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1 1 NCBI Bookshelf. (2004). Cardiology explained: Cardiovascular Examination. A service of the National Library of Medicine, National Institutes of Health.

2 2 Malik, M.B.and Gopal, S. (2020). Cardiac exam. StatPearls.

3 3 Jacob, R.and Khan, M. (2018). Cardiac biomarkers: What is and what can be. Indian Journal of Cardiovascular Disease in Women WINCARS 03 (04). https://doi.org/10.1055/s-0039-1679104.

4 4 Peterson, P.N., Magid, D.J., Ross, C., Ho, P.M., Rumsfeld, J.S., Lauer, M.S., Lyons, E.E., Smith, S.S., and Masoudi, F.A. (2008). Association of exercise capacity on treadmill with future cardiac events in patients referred for exercise testing. Archives of Internal Medicine 168 (2). https://doi.org/10.1001/archinternmed.2007.68.

5 5 Cole, C.R., Foody, J.A.M., Blackstone, E.H., and Lauer, M.S. (2000). Heart rate recovery after submaximal exercise testing as a predictor of mortality in a cardiovascularly healthy cohort. Annals of Internal Medicine 132 (7). https://doi.org/10.7326/0003-4819-132-7-200004040-00007.

6 6 Jouven, X., Empana, J.-P., Schwartz, P.J., Desnos, M., Courbon, D., and Ducimetière, P. (2005). Heart-rate profile during exercise as a predictor of sudden death. New England Journal of Medicine 352 (19). https://doi.org/10.1056/nejmoa043012.

7 7 Kolber, M.R.and Scrimshaw, C. (2014). Family history of cardiovascular disease. Canadian Family Physician 60 (11).

8 8 Benjamin, E.J., Blaha, M.J., Chiuve, S.E., Cushman, M., Das, S.R., Deo, R., De Ferranti, S.D., Floyd, J., Fornage, M., Gillespie, C., Isasi, C.R., Jim’nez, M.C., Jordan, L.C., Judd, S.E., Lackland, D., Lichtman, J.H., Lisabeth, L., Liu, S., Longenecker, C.T., and Muntner, P. (2017). Heart disease and stroke statistics – 2017 Update: A report from the American Heart Association. Circulation 135 (10). https://doi.org/10.1161/CIR.0000000000000485.

9 9 Franklin, S.S., Larson, M.G., Khan, S.A., Wong, N.D., Leip, E.P., Kannel, W.B., and Levy, D. (2001). Does the relation of blood pressure to coronary heart disease risk change with aging?: The Framingham heart study. Circulation 103 (9). https://doi.org/10.1161/01.CIR.103.9.1245.

10 10 Rader, D.J.and Hovingh, G.K. (2014). HDL and cardiovascular disease. The Lancet 384 (9943). https://doi.org/10.1016/S0140-6736(14)61217-4.

11 11 Fernandez, M.L.and Webb, D. (2008). The LDL to HDL cholesterol ratio as a valuable tool to evaluate coronary heart disease risk. Journal of the American College of Nutrition 27 (1). https://doi.org/10.1080/07315724.2008.10719668.

12 12 Navar, A.M., Pagidipati, N., Mulder, H., Aberra, T., Philip, S., Granowitz, C., and Peterson, E. (2019). Triglycerides as a risk factor for coronary heart disease: What measure and what cutoff? Journal of the American College of Cardiology 73 (9). https://doi.org/10.1016/s0735-1097(19)32471-4.

13 13 Chua Chiaco, J.M.S., Parikh, N.I., and Fergusson, D.J. (2013). The jugular venous pressure revisited. Cleveland Clinic Journal of Medicine 80 (10). https://doi.org/10.3949/ccjm.80a.13039.

14 14 Kupari, M., Koskinen, P., Virolainen, J., Hekali, P., and Keto, P. (1994). Prevalence and predictors of audible physiological third heart sound in a population sample aged 36 to 37 years. Circulation 89 (3). https://doi.org/10.1161/01.CIR.89.3.1189.

15 15 Breuer, H. (1981). Auscultation of the heart in pregnancy (author’s transl). MMW, Munchener Medizinische Wochenschrift 123 (45): 1705–1707.

16 16 Roguin, A. (2006). René Theophile Hyacinthe Laënnec (1781-1826): The man behind the stethoscope. Clinical Medicine and Research 4 (3). https://doi.org/10.3121/cmr.4.3.230.

17 17 Swarup, S.and Makaryus, A.N. (2018). Digital stethoscope: Technology update. Medical Devices: Evidence and Research 11. https://doi.org/10.2147/MDER.S135882.

18 18 AlGhatrif, M.and Lindsay, J. (2012). A brief review: History to understand fundamentals of electrocardiography. Journal of Community Hospital Internal Medicine Perspectives 2 (1). https://doi.org/10.3402/jchimp.v2i1.14383.

19 19 Singh, S.and Goyal, A. (2007). The origin of echocardiography: A tribute to Inge Edler. Texas Heart Institute Journal/from the Texas Heart Institute of St. Luke’s Episcopal Hospital, Texas Children’s Hospital 34 (4).

