Biomedical Engineering

Academic Staff

June 11, 2026, 10:50 a.m.
Fars Esmat Fathel Samann
Fars Esmat Fathel Samann (PhD)
Lecturer
Lecturer in Signal Processing and System

Biomedical Engineering
College of Engineering
University of Duhok
[email protected]
ORCID
Google Scholar

  • Dr.-Ing., Life Science Engineering, Technische Hochschule Mittelhessen – University of Applied Sciences, Germany, 2021–2025
  • M.Sc., Electronic Communications and Computer Engineering, University of Nottingham, UK, 2013–2014
  • B.Sc., Electrical and Computer Engineering, University of Duhok, Iraq, 2007–2011

Fars Samann was born in Baghdad, Iraq, in 1988. He received his Bachelor of Science (B.Sc), in field of Electrical and Computer Engineering , from University of Duhok, Kurdistan-Iraq, in 2011. He completed his Master of Science (M.Sc), in field of Electronic Communication and Computer
Engineering, from the University of Nottingham, Nottingham, United kingdom. He joined the department of Electrical and Computer Engineering, University of Duhok in 2014 as an assistant lecturer. Then he joined the department of Biomedical Engineering, University of Duhok, in 2019. He completed his Doctor of Philosophy (PhD) in Biomedical Engineering at the Faculty of Life Science Engineering (LSE), Technische Hochschule Mittelhessen (THM) – University of Applied Sciences, Gießen, Germany, with the highest honor distinction (Summa Cum Laude). His research interests lie in the area of biomedical engineering, signal processing, denoising bio-signals via denoising autoencoder, bio-signals processing by neural network, sparse modeling of biomedical signals, and denoising bio-signals via sparse modelling. He has collaborated actively with researchers in several other disciplines of Electrical and Computer Engineering.

  • Signal and System (Undergraduate)
  • Biomedical Measurement Techniques (Undergraduate)
  • Electrical Circuit (Undergraduate)

  • Participating in the 2nd International Summer School on Cyprus part of the master course of “Biomedical Engineering” of the module “Signal and Image processing in Medicine” (6 ECTS). (this program was Granted by DAAD).
  • Participating in the DAAD project “Bioniq- Bio/MedPhys” to visit university of applied science, THM, Giessen in the period 1st of October 2018 to the 31th of December 2018, working on my PhD proposal in the field of Biomedical Engineering.
  • Participate in the DAAD project “Sustainable Development of Biomedical Engineering in Northern of Iraq-SD-BIONIQ” to visit university of applied science, THM, Giessen in the period 1st of September 2019 to the 30th of November 2019, working on my PhD proposal in the field of Biomedical Engineering.
  • Developing Msc project “LOCALIZATION OF MONOPOLE AND DIPOLE SOURCE IN 3 DIMENSIONS” for the Msc students of university of applied science, THM, Giessen as a part of the DAAD project “Bioniq- Bio/MedPhys”.
  • Training on EEG recording in Azadi Hospital for three months since 27th of March 2019
  • Participating in Innovation Expo of Duhok Province Universities 2017 with project title “Adapted TV remote control using EOG”. This event was funded by European Union and implemented by UNDP.

Research

1. Alfa, M., Samann, F., & Schanze, T. (2026). ML-CDAE: Multi-Lead Convolutional Denoising Autoencoder for Denoising 12-Lead ECG Signals. Signals, 7(1), 18. https://doi.org/10.3390/signals7010018

2. F. Samann and T. Schanze, “AE-DD: Autoencoder-Driven Dictionary with Matching Pursuit for Joint ECG Denoising, Compression, and Morphology Decomposition,” AI, vol. 6, no. 9, p. 234, Sep. 2025, doi: 10.3390/ai6090234. (Impact factor=5)

3. F. Samann, Towards Real-Time ECG Signal Denoising using Sparse and Shallow Running Denoising Autoencoder, Technische Hochschule Mittelhessen, 2025.

4. N. Busch, F. Samann, A. Neißner, M. Fiebich, and T. Schanze, “Denoising of low dose CT scans by means of Denoising Autoencoder,” Abstracts of the 58th Annual Meeting of the German Society of Biomedical Engineering, 2024.

5. A. Prächte, F. Samann, and T. Schanze, “Implementation of running denoising autoencoder (RunDAE) on Arduino for real-time denoising of ECG,” Abstracts of the 58th Annual Meeting of the German Society of Biomedical Engineering, 2024.

6. F. Samann, F. Hubich, T. Ott, and T. Schanze, “Automatisierungstechnik: Muscle fatigue detection based on sEMG signal using autocorrelation function and neural networks,” De Gruyter, 2024.

7. F. Samann and T. Schanze, “Denoising by spectral selections of SVD representations of Hankel matricificated data with application to PPG signals,” IFAC-PapersOnLine, vol. 58, no. 24, pp. 175–180, 2024.

8. F. Samann, F. Hubich, T. Ott, and T. Schanze, “Muscle fatigue detection based on sEMG signal using autocorrelation function and neural networks,” at - Automatisierungstechnik, vol. 72, no. 5, pp. 408–416, 2024.

9. F. Samann and T. Schanze, “RESEMBLING THE MORPHOLOGIES OF ECG SIGNALS USING REGULARIZED DENOISING AUTOENCODER,” Passer Journal of Basic and Applied Sciences, vol. 6 (Special Issue), pp. 341–351, 2024.

