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Automated stenosis classification on invasive coronary angiography using modified dual cross pattern with iterative feature selection

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Abstract

Coronary artery disease (CAD) is a global health concern; the need for early diagnosis cannot be overstated. Many machine learning techniques have been used electrocardiography (ECG) signal to detect CAD and they have used advanced signal processing methods. In this study, we present an automated novel approach for detecting coronary artery stenosis, by integrating the residual exemplar center symmetric dual cross pattern (ResExCSDCP), relief and iterative neighborhood component analysis (RFINCA) techniques. In this work, we collected three coronary angiography images datasets to show general classification ability of the proposed model and these images were gathered from right coronary artery (RCA), left anterior descending artery (LAD), and circumflex artery (CX). The features have been extracted from patches by deploying ResExCSDCP feature extractor. The most informative features have been selected deploying RFINCA and k-nearest neighbor (kNN) has been employed for classification. Our proposed ResExCSDCP and RFINCA-based model attained accuracies of 96.73% ± 1.38, 97.24% ± 1.12%, and 98.51% ± 0.31% for the automatic detection of RCA, LAD, and CX coronary angiography images, respectively. The results demonstrate that our proposal has the potential to assist the cardiologists in making accurate diagnosis and improve the quality of cardiac health.

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Data Availability

The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Wong ND (2014) Epidemiological studies of CHD and the evolution of preventive cardiology. Nat Rev Cardiol 11(5):276. https://doi.org/10.1038/nrcardio.2014.26

    Article  Google Scholar 

  2. Koji Y, Tomiyama H, Ichihashi H, Nagae T, Tanaka N, Takazawa K, Ishimaru S, Yamashina A (2004) Comparison of ankle-brachial pressure index and pulse wave velocity as markers of the presence of coronary artery disease in subjects with a high risk of atherosclerotic cardiovascular disease. Am J Cardiol 94(7):868–872. https://doi.org/10.1016/j.amjcard.2004.06.020

    Article  Google Scholar 

  3. Alizadehsani R, Roshanzamir M, Abdar M, Beykikhoshk A, Khosravi A, Nahavandi S, Plawiak P, Tan RS, Acharya UR (2020) Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries. Expert Syst. https://doi.org/10.1111/exsy.12573

    Article  Google Scholar 

  4. Zerwic JJ, King KB, Wlasowicz GS (1997) Perceptions of patients with cardiovascular disease about the causes of coronary artery disease. Heart Lung 26(2):92–98. https://doi.org/10.1016/s0147-9563(97)90068-6

    Article  Google Scholar 

  5. Gruszczyńska I, Mosdorf R, Sobaniec P, Żochowska-Sobaniec M, Borowska M (2019) Epilepsy identification based on EEG signal using RQA method. Adv Med Sci 64(1):58–64. https://doi.org/10.1016/j.advms.2018.08.003

    Article  Google Scholar 

  6. Nasarian E, Abdar M, Fahami MA, Alizadehsani R, Hussain S, Basiri ME, Zomorodi-Moghadam M, Zhou X, Pławiak P, Acharya UR (2020) Association between work-related features and coronary artery disease: a heterogeneous hybrid feature selection integrated with balancing approach. Pattern Recogn Lett 133:33–40. https://doi.org/10.1016/j.patrec.2020.02.010

    Article  Google Scholar 

  7. Ghiasi MM, Zendehboudi S, Mohsenipour AA (2020) Decision tree-based diagnosis of coronary artery disease: CART model. Comput Meth Prog Bio 192:105400. https://doi.org/10.1016/j.cmpb.2020.105400

    Article  Google Scholar 

  8. Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, Prescott E, Storey RF, Deaton C, Cuisset T (2020) 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 41(3):407

    Article  Google Scholar 

  9. Greulich S, Bruder O, Parker M, Schumm J, Grün S, Schneider S, Klem I, Sechtem U, Mahrholdt H (2012) Comparison of exercise electrocardiography and stress perfusion CMR for the detection of coronary artery disease in women. J Cardiov Magn Reson 14(1):36. https://doi.org/10.1186/1532-429X-14-36

    Article  Google Scholar 

  10. Neumann F-J, Sousa-Uva M, Ahlsson A, Alfonso F, Banning AP, Benedetto U, Byrne RA, Collet J-P, Falk V, Head SJ (2019) 2018 ESC/EACTS guidelines on myocardial revascularization. Eur Heart J 40(2):87–165

    Article  Google Scholar 

  11. Suh YJ, Lee JW, Shin SY, Goo JM, Kim Y, Yong HS (2020) Coronary artery calcium severity grading on non-ECG-gated low-dose chest computed tomography: a multiple-observer study in a nationwide lung cancer screening registry. Eur Radiol:1–8. https://doi.org/10.1007/s00330-020-06707-x

