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Research Article

MI-CSBO: a hybrid system for myocardial infarction classification using deep learning and Bayesian optimization

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Received 08 Sep 2023, Accepted 11 Jul 2024, Published online: 24 Jul 2024

References

  • Arif M, Malagore IA, Afsar FA. 2012. Detection and localization of myocardial infarction using K-nearest neighbor classifier. J Med Syst. 36(1):279–289. doi: 10.1007/s10916-010-9474-3.
  • Arshad QA, Ali M, Hassan S-u, Chen C, Imran A, Rasul G, Sultani W. 2022. A dataset and benchmark for malaria life-cycle classification in thin blood smear images. Neural Comput Applic. 34(6):4473–4485. doi: 10.1007/s00521-021-06602-6.
  • Baloglu UB, Talo M, Yildirim O, San Tan R, Acharya U. 2019. Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recog Lett. 122:23–30. doi: 10.1016/j.patrec.2019.02.016.
  • Boateng S, Sanborn T. 2013. Acute myocardial infarction. Dis Mon. 59(3):83–96. doi: 10.1016/j.disamonth.2012.12.004.
  • Daly M, Finlay D, Guldenring D, Nugent C, Tomlin A, Smith B, Adgey A, Harbinson M. 2012. Detection of acute coronary occlusion in patients with acute coronary syndromes presenting with isolated ST-segment depression. Eur Heart J Acute Cardiovasc Care. 1(2):128–135. doi: 10.1177/2048872612448977.
  • Deng M, Huang X, Liang Z, Lin W, Mo B, Liang D, Ruan S, Chen J. 2023. Classification of cardiac electrical signals between patients with myocardial infarction and normal subjects by using nonlinear dynamics features and different classification models. Biomed Signal Process Control. 79:104105. pagesdoi: 10.1016/j.bspc.2022.104105.
  • Dev Sharma L, Kumar Sunkaria R. 2018. Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. SIViP. 12(2):199–206. doi: 10.1007/s11760-017-1146-z.
  • Diker A, Avci E, Cömert Z, Avci D, Kaçar E, Serhatlioğlu İ. 2018. Classification of ECG signal by using machine learning methods. 2018 26th Signal Processing and Communications Applications Conference (SIU). p. 1–4. doi: 10.1109/SIU.2018.8404298.
  • Di̇ker A. 2022. An efficient model of residual based convolutional neural network with Bayesian optimization for the classification of malarial cell images. Comput Biol Med. 148:105635. doi: 10.1016/j.compbiomed.2022.105635.
  • Ehnheart. [accessed on 2022 Nov 30]. https://ehnheart.org/wp-content/uploads/2023/07/CVD-Statistics.pdf.
  • Eliot MA, Braunwald E. 2004. Akut miyokard infarktüsü. İç Hastalıkları Prensipleri‘nde. 1. Cilt, 15. Baskı: İstanbul Nobel Tıp Kitabevi; p. 1383–7.
  • Fang R, Lu CC, Chuang CT, Chang WH. 2022. A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification. Comput Meth Program Biomed. 219:106762. doi: 10.1016/j.cmpb.2022.106762.
  • Farooq A, Anwar S, Awais M, Rehman S. 2017. A deep CNN based multi-class classification of Alzheimer’s disease using MRI. Proceedings of the 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China. pp. 1–6. doi: 10.1109/IST.2017.8261460.
  • Fathil MFM, Md Arshad MK, Gopinath SCB, Hashim U, Adzhri R, Ayub RM, Ruslinda AR, Nuzaihan M N M, Azman AH, Zaki M, et al. 2015. Diagnostics on acute myocardial infarction: cardiac troponin biomarkers. Biosens Bioelectron. 70:209–220. doi: 10.1016/j.bios.2015.03.037.
  • Gibbons RJ, Valeti US, Araoz PA, Jaffe AS. 2004. The quantification of infarct size. J Am Coll Cardiol. 44(8):1533–1542. doi: 10.1016/j.jacc.2004.06.071.
  • He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. Proceeding of IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778.
  • He T, Droppo J. 2016. Exploiting LSTM structure in deep neural networks for speech recognition. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China. pp. 5445–5449. doi: 10.1109/ICASSP.2016.7472718.