20 20 Kunhoth, J., Karkar, A., Al-Maadeed, S., and Al-Attiyah, A. (2019). Comparative analysis of computer-vision and BLE technology based indoor navigation systems for people with visual impairments. International Journal of Health Geographics 18 (1). https://doi.org/10.1186/s12942-019-0193-9.

21 21 Al Maadeed, S., Kunhoth, S., Bouridane, A., and Peyret, R. (2017). Multispectral imaging and machine learning for automated cancer diagnosis. 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017. https://doi.org/10.1109/IWCMC.2017.7986547.

22 22 Masetic, Z.and Subasi, A. (2016). Congestive heart failure detection using random forest classifier. Computer Methods and Programs in Biomedicine 130. https://doi.org/10.1016/j.cmpb.2016.03.020.

23 23 Molinari, F., Meiburger, K.M., Saba, L., Rajendra Acharya, U., Ledda, M., Nicolaides, A., and Suri, J.S. (2012). Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets. Ultrasonics 52 (7). https://doi.org/10.1016/j.ultras.2012.03.005.

24 24 Nagaraj, Y., Teja, A.H.S., and Narasimhadhan, A.V. (2019). Automatic segmentation of intima media complex in carotid ultrasound images using support vector machine. Arabian Journal for Science and Engineering 44 (4). https://doi.org/10.1007/s13369-018-3549-8.

25 25 Biswas, M., Saba, L., Chakrabartty, S., Khanna, N.N., Song, H., Suri, H.S., Sfikakis, P.P., Mavrogeni, S., Viskovic, K., Laird, J.R., Cuadrado-Godia, E., Nicolaides, A., Sharma, A., Viswanathan, V., Protogerou, A., Kitas, G., Pareek, G., Miner, M., and Suri, J.S. (2020). Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment. Computers in Biology and Medicine 123. https://doi.org/10.1016/j.compbiomed.2020.103847.

26 26 Biswas, M., Kuppili, V., Araki, T., Edla, D.R., Godia, E.C., Saba, L., Suri, H.S., Omerzu, T., Laird, J.R., Khanna, N.N., Nicolaides, A., and Suri, J.S. (2018). Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort. Computers in Biology and Medicine 98. https://doi.org/10.1016/j.compbiomed.2018.05.014.

27 27 Menchón-Lara, R.M., Sancho-Gómez, J.L., and Bueno-Crespo, A. (2016). Early-stage atherosclerosis detection using deep learning over carotid ultrasound images. Applied Soft Computing Journal 49. https://doi.org/10.1016/j.asoc.2016.08.055.

28 28 Li, D., Zhang, J., Zhang, Q., and Wei, X. (2017). Classification of ECG signals based on 1D convolution neural network. 2017 IEEE 19th International Conference on E-Health Networking, Applications and Services, Healthcom 2017, 2017-December. https://doi.org/10.1109/HealthCom.2017.8210784.

29 29 Rajput, J.S., Sharma, M., Tan, R.S., and Acharya, U.R. (2020). Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank. Computers in Biology and Medicine 123. https://doi.org/10.1016/j.compbiomed.2020.103924.

30 30 Eltrass, A.S., Tayel, M.B., and Ammar, A.I. (2021). A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform. Biomedical Signal Processing and Control 65. https://doi.org/10.1016/j.bspc.2020.102326.

31 31 Moridani, M.K., Abdi Zadeh, M., and Shahiazar Mazraeh, Z. (2019). An efficient automated algorithm for distinguishing normal and abnormal ECG signal. IRBM 40 (6). https://doi.org/10.1016/j.irbm.2019.09.002.

32 32 Van Den Oever, L.B., Cornelissen, L., Vonder, M., Xia, C., Van Bolhuis, J.N., Vliegenthart, R., Veldhuis, R.N.J., De Bock, G.H., Oudkerk, M., and Van Ooijen, P.M.A. (2020). Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium. European Journal of Radiology 129. https://doi.org/10.1016/j.ejrad.2020.109114.

33 33 Van Assen, M., Martin, S.S., Varga-Szemes, A., Rapaka, S., Cimen, S., Sharma, P., Sahbaee, P., De Cecco, C.N., Vliegenthart, R., Leonard, T.J., Burt, J.R., and Schoepf, U.J. (2021). Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study. European Journal of Radiology 134. https://doi.org/10.1016/j.ejrad.2020.109428.

34 34 Zhang, N., Yang, G., Zhang, W., Wang, W., Zhou, Z., Zhang, H., Xu, L., and Chen, Y. (2021). Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications. European Journal of Radiology 134. https://doi.org/10.1016/j.ejrad.2020.109420.

35 35 Dekker, M., Waissi, F., Bank, I.E.M., Isgum, I., Scholtens, A.M., Velthuis, B.K., Pasterkamp, G., De Winter, R.J., Mosterd, A., Timmers, L., and De Kleijn, D.P.V. (2021). The prognostic value of automated coronary calcium derived by a deep learning approach on non-ECG gated CT images from 82Rb-PET/CT myocardial perfusion imaging. International Journal of Cardiology 329. https://doi.org/10.1016/j.ijcard.2020.12.079.