  12. Toğaçar M, Ergen B, Cömert Z (2020) Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks. Med Biol Eng Comput 1–14. https://doi.org/10.1007/s11517-020-02290-x

  13. Aggarwal AK, Jaidka P (2022) Segmentation of crop images for crop yield prediction. Int J Biol Biomed 7:40–44

    Google Scholar 

  14. Xiao J, Suab SA, Chen X, Singh CK, Singh D, Aggarwal AK, Korom A, Widyatmanti W, Mollah TH, Minh HVT (2023) Enhancing assessment of corn growth performance using unmanned aerial vehicles (UAVs) and deep learning. Measurement 214:112764

    Article  Google Scholar 

  15. Thukral R, Arora A, Kumar A (2022) Gulshan Denoising of thermal images using deep neural network. In: Proceedings of International Conference on Recent Trends in Computing: ICRTC 2021. Springer, pp 827–833

  16. Aggarwal AK (2022) Biological Tomato Leaf disease classification using deep learning framework. Int J Biol Biomed Eng 16(1):241–244

    Article  Google Scholar 

  17. Kaur A, Chauhan APS (2019) Aggarwal AK Machine learning based comparative analysis of methods for enhancer prediction in genomic data. In: 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT). IEEE, pp 142–145

  18. Kumar A, Rastogi P, Srivastava P (2015) Design and FPGA implementation of DWT, image text extraction technique. Procedia Comput Sci 57:1015–1025

    Article  Google Scholar 

  19. Aggarwal AK (2022) Learning texture features from glcm for classification of brain tumor mri images using random forest classifier. Trans Signal Process 18:60–63

    Article  Google Scholar 

  20. Kaur A, Chauhan APS, Aggarwal AK (2021) An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network. Expert Syst Appl 186:115686

    Article  Google Scholar 

  21. Kumar A (2023) Study and analysis of different segmentation methods for brain tumor MRI application. Multimed Tools Appl 82(5):7117–7139

    Article  Google Scholar 

  22. Dhyani S, Kumar A, Choudhury S (2023) Arrhythmia disease classification utilizing ResRNN. Biomed Signal Process Control 79:104160

    Article  Google Scholar 

  23. Wan T, Shang X, Yang W, Chen J, Li D, Qin Z (2018) Automated coronary artery tree segmentation in x-ray angiography using improved hessian based enhancement and statistical region merging. Comput Meth Prog Bio 157:179–190. https://doi.org/10.1016/j.cmpb.2018.01.002

    Article  Google Scholar 

  24. Kang D, Dey D, Slomka PJ, Arsanjani R, Nakazato R, Ko H, Berman DS, Li D, Kuo CJ (2015) Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography. J Med Imaging 2(1):014003. https://doi.org/10.1117/1.JMI.2.1.014003

    Article  Google Scholar 

  25. Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Išgum I (2018) A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE T Med Imaging 38(7):1588–1598. https://doi.org/10.1109/TMI.2018.2883807

    Article  Google Scholar 

  26. Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, Boghrati R, Ghandeharioun A, Bahadorian B, Sani ZA (2013) A data mining approach for diagnosis of coronary artery disease. Comput Meth Prog Bio 111(1):52–61. https://doi.org/10.1016/j.cmpb.2013.03.004

    Article  Google Scholar 

  27. Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA (2017) Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput Meth Prog Bio 141:19–26. https://doi.org/10.1016/j.cmpb.2017.01.004

    Article  Google Scholar 

  28. Setiawan NA, Venkatachalam PA, Hani AFM (2020) Diagnosis of coronary artery disease using artificial intelligence based decision support system. arXiv preprint arXiv:200702854

  29. Newman D (1998) UCI repository of machine learning databases, University of California, Irvine. https://archive.ics.uci.edu/datasets. Accessed 1 Jan 2022

  30. Alizadehsani R, Zangooei MH, Hosseini MJ, Habibi J, Khosravi A, Roshanzamir M, Khozeimeh F, Sarrafzadegan N, Nahavandi S (2016) Coronary artery disease detection using computational intelligence methods. Knowl-Based Syst 109:187–197. https://doi.org/10.1016/j.knosys.2016.07.004

    Article  Google Scholar 

  31. Butun E, Yildirim O, Talo M, Tan R-S, Acharya UR (2020) 1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals. Phys Medica 70:39–48. https://doi.org/10.1016/j.ejmp.2020.01.007

    Article  Google Scholar 

  32. Philips (2020) https://www.philips.com.tr/healthcare, Philips medical systems, Nederland B.V. Veenpluis 4–6 5684 PC Best. Access date: 01.06.2022

  33. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc Cvpr IEEE. pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  34. Ding C, Choi J, Tao D, Davis LS (2015) Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE T Pattern Anal 38(3):518–531. https://doi.org/10.1109/TPAMI.2015.2462338

    Article  Google Scholar 

  35. Qin C, Chen X, Luo X, Zhang X, Sun X (2018) Perceptual image hashing via dual-cross pattern encoding and salient structure detection. Inform Sciences 423:284–302. https://doi.org/10.1016/j.ins.2017.09.060

    Article  MathSciNet  Google Scholar 

  36. Tuncer T, Ertam F, Dogan S, Subasi A (2020) An automated daily sport activities and gender recognition method based on novel multi-kernel local diamond pattern using sensor signals. IEEE T Instrum Meas. https://doi.org/10.1109/TIM.2020.3003395

    Article  Google Scholar 

  37. Tuncer T, Dogan S, Özyurt F, Belhaouari SB, Bensmail H (2020) Novel multi center and threshold ternary pattern based method for disease detection method using voice. IEEE Access 8:84532–84540

    Article  Google Scholar 

  38. Akbal E, Tuncer T. FusedTSNet: An automated nocturnal sleep sound classification method based on a fused textural and statistical feature generation network. Appl Acoust 171:107559. https://doi.org/10.1016/j.apacoust.2020.107559

  39. Qin C, Song S, Huang G, Zhu L (2015) Unsupervised neighborhood component analysis for clustering. Neurocomputing 168:609–617. https://doi.org/10.1016/j.neucom.2015.05.064

    Article  Google Scholar 

  40. Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE T Syst Man Cyb 4:580–585. https://doi.org/10.1109/TSMC.1985.6313426

    Article  Google Scholar 

  41. Zreik M, Van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Išgum I (2018) A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging 38(7):1588–1598

    Article  Google Scholar 

  42. Danilov VV, Klyshnikov KY, Gerget OM, Kutikhin AG, Ganyukov VI, Frangi AF, Ovcharenko EA (2021) Real-time coronary artery stenosis detection based on modern neural networks. Sci Rep 11(1):7582

    Article  Google Scholar 

  43. Khozeimeh F, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Alizadehsani R, Tartibi M, Hussain S, Sani ZA, Khodatars M (2022) RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance. Sci Rep 12(1):11178

    Article  Google Scholar 

  44. Jungiewicz M, Jastrzębski P, Wawryka P, Przystalski K, Sabatowski K, Bartuś S (2023) Vision Transformer in stenosis detection of coronary arteries. Expert Syst Appl 228:120234

    Article  Google Scholar 

  45. Han T, Ai D, Li X, Fan J, Song H, Wang Y, Yang J (2023) Coronary artery stenosis detection via proposal-shifted spatial-temporal transformer in X-ray angiography. Comput Biol Med 153:106546

    Article  Google Scholar 

  46. Wu X, Deng L, Li W, Peng P, Yue X, Tang L, Pu Q, Ming Y, Zhang X, Huang X (2023) Deep learning‐based acceleration of compressed sensing for noncontrast‐enhanced coronary magnetic resonance angiography in patients with suspected coronary artery disease. J Magn Reson Imaging. https://doi.org/10.1002/jmri.28653

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Authors and Affiliations

  1. Department Cardiology, Firat University Hospital, Firat University, Elazig, Turkey

    Mehmet Ali Kobat, Tarik Kivrak & Yusuf Akin

  2. School of Business (Information System), University of Southern Queensland, Toowoomba, Australia

    Prabal Datta Barua

  3. Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

    Turker Tuncer & Sengul Dogan

  4. Department of Biomedical Engineering, MIT, Manipal Academy of Higher Education, Manipal, 576104, India

    G. Muralidhar Bairy

  5. Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore

    Ru-San Tan

  6. Duke-NUS Medical School, Singapore, Singapore

    Ru-San Tan

  7. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia

    U. Rajendra Acharya

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  1. Mehmet Ali Kobat
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Correspondence to Turker Tuncer.

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This research has been approved on ethical grounds by the Non-Interventional Research Ethics Board Decisions, Firat University on 29 May 2020 (2020/08–37).

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Kobat, M.A., Barua, P.D., Tuncer, T. et al. Automated stenosis classification on invasive coronary angiography using modified dual cross pattern with iterative feature selection. Multimed Tools Appl 83, 35957–35977 (2024). https://doi.org/10.1007/s11042-023-16697-9

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