  • He Z, Yuan S, Zhao J, Du B, Yuan Z, Alhudhaif A, Alenezi F, Althubiti SA. 2022. A novel myocardial infarction localization method using multi-branch DenseNet and spatial matching-based active semi-supervised learning. Info Sci J. 606:649–668. doi: 10.1016/j.ins.2022.05.070.
  • Karthik R, Hariharan M, Anand S, Mathikshara P, Johnson A, Menaka R. 2020. Attention embedded residual CNN for disease detection in tomato leaves. Appl Soft Comput J. 86:105933. doi: 10.1016/j.asoc.2019.105933.
  • Khatar Z, Bentaleb D. 2024. Enhanced ECG signal features transformation to RGB matrix imaging for advanced deep learning classification of myocardial infarction and cardiac arrhythmia. Multimed Tools Appl. 1–21. doi: 10.1007/s11042-024-19352-z.
  • Kumar V, Abbas AK, Aster JC, Cotran R. 2020. Pathologic basis of disease. 9th ed. Elsevier Publication. p. 483–521. ISBN: 9780323296465
  • Kuş Z. 2019. Mikrokanonikal Optimizasyon Algoritması ile Konvolüsyonel Sinir Ağlarinda Hiper Parametrelerin Optimize Edilmesi, Graduate Education Institute [Master of Thesis]. Fatih Sultan Mehmet Vakıf Üniversitesi.
  • Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H. 2017. Feature selection: a data perspective. ACM Comput Surv. 50(6):1–45. doi: 10.1145/3136625.
  • Martin H, Izquierdo W, Cabrerizo M, Cabrera A, Adjouadi M. 2021. Near real-time single-beat myocardial infarction detection from single-lead electrocardiogram using long short-term memory neural network. Biomed Signal Proces Control. 68:102683. pagesdoi: 10.1016/j.bspc.2021.102683.
  • Narayanan BN, Ali RA, Hardie RC. 2019. Performance analysis of machine learning and deep learning architectures for malaria detection on cell images. SPIE Applications of Machine Learning. pp. 29. doi: 10.1117/12.2524681.
  • Pride YB, Tung P, Mohanavelu S, Zorkun C, Wiviott SD, Antman EM, Giugliano R, Braunwald E, Gibson CM. 2010. Angiographic and clinical outcomes among patients with acute coronary syndromes presenting with isolated anterior ST-segment depression. JACC Cardiovasc Interv. 3(8):806–811. doi: 10.1016/j.jcin.2010.05.012.
  • Rahman SSMM, Chen Z, Lalande A, Decourselle T, Cochet A, Pommier T, Cottin Y, Salomon M, Couturier R. 2023. Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: a quick response. PLoS One. 18(5):e0285165. doi: 10.1371/journal.pone.0285165.
  • Reinstadler SJ, Klug G, Feistritzer H-J, Metzler B, Mair J. 2015. Testing in acute myocardial infarction: ready for routine use? Dis Markers. 2015:614145. doi: 10.1155/2015/614145.
  • Sadhukhan D, Pal S, Mitra M. 2018. Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG data. IEEE Trans Instrum Meas. 67(10):2303–2313. doi: 10.1109/TIM.2018.2816458.
  • Safdarian N, Dabanloo NJ, Attarodi G. 2014. A new pattern recognition method for detection and localization of myocardial infarction using t-wave integral and total integral as extracted features from one cycle of ECG signal. JBiSE. 07(10):818–824. doi: 10.4236/jbise.2014.710081.
  • Sun L, Lu Y, Yang K, Li S. 2012. ECG analysis using multiple instance learning for myocardial infarction detection. IEEE Trans Biomed Eng. 59(12):3348–3356. doi: 10.1109/TBME.2012.2213597.
  • Timmis A, Vardas P, Townsend N, Torbica A, Katus H, De Smedt D. 2021. European Society of Cardiology: cardiovascular disease statistics. Atlas Writing Group Euro Soc Cardiol. 43(8):716–799. doi: 10.1093/eurheartj/ehab892.
  • Wang K, Asinger RW, Marriott HJL. 2003. ST-segment elevation in conditions other than acute myocardial infarction. N Engl J Med. 349(22):2128–2135. doi: 10.1056/NEJMra022580.
  • White HD, Chew DP. 2008. Acute myocardial infarction. Lancet. 16372(9638):570–584. doi: 10.1016/S0140-6736(08)61237-4.
  • Yoo Y, Baek J-G. 2018. A novel image feature for the remaining useful lifetime prediction of bearings based on continuous wavelet transform and convolutional neural network. Appl Sci. 8(7):1102. doi: 10.3390/app8071102.

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