Year 2017, Volume 2, Issue 1, Pages 29 - 36 2017-02-25

In this study, a decision-support system is presented to aid cardiologists during the diagnosis and to create a base for a new diagnosis system which separates two classes (CAD and no-CAD patients) using an electrocardiogram (ECG).

24 hour filtered ECG signals from PhysioNet were used. 15 second short-term ECG segments were extracted from 24 hour ECG signals to increase the number of samples and to provide a convenient transformation in a short period of time. The Hilbert-Huang Transform, which is effective on non-linear and non-stationary signals, was used to extract the features from short-term ECG signals. Instinct Mode Function (IMF) was extracted by applying Empirical Mode Decomposition to short-term ECG signals. The Hilbert Transform (HT) was applied to each IMF to obtain instantaneous frequency characteristics of the signal. Dataset was created by extracting statistical features from HT applied to IMF. Deep Belief Networks (DBN) which have a common use in Deep Learning algorithms were used as the classifier. DBN classification accuracy in the diagnosis of the CAD is discussed. The extracted dataset was tested using the 10-fold cross validation method.

The test characteristics (sensitivity, accuracy and specificity) that are the basic parameters of independent testing in the medical diagnostic systems were calculated using this validation method. Short-term ECG signals of CAD patients and no-CAD groups were classified by the DBN with the rates of 98.05%, 98.88% and 96.02%, for accuracy, specificity and sensitivity, respectively.

The DBN model achieved higher accuracy rates than the Neural Network classifier.

