Predicting Heart Failure. Группа авторов

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      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.

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