med Syst 36:1065–1094, 2012.PubMedCrossRef
40.
Watrous, R. L., Thompson, W. R., and Ackerman, S. J., The impact of computer-assisted auscultation on physician referrals of asymptomatic patient with heart murmurs. Clin Cardiol 31:79–83, 2008.PubMedCrossRef
41.
Wood, J. C., and Barry, D. T., Time-frequency analysis of the first heart sound. IEEE Eng Med Biol Mag 95:144–151, 1995.CrossRef
42.
Zang, X., Durand, L. G., Senhadji, L., Lee, H. C., and Coatrieux, J. L., Time–frequency scaling transformation of the phonocardiogram based of the matching pursuit method. IEEE Trans Biomed Eng 45:972–979, 1998.CrossRef
Metadata
Title
An Intelligent Phonocardiography for Automated Screening of Pediatric Heart Diseases
Authors
Amir A. Sepehri
Armen Kocharian
Azin Janani
Arash Gharehbaghi
Publication date
01-01-2016
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 1/2016
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-015-0359-3

Other articles of this Issue 1/2016

Journal of Medical Systems 1/2016 Go to the issue

Mobile Systems

The Effect of Screen Size on Mobile Phone User Comprehension of Health Information and Application Structure: An Experimental Approach

Systems-Level Quality Improvement

A Novel Framework for Medical Web Information Foraging Using Hybrid ACO and Tabu Search

Patient Facing Systems

A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People

Systems-Level Quality Improvement

A Method to Differentiate Mild Cognitive Impairment and Alzheimer in MR Images using Eigen Value Descriptors

Education & Training

A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images

Systems-Level Quality Improvement

Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres