Prediction of Arrhythmias and Acute Myocardial Infarctions using Machine Learning
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References
K.-Y. Chin, K.-F. Lee, and Y.-L. Chen, “Using an interactive ubiquitous learning system to enhance authentic learning experiences in a cultural heritage course,” Interactive Learning Environments, vol. 26, no. 4, pp. 444–459, 2018. [Online]. Available: https://doi.org/10.1080/10494820.2017.1341939
F. P. Mota, F. P. de Toledo, V. Kwecko, S. Devincenzi, P. Núñez, and S. S. da C. Botelho, “Ubiquitous learning: Asystematic review,” in 2019 IEEE Frontiers in Education Conference (FIE), 2019, pp. 1–9. [Online]. Available: https://doi.org/10.1109/FIE43999.2019.9028361
Y. Guo and G. Y. H. Lip, “Beyond atrial fibrillation detection: how digital tools impact the care of patients with atrial fibrillation,” European Journal of Internal Medicine, vol. 93, pp. 117–118, 2021. [Online]. Available: https://doi.org/10.1016/j.ejim.2021.08.026
Y. Guo, H. Wang, H. Zhang, T. Liu, Z. Liang, Y. Xia, L. Yan, Y. Xing, H. Shi, S. Li, Y. Liu, F. Liu, M. Feng, Y. Chen, G. Y. H. Lip, and M.A.F.A. II Investigators, “Mobile photoplethysmographic technology to detect atrial fibrillation,” Journal of the American College of Cardiology, vol. 74, no. 19, pp. 2365–2375, Sep. 2019. [Online]. Available: https://doi.org/10.1016/j.jacc.2019.08.019
M. V. Perez, K. W. Mahaffey, H. Hedlin, J. S. Rumsfeld, A. Garcia, T. Ferris, V. Balasubramanian, A. M. Russo, A. Rajmane, L. Cheung, G. Hung, J. Lee, P. Kowey, N. Talati, D. Nag, S. E. Gummidipundi, A. Beatty, M. T. Hills, S. Desai, C. B. Granger, M. Desai, and M. P. Turakhia, “Large-scale assessment of a smartwatch to identify atrial fibrillation,” New England Journal of Medicine, vol. 381, no. 20, pp. 1909–1917, 2019, pMID: 31722151. [Online]. Available: https://doi.org/10.1056/NEJMoa1901183
G. Boriani, R. B. Schnabel, J. S. Healey, R. D. Lopes, N. Verbiest-van Gurp, T. Lobban, J. A. Camm, and B. Freedman, “Consumer-led screening for atrial fibrillation using consumer-facing wearables, devices and apps: A survey of health care professionals by af-screen international collaboration,” European Journal of Internal Medicine, vol. 82, pp. 97–104, 2020. [Online]. Available: https://doi.org/10.1016/j.ejim.2020.09.005
G. H. Mairesse and H. Heidbüchel, “Consumer-led screening for atrial fibrillation: What is the next step?” European Journal of Internal Medicine, vol. 90, pp. 16–18, 2021. [Online]. Available: https://doi.org/10.1016/j.ejim.2021.05.030
J. R. Baman, D. T. Mathew, M. Jiang, and . Passman, R, “Mobile health for arrhythmia diagnosis and management,” Journal of General Internal Medicine, no. 37, pp. 188–197, 2022. [Online]. Available: https://doi.org/10.1007/s11606-021-07007-w
B. Freedman, J. Camm, H. Calkins, J. S. Healey, M. Rosenqvist, J. Wang, C. M. Albert, C. S. Anderson, S. Antoniou, E. J. Benjamin, G. Boriani, J. Brachmann, A. Brandes, T.-F. Chao, D. Conen,
J. Engdahl, L. Fauchier, D. A. Fitzmaurice, L. Friberg, B. J. Gersh, D. J. Gladstone, T. V. Glotzer, K. Gwynne, G. J. Hankey, J. Harbison, G. S. Hillis, M. T. Hills, H. Kamel, P. Kirchhof, P. R. Kowey, D. Krieger, V. W. Y. Lee, L.-A. Levin, G. Y. H. Lip, T. Lobban, N. Lowres, G. H. Mairesse, C. Martinez, L. Neubeck, J. Orchard, J. P. Piccini, K. Poppe, T. S. Potpara, H. Puererfellner, M. Rienstra, R. K. Sandhu, R. B. Schnabel, C.-W. Siu, S. Steinhubl, J. H. Svendsen, E. Svennberg, S. Themistoclakis, R. G. Tieleman, M. P. Turakhia, A. Tveit, S. B. Uittenbogaart, I. C. V. Gelder, A. Verma, R. Wachter, B. P. Yan, A. A. Awwad, F. Al-Kalili, T. Berge, G. Breithardt, G. Bury, W. Caorsi, N. Chan, S. Chen, I. Christophersen, S. Connolly, H. Crijns, S. Davis, U. Dixen, R. Doughty, X. Du, M. Ezekowitz, M. Fay, V. Frykman, M. Geanta, H. Gray, N. Grubb, A. Guerra, J. Halcox, R. Hatala, H. Heidbuchel, R. Jackson, L. Johnson, S. Kaab, K. Keane, Y. Kim, G. Kollios, M. Lochen, C. Ma, J. Mant, M. Martinek, I. Marzona, K. Matsumoto, D. McManus, P. Moran, N. Naik, T. Ngarmukos, D. Prabhakaran, D. Reidpath, A. Ribeiro, A. Rudd, I. Savalieva, R. Schilling, M. Sinner, S. Stewart, N. Suwanwela, N. Takahashi, E. Topol, S. Ushiyama, N. V. van Gurp, N. Walker, and T. Wijeratne, “Screening for atrial fibrillation,” Circulation, vol. 135, no. 19, pp. 1851–1867, 2017. [Online]. Available: https://doi.org/10.1161/CIRCULATIONAHA.116.026693
M. V. McConnell, M. P. Turakhia, R. A. Harrington, A. C. King, and E. A. Ashley, “Mobile health advances in physical activity, fitness, and atrial fibrillation: Moving hearts,” Journal of the American College of Cardiology, vol. 71, no. 23, pp. 2691–2701, 2018. [Online]. Available: https://doi.org/10.1016/j.jacc.2018.04.030
N. Brasier, C. J. Raichle, M. Dörr, A. Becke, V. Nohturfft, S. Weber, F. Bulacher, L. Salomon, T. Noah, R. Birkemeyer, and J. Eckstein, “Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO),” EP Europace, vol. 21, no. 1, pp. 41–47, 2018. [Online]. Available: https://doi.org/10.1093/europace/euy176
M. Weiser, “The computer for the 21st century,” ScientificAmericanUbicompPaperafter-SciAmediting, vol. 265, no. 3, pp. 94–104, 2011. [Online]. Available: https://bit.ly/3uYsmiU
X. Ye, Y. Huang, and Q. Lu, “Explainable prediction of cardiac arrhythmia using machine learning,” in 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2021, pp. 1–5. [Online]. Available: https://doi.org/10.1109/CISP-BMEI53629.2021.9624213
K. Mc Namara, H. Alzubaidi, and J. K. Jackson, “Cardiovascular disease as a leading cause of death: how are pharmacists getting involved?” Integrated pharmacy research & practice, vol. 8, pp. 1–11, Feb. 2019. [Online]. Available: https://doi.org/10.2147/iprp.s133088
J. Bao, “Multi-features based arrhythmia diagnosis algorithm using xgboost,” in 2020 International Conference on Computing and Data Science (CDS), 2020, pp. 454–457. [Online]. Available: https://doi.org/10.1109/CDS49703.2020.00095
G. Silveri, M. Merlo, L. Restivo, B. De Paola, A. Miladinovic, M. Ajcevic, G. Sinagra, and A. Accardo, “Identification of ischemic heart disease by using machine learning technique based on parameters measuring heart rate variability,” in 2020 28th European Signal Processing Conference (EUSIPCO), 2021, pp. 1309–1312. [Online]. Available: https://doi.org/10.23919/Eusipco47968.2020.9287800
X. Wu, Y. Zheng, C.-H. Chu, and Z. He, “Extracting deep features from short ecg signals for early atrial fibrillation detection,” Artificial Intelligence in Medicine, vol. 109, p. 101896, 2020. [Online]. Available: https://doi.org/10.1016/j.artmed.2020.101896
D. Kasper, A. Fauci, S. Hauser, D. Longo, J. Jameson, and J. Loscalzo, Harrison’s principles of internal medicine, 19th ed. Mc Graw Hill, 2014. [Online]. Available: https://bit.ly/3hqHin8
I. Goldenberg, R. Goldkorn, N. Shlomo, M. Einhorn, J. Levitan, R. Kuperstein, R. Klempfner, and B. Johnson, “Heart rate variability for risk assessment of myocardial ischemia in patients without known coronary artery disease: The HRV-DETECT (heart rate variability for the detection of myocardial ischemia) study,” Journal of the American Heart Association, vol. 8, no. 24, p. e014540, Dec. 2019. [Online]. Available: https://doi.org/10.1161/jaha.119.014540
E. J. da S. Luz, W. R. Schwartz, G. Cámara-Chávez, and D. Menotti, “Ecg-based heartbeat classification for arrhythmia detection: A survey,” Computer Methods and Programs in Biomedicine, vol. 127, pp. 144–164, 2016. [Online]. Available: https://doi.org/10.1016/j.cmpb.2015.12.008
H. Zhu, Y. Zhao, Y. Pan, H. Xie, F. Wu, and R. Huan, “Robust heartbeat classification for wearable Single-Lead ECG via extreme gradient boosting,” Sensors (Basel), vol. 21, no. 16, Aug. 2021. [Online]. Available: https://doi.org/10.3390/s21165290
S. Bhalerao, I. A. Ansari, and A. Kumar, “Reversible ecg data hiding: Analysis and comparison of ann, regression svm and random forest regression,” in 2020 International Conference on Communication and Signal Processing (ICCSP), 2020, pp. 0667–0671. [Online]. Available: https://doi.org/10.1109/ICCSP48568.2020.9182219
M. Manjula and A. Sarma, “Comparison of empirical mode decomposition and wavelet based classification of power quality events,” Energy Procedia, vol. 14, pp. 1156–1162, 2012. [Online]. Available: https://doi.org/10.1016/j.egypro.2011.12.1069
S. Murawwat, H. M. Asif, S. Ijaz, M. Imran Malik, and K. Raahemifar, “Denoising and classification of arrhythmia using memd and ann,” Alexandria Engineering Journal, vol. 61, no. 4, pp. 2807–2823, 2022. [Online]. Available: https://doi.org/10.1016/j.aej.2021.08.014
M. Chandra Gaddam and S. Pattnaik, “An ann ensemble based ecg signal classification approach for accurate arrhythmia detection,” International Journal of Emerging Technology and Advanced Engineering, vol. 10, pp. 57–61, 2020. [Online]. Available: http://dx.doi.org/10.46338/IJETAE0820_08
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. [Online]. Available: https://doi.org/10.1038/nature14539
T. Kanai, N. Tanabe, Y. Miyagi, and J. Aoyama, “Cnn-type myocardial infarction prediction based on cardiac cycle determination,” in 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2021, pp. 1–2. [Online]. Available: https://doi.org/10.1109/ISPACS51563.2021.9651000
A. Escontrela. (2020) Convolutional neural networks from the ground up. [Online]. Available: https://bit.ly/2EXtsnf
M. Dey, N. Omar, and M. A. Ullah, “Temporal feature-based classification into myocardial infarction and other cvds merging cnn and bi-lstm from ecg signal,” IEEE Sensors Journal, vol. 21, no. 19, pp. 21 688–21 695, 2021. [Online]. Available: https://doi.org/10.1109/JSEN.2021.3079241
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” NAACL, vol. abs/1810.04805, 2018. [Online]. Available: https://doi.org/10.48550/arXiv.1810.04805
R. Shwartz-Ziv and A. Armon, “Tabular data: Deep learning is not all you need,” CoRR, vol. abs/2106.03253, 2021. [Online]. Available: https://doi.org/10.48550/arXiv.2106.03253
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. abs/1603.02754, 2016. [Online]. Available: https://doi.org/10.1145/2939672.2939785
R. Shwartz-Ziv, A. Painsky, and N. Tishby, “Representation compression and generalization in deep neural networks,” in ICLR 2019 Conference Blind Submission, 2018. [Online]. Available: https://bit.ly/3YjzJz0
T. Poggio, A. Banburski, and Q. Liao, “Theoretical issues in deep networks,” Proceedings of the National Academy of Sciences, vol. 117, no. 48, pp. 30 039–30 045, 2020. [Online]. Available: https://www.pnas.org/doi/abs/10.1073/pnas.1907369117
Z. Piran, R. Shwartz-Ziv, and N. Tishby, “The dual information bottleneck,” CoRR, vol. abs/2006.04641, 2020. [Online]. Available: https://doi.org/10.48550/arXiv.2006.04641
A. V. Dorogush, A. Gulin, G. Gusev, N. Kazeev, L. O. Prokhorenkova, and A. Vorobev, “Fighting biases with dynamic boosting,” CoRR, vol. abs/1706.09516, 2017. [Online]. Available: https://doi.org/10.48550/arXiv.1706.09516
H. Shi, H. Wang, Y. Huang, L. Zhao, C. Qin, and C. Liu, “A hierarchical method based on weighted extreme gradient boosting in ecg heartbeat classification,” Computer Methods and Programs in Biomedicine, vol. 171, pp. 1–10, 2019. [Online]. Available: https://doi.org/10.1016/j.cmpb.2019.02.005
Z. Yue and Z. Jinjing, “Atrial fibrillation detection based on eemd and xgboost,” Journal of Physics: Conference Series, vol. 1229, no. 1, p. 012074, 2019. [Online]. Available: https://dx.doi.org/10.1088/1742-6596/1229/1/012074
B. R. Manju and A. R. Nair, “Classification of cardiac arrhythmia of 12 lead ecg using combination of smoteenn, xgboost and machine learning algorithms,” in 2019 9th International Symposium on Embedded Computing and System Design (ISED), 2019, pp. 1–7. [Online]. Available: https://doi.org/10.1109/ISED48680.2019.9096244
N. V. Chawla, K. W. Bowyer, L. O.Hall, and W. P. Kegelmeyer, “Smote: synthetic minority oversampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002. [Online]. Available: https://doi.org/10.1613/jair.953
F. Giannakas, C. Troussas, A. Krouska, C. Sgouropoulou, and I. Voyiatzis, “Xgboost and deep neural network comparison: The case of teams’ performance,” in Intelligent Tutoring Systems, A. I. Cristea and C. Troussas, Eds. Springer International Publishing, 2021, pp. 343–349. [Online]. Available: https://doi.org/10.1007/978-3-030-80421-3_37
P. D. Arini and E. R. Valverde, “Beat-to-beat electrocardiographic analysis of ventricular repolarization variability in patients after myocardial infarction,” Journal of electrocardiology, vol. 49, no. 2, pp. 206–213, Dec. 2015. [Online]. Available: https://doi.org/10.1016/j.jelectrocard.2015.12.003
W. Liu, F. Wang, Q. Huang, S. Chang, H. Wang, and J. He, “MFB-CBRNN: A hybrid network for MI detection using 12-lead ECGs,” IEEE journal of biomedical and health informatics, vol. 24, no. 2, pp. 503–514, Apr. 2019. [Online]. Available: https://doi.org/10.1109/jbhi.2019.2910082
M. R. Rajeshwari and K. S. Kavitha, “Arrhythmia ventricular fibrillation classification on ECG signal using ensemble feature selection and deep neural network,” Cluster Computing, vol. 25, no. 5, pp. 3085–3102, 2022. [Online]. Available: https://doi.org/10.1007/s10586-022-03547-w
H. M. Rai and K. Chatterjee, “Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data,” Applied Intelligence, vol. 52, no. 5, pp. 5366–5384, 2022. [Online]. Available: https://doi.org/10.1007/s10489-021-02696-6
B. Król-Józaga, “Atrial fibrillation detection using convolutional neural networks on 2-dimensional representation of ecg signal,” Biomedical Signal Processing and Control, vol. 74, p. 103470, 2022. [Online]. Available: https://doi.org/10.1016/j.bspc.2021.103470