Coronary Artery Disease, CAD, Deep Belief Networks, DBN, Deep Learning Algorithm, Hilbert-Huang Transform
  • M. Gabriel Khan, Rapid ECG Interpretation(Contemporary Cardiology), 3rd edition. Humana Press, 2007.
  • U. F. Chan, W. W. Chan, S. H. Pun, M. I. Vai, and P. U. Mak, “Flexible implementation of front-end bioelectric signal amplifier using fpaa for telemedicine system,” in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2007, pp. 3721–3724.
  • Y. Özbay and G. Tezel, “A new method for classification of ECG arrhythmias using neural network with adaptive activation function,” Digit. Signal Process., vol. 20, no. 4, pp. 1040–1049, 2010.
  • “CAD Information.” [Online]. Available: [Accessed: 01-Jan-2016].
  • G. K. Hansson, “Inflammation, atherosclerosis, and coronary artery disease,” N. Engl. J. Med., vol. 352, no. 16, pp. 1685–1695, 2005.
  • K. Polat and S. Güneş, “A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS,” Comput. Methods Programs Biomed., vol. 88, no. 2, pp. 164–174, 2007.
  • R. Yilmaz and R. Demirbag, “P-wave dispersion in patients with stable coronary artery disease and its relationship with severity of the disease,” J. Electrocardiol., vol. 38, no. 3, pp. 279–284, 2005.
  • İ. Babaoglu, O. Findik, and E. Ülker, “A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine,” Expert Syst. Appl., vol. 37, no. 4, pp. 3177–3183, 2010.
  • Y. N. Devi and S.Anto, “An Evolutionary-Fuzzy Expert System for the Diagnosis of Coronary Artery Disease,” , An Evol. Fuzzy Expert Syst. Diagnosis Coron. Artery Dis., vol. 3, no. 4, pp. 1478–1484, 2014.
  • M. G. Tsipouras, T. P. Exarchos, D. I. Fotiadis, A. P. Kotsia, K. V. Vakalis, K. K. Naka, and L. K. Michalis, “Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling,” IEEE Trans. Inf. Technol. Biomed., vol. 12, no. 4, pp. 447–458, 2008.
  • Noor Akhmad Setiawan, P. A. Venkatachalam, and Ahmad Fadzil M.Hani, “Diagnosis of coronary artery disease using artificial intelligence based decision support system,” Proc. Int. Conf. Man-Machine Syst., 2009.
  • N. Ghadiri Hedeshi and M. Saniee Abadeh, “Coronary artery disease detection using a fuzzy-boosting PSO approach,” Comput. Intell. Neurosci., vol. 2014, 2014.
  • M. C. Colak, C. Colak, H. Kocatürk, S. Sağiroğlu, and I. Barutçu, “Predicting coronary artery disease using different artificial neural network models.,” Anadolu Kardiyol. Derg., vol. 8, no. 4, pp. 249–254, 2008.
  • A. Rajkumar and M. G. S. Reena, “Diagnosis Of Heart Disease Using Datamining Algorithm,” Glob. J. Comput. Sci. Technol., vol. 10, no. 10, pp. 38–43, 2010.
  • N. Lavesson, A. Halling, and M. Freitag, “Classifying the Severity of an Acute Coronary Syndrome by Mining Patient Data,” 26th Annu. Work. Swedish Artif. Intell. Soc., vol. 35, pp. 55–63, 2009.
  • M. Shouman, T. Turner, and R. Stocker, “Using data mining techniques in heart disease diagnosis and treatment,” Japan-Egypt Conf. Electron. Commun. Comput., pp. 173–177, 2012.
  • M. Akay, W. Welkowitz, J. L. Semmlow, and J. Kostis, “Application of the ARMA method to acoustic detection of coronary artery disease,” Med. Biol. Eng. Comput., vol. 29, no. 4, pp. 365–372, 1991.
  • U. R. Acharya, O. Faust, V. Sree, G. Swapna, R. J. Martis, N. A. Kadri, and J. S. Suri, “Linear and nonlinear analysis of normal and CAD-affected heart rate signals,” Comput. Methods Programs Biomed., vol. 113, no. 1, pp. 55–68, 2014.
  • H. G. Lee, K. Y. Noh, and K. H. Ryu, “Mining biosignal data: Coronary artery disease diagnosis using linear and nonlinear features of HRV,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, vol. 4819 LNAI, pp. 218–228.
  • S. Dua, X. Du, S. Vinitha Sree, and V. I. Thajudin Ahamed, “Novel classification of coronary artery disease using heart rate variability analysis,” J. Mech. Med. Biol., vol. 12, no. 4, p. 1240017, 2012.
  • H. G. L. H. G. Lee, K. Y. N. K. Y. Noh, and K. H. R. K. H. Ryu, “A Data Mining Approach for Coronary Heart Disease Prediction using HRV Features and Carotid Arterial Wall Thickness,” 2008 Int. Conf. Biomed. Eng. Informatics, vol. 1, 2008.
  • H. C. W. Kim, S. Jin, Y. Park, “A study on development of multi-parametric measure of heart rate variability diagnosing cardiovascular disease,” IFMBE Proc. 14, pp. 3480–3483, 2007.
  • M. Karimi, R. Amirfattahi, S. Sadri, and S. A. Marvasti, “Noninvasive detection and classification of coronary artery occlusions using wavelet analysis of heart sounds with neural networks,” in Medical Applications of Signal Processing, 2005. The 3rd IEE International Seminar on (Ref. No. 2005-1119), 2005, pp. 117–120.
  • S. Arafat, M. Dohrmann, and M. Skubic, “Classification of Coronary Artery Disease Stress ECGs using Uncertainty Modeling,” in 2005 ICSC Congress on Computational Intelligence Methods and Applications, 2005, pp. 1–4.
  • R. Alizadehsani, J. Habibi, M. J. Hosseini, H. Mashayekhi, R. Boghrati, A. Ghandeharioun, B. Bahadorian, and Z. A. Sani, “A data mining approach for diagnosis of coronary artery disease,” Comput. Methods Programs Biomed., vol. 111, no. 1, pp. 52–61, 2013.
  • Z. Zhao and C. Ma, “An Intelligent System for Noninvasive Diagnosis of Coronary Artery Disease with EMD-TEO and BP Neural Network,” in 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, 2008, pp. 631–635.
  • U. R. Acharya, S. V. Sree, M. Muthu Rama Krishnan, N. Krishnananda, S. Ranjan, P. Umesh, and J. S. Suri, “Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images,” Comput. Methods Programs Biomed., vol. 112, no. 3, pp. 624–632, 2013.
  • D. Giri, U. Rajendra Acharya, R. J. Martis, S. Vinitha Sree, T. C. Lim, T. Ahamed, and J. S. Suri, “Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform,” Knowledge-Based Syst., vol. 37, pp. 274–282, 2013.
  • S. Patidar, R. B. Pachori, and U. Rajendra Acharya, “Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals,” Knowledge-Based Syst., vol. 82, pp. 1–10, 2015.
  • N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. A Math. Phys. Eng. Sci., vol. 454, no. 1971, pp. 903–995, 1998.
  • U. Maji, M. Mitra, and S. Pal, “Automatic Detection of Atrial Fibrillation Using Empirical Mode Decomposition and Statistical Approach,” Procedia Technol., vol. 10, pp. 45–52, 2013.
  • G. Altan, A. Yayik, Y. Kutlu, S. Yildirim, and E. Yildirim, “Analyse of Congestive Heart Failure Using Hilbert- Huang Transform,” Dokuz Eylul Univ. Eng. Sci., vol. 16, pp. 94–103, 2014.
  • G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, “Deep Neural Networks for Acoustic Modeling in Speech Recognition,” IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82–97, 2012.
  • S. M. Siniscalchi, D. Yu, L. Deng, and C. H. Lee, “Exploiting deep neural networks for detection-based speech recognition,” Neurocomputing, vol. 106, pp. 148–157, 2013.
  • C. Farabet, C. Couprie, L. Najman, and Y. Lecun, “Learning hierarchical features for scene labeling,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1915–1929, 2013.
  • P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. Lecun, “Pedestrian detection with unsupervised multi-stage feature learning,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, pp. 3626–3633.
  • Q. Le and T. Mikolov, “Distributed Representations of Sentences and Documents,” Int. Conf. Mach. Learn. - ICML 2014, vol. 32, pp. 1188–1196, 2014.
  • I. Sutskever, O. Vinyals, and Q. Le, “Sequence to sequence learning with neural networks,” Adv. Neural Inf. …, p. 9, 2014.
  • H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, “Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations,” Proc. 26th Annu. Int. Conf. Mach. Learn. ICML 09, vol. 2008, pp. 1–8, 2009.
  • P. W. Mirowski, Y. LeCun, D. Madhavan, and R. Kuzniecky, “Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG,” in Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, 2008, pp. 244–249.
  • P. W. Mirowski, D. Madhavan, and Y. Lecun, “Time-delay neural networks and independent component analysis for eeg-based prediction of epileptic seizures propagation,” in Advancement of Artificial Intelligence Conference, 2007, pp. 1892–1893.
  • D. Wang and Y. Shang, “Modeling Physiological Data with Deep Belief Networks.,” Int. J. Inf. Educ. Technol., vol. 3, no. 5, pp. 505–511, 2013.
  • P. Drotár, J. Gazda, and Z. Smékal, “An experimental comparison of feature selection methods on two-class biomedical datasets,” Comput. Biol. Med., vol. 66, pp. 1–10, 2015.
  • P. Tamilselvan and P. Wang, “Failure diagnosis using deep belief learning based health state classification,” Reliab. Eng. Syst. Saf., vol. 115, pp. 124–135, 2013.
  • E. de la Rosa and W. Yu, “Randomized algorithms for nonlinear system identification with deep learning modification,” Inf. Sci. (Ny)., p. -, 2015.
  • M. Längkvist, L. Karlsson, and A. Loutfi, “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognit. Lett., vol. 42, no. 1, pp. 11–24, 2014.
  • Y. Bengio and O. Delalleau, “Justifying and generalizing contrastive divergence,” Neural Comput., vol. 21, no. 6, pp. 1601–1621, 2009.
  • G. Hinton, G. Hinton, T. Sejnowski, and T. Sejnowski, Learning and relearning in Boltzmann machines, vol. 1. 1986.
  • A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. . Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,” Circulation, vol. 101, pp. 215–220, 2000.
  • N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971. pp. 903–995, 1998.
  • G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets.,” Neural Comput., vol. 18, no. 7, pp. 1527–54, 2006.
  • Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy Layer-Wise Training of Deep Networks,” Adv. Neural Inf. Process. Syst., vol. 19, no. 1, p. 153, 2007.
  • Y. Liu, S. Zhou, and Q. Chen, “Discriminative deep belief networks for visual data classification,” Pattern Recognit., vol. 44, no. 10–11, pp. 2287–2296, 2011.
  • R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. 2000.
Subjects Engineering
Journal Section Makaleler

