Page 128 - AIH-2-3
P. 128
Artificial Intelligence in Health Opportunities for AI-based arrhythmia screening
17. Garg N, Agarwal BL, Modi N, Radhakrishnan S, Sinha N. Conference on Recent Trends in Electronics, Information and
Dextrocardia: An analysis of cardiac structures in Communication Technology (RTEICT).
125 patients. Int J Cardiol. 2003;88(2-3):143-156.
doi: 10.1109/rteict42901.2018.9012
doi: 10.1016/s0167-5273(02)00539-9
30. Proakis JG, Manolakis DG. Digital Signal Processing.
18. Wiener N. The Interpolation, Extrapolation and Smoothing Available from: https://uvceee.wordpress.com/wp-content/
of Stationary Time Series’, Report of the Services 19, Research uploads/2016/09/digital_signal_processing_principles_
Project DIC-6037 MIT; 1942. algorithms_and_applications_third_edition.pdf [Last
accessed on 2025 Mar 18].
19. Luengo-Fernandez R, Walli-Attaei M, Gray A, et al.
Economic burden of cardiovascular diseases in the 31. Bhattacharyya SS, Deprettere EF, Leupers R, Takala J.
European Union: A population-based cost study. Eur Heart Handbook of Signal Processing Systems. 3 ed. New York, NY:
rd
J. 2023;44(45):4752-4767. Springer; 2019. Available from: https://link.springer.com/
book/10.1007/978-3-319-91734-4 [Last accessed on 2025
doi: 10.1093/eurheartj/ehad583
Mar 18].
20. Brown RG, Hwang PYC. Introduction to Random Signals 32. Lyons RG. Understanding Digital Signal Processing. Hoboken,
and Applied Kalman Filtering. 3 ed. New York, NY: John NJ: Prentics Hall; 2010.
rd
Wiley & Sons; 1996.
33. Casasent D, Chang WT. Correlation synthetic discriminant
21. Welch LR. Wiener-Hopf Theory 2006-11-25. Available from: functions. Appl Optics. 1986;25(14):2343-2350.
https://csi.usc.edu/PDF/wienerhopf.pdf [Last accessed on
2025 Mar 18]. doi: 10.1364/AO.25.002343
22. Wiener N, Hopf E. Ueber Eine Klasse Singulärer 34. Holton T. Digital Signal Processing, Principles and Applications.
Integralgleichungen. Berlin: Sitzungber. Akad. Wiss; 1931. Cambridge, UK: Cambridge University Press; 2021.
p. 696-706. doi: 10.1017/9781108290050
23. Wiener N. Extrapolation, Interpolation, and Smoothing of 35. Alizadehsani R, Roshanzamir M, Hussain S, et al. Handling
Stationary Time Series. New York NY: John Wiley & Sons; 1949. of uncertainty in medical data using machine learning and
24. Kolmogorov AN. Stationary sequences in Hilbert space’, (In probability theory techniques: A review of 30 years (1991-
Russian) Bull. Moscow Univ. 1941;2(6):1-40. English translation 2020). Ann Oper Res. 2021;21:1-42.
In: Kailath T, editor. Linear Least Squares Estimation. doi: 10.1007/s10479-021-04006-2
Stroudsburg, PA; Dowden, Hutchinson & Ross; 1977.
36. Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-
25. Tison GH, Zhang J, Delling FN, Deo RC. Automated and level arrhythmia detection and classification in ambulatory
interpretable patient ECG profiles for disease detection, electrocardiograms using a deep neural network. Nat Med.
tracking, and discovery. Circ Cardiovasc Qual Outcomes. 2019;25(1):65-69.
2019;12(9):e005289.
doi: 10.1038/s41591-018-0268-3
doi: 10.1161/CIRCOUTCOMES.118.005289
37. Turin GL. An introduction to matched filters. IRE Trans
26. Christopoulos G, Graff-Radford J, Lopez CL, et al. Artificial Inform Theory. 1960;6(3):311-332.
intelligence-electrocardiography to predict incident atrial
fibrillation: A population-based study. Circ Arrhythm doi: 10.1109/TIT.1960.1057571
Electrophysiol. 2020;13(12):e009355. 38. Davenpoort MA, Boufounos P, Wakin M, Baraniuk R.
doi: 10.1161/CIRCEP.120.009355 Signal processing with compressive measurements. IEEE J
Select Top Signal Process. 2010;4(2):445-460.
27. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional
neural networks: An overview and application in radiology. doi: 10.1109/JSTSP.2009.2039178
Insights Imaging. 2018;9(4):611-629. 39. Wimalajeewa T, Varshney PK. Sparse signal detection
doi: 10.1007/s13244-018-0639-9 with compressive measurements via partial support set
estimation. IEEE Trans Signal Inform Process Over Netw.
28. Rudin C. Stop explaining black box machine learning 2017;3(1):46-60.
models for high stakes decisions and use interpretable
models instead. Nat Mach Intell. 2019;1(5):206-215. doi: 10.1109/TSIPN.2016.2601025
40. Fornasier M, Rauhut H. Compressive sensing. In:
doi: 10.1038/s42256-019-0048-x
Scherzer O, editor. Handbook of Mathematical Methods in
29. Ashok A, Babburi A, Ardra T, Gayathri KS, Indu RJ, Imaging. New York, NY: Springer; 2011.
Narayanan G. Performance Comparison of Matched
Filter, Wavelet Denoising and Wiener Filter Technique in doi: 10.1007/978-0-387-92920-0_6
Communication Receivers. In: 2018 3 IEEE International 41. Cauchy AL. Extrait du Mémoire sur Quelques Séries Analogues
rd
Volume 2 Issue 3 (2025) 122 doi: 10.36922/aih.8468