Author: Gokhan ALTAN
Country: Turkey

Bibtex @review { ejens293042, journal = {European Journal of Engineering and Natural Sciences}, issn = {}, eissn = {2458-8156}, address = {CNR Çevre}, year = {2017}, volume = {2}, pages = {29 - 36}, doi = {}, title = {Diagnosis of Coronary Artery Disease Using Deep Belief Networks}, key = {cite}, author = {ALTAN, Gokhan} }
APA ALTAN, G . (2017). Diagnosis of Coronary Artery Disease Using Deep Belief Networks. European Journal of Engineering and Natural Sciences, 2 (1), 29-36. Retrieved from
MLA ALTAN, G . "Diagnosis of Coronary Artery Disease Using Deep Belief Networks". European Journal of Engineering and Natural Sciences 2 (2017): 29-36 <>
Chicago ALTAN, G . "Diagnosis of Coronary Artery Disease Using Deep Belief Networks". European Journal of Engineering and Natural Sciences 2 (2017): 29-36
RIS TY - JOUR T1 - Diagnosis of Coronary Artery Disease Using Deep Belief Networks AU - Gokhan ALTAN Y1 - 2017 PY - 2017 N1 - DO - T2 - European Journal of Engineering and Natural Sciences JF - Journal JO - JOR SP - 29 EP - 36 VL - 2 IS - 1 SN - -2458-8156 M3 - UR - Y2 - 2017 ER -
EndNote %0 European Journal of Engineering and Natural Sciences Diagnosis of Coronary Artery Disease Using Deep Belief Networks %A Gokhan ALTAN %T Diagnosis of Coronary Artery Disease Using Deep Belief Networks %D 2017 %J European Journal of Engineering and Natural Sciences %P -2458-8156 %V 2 %N 1 %R %U
ISNAD ALTAN, Gokhan . "Diagnosis of Coronary Artery Disease Using Deep Belief Networks". European Journal of Engineering and Natural Sciences 2 / 1 (February 2017): 29-36.