10. L. M. Meyer, F. Samann, and T. Schanze, “DualSort: online spike sorting with a running neural network,” Journal of Neural Engineering, vol. 20, no. 5, p. 056031, 2023. (Impact factor=4)

11. F. Samann and T. Schanze, “RunDAE model: Running denoising autoencoder models for denoising ECG signals,” Computers in Biology and Medicine, p. 107553, 2023. (Impact factor=7)

12. F. Samann, L. Meyer, and T. Schanze, “Removing noise and overlapping spikes from extracellular recordings using a regularized denoising autoencoder,” Current Directions in Biomedical Engineering, vol. 9, no. 1, pp. 279–282, 2023.

13. F. Samann and T. Schanze, “Multiple ECG segments denoising autoencoder model,” Biomedical Engineering/Biomedizinische Technik, vol. 68, no. 3, pp. 275–284, 2023. (Impact factor=0.9)

14. F. Samann and T. Schanze, “EMG based muscle fatigue detection using autocorrelation and k-means clustering,” Proceedings on Automation in Medical Engineering, vol. 2, no. 1, p. 739, 2023.

15. L. M. Meyer, T. Schanze, and F. Samann, “A single-hidden-layer neural network for the classification of spike-waveforms,” Proceedings on Automation in Medical Engineering, vol. 2, no. 1, p. 747, 2023.

16. B. Marwan, F. Samann, and T. Schanze, “Cleaning Noisy ECG based on the Signal Quality with Single and Multiple Hidden Layer Autoencoder,” 2022 2nd International Conference on Intelligent Cybernetics Technology, 2022.

17. B. Marwan, F. Samann, and T. Schanze, “Denoising of ECG with single and multiple hidden layer autoencoders,” Current Directions in Biomedical Engineering, vol. 8, no. 2, pp. 652–655, 2022.

18. F. Samann and T. Schanze, “Multiple parallel hidden layers autoencoder for denoising ECG signal,” Current Directions in Biomedical Engineering, vol. 8, no. 2, pp. 161–164, 2022.

19. F. Samann and T. Schanze, “Abstracts of the 2022 Joint Annual Conference of the Austrian (ÖGBMT), German (VDE DGBMT) and Swiss (SSBE) Societies for Biomedical Engineering,” Biomedical Engineering/Biomedizinische Technik, vol. 67, suppl. 1, pp. 1–580, 2022.

20. F. Samann and T. Schanze, “Entrauschen von EKG-Signalen anhand von Autoencodern mit hybriden verborgenen Neuronenschichten,” DGMP 2022 – 53. Jahrestagung der Deutschen Gesellschaft für Medizinische Physik, 2022.

21. F. Samann and T. Schanze, “Denoising biomedical signals via adaptive low-rank matrix representation by singular value decomposition using wavelets,” 2021 4th Int. Conf. on Bio-Engineering for Smart Technologies, 2021.

22. F. Samann and T. Schanze, “Finding an optimal dictionary of different wavelet types using sparse modeling to denoise ECG signal,” Current Directions in Biomedical Engineering, vol. 7, no. 2, pp. 125–128, 2021.

23. F. Samann and T. Schanze, “Use of a trained denoising autoencoder to estimate the noise level in the ECG,” Current Directions in Biomedical Engineering, vol. 7, no. 2, pp. 562–565, 2021.

24. F. Samann, S. A. Bamerni, J. A. Khorsheed, and A. K. Al-sulaifanie, “Adaptive Real-Time Wavelet Denoising Architecture Based on Feedback Control Loop,” Journal of Engineering Research, vol. 9 (ICRIE Special Issue), pp. 1–18, 2021.

25. F. Samann and T. Schanze, “On estimating the optimal autoencoder model for denoising ECG using Akaike Information Criterion,” AUTOMED - Automation in Medical Engineering, 2021.

26. R. Bassam and F. Samann, “Smart Parking System based on Improved OCR Model,” IOP Conf. Ser.: Materials Science and Engineering, vol. 978, no. 1, p. 012007, 2020.

27. M. Schubert, F. Samann, and T. Schanze, “An improved simple experimental setup for superimposed PPG signal separation,” Innovative digitale Verarbeitung bioelektrischer und -magnetischer Signale, 2020.

28. M. Schubert, F. Samann, and T. Schanze, “QRS triggered averaging for superimposed PPG separation,” Proc. on Automation in Medical Engineering, vol. 1, no. 1, p. 014, 2020.

29. M. Schubert, F. Samann, and T. Schanze, “Towards non-invasive fetal blood oxygen level acquisition: ECG-triggered separation of superimposed PPG,” 54th Annual Conference of the German Society for Biomedical Engineering, vol. 1, pp. 1–2, 2020.

30. F. Samann, A. Rausch, and T. Schanze, “Electrical Dipole Source Localization using Hybrid Least Squares Method in combination with ICA,” Current Directions in Biomedical Engineering, vol. 5, no. 1, pp. 361–364, 2019.

31. F. Samann and T. Schanze, “An efficient ECG denoising method using discrete wavelet with Savitzky-Golay filter,” Current Directions in Biomedical Engineering, vol. 5, no. 1, pp. 385–387, 2019.

32. F. E. Samann, “Real-time Liquid Level and color Detection system using Image Processing,” Academic Journal of Nawroz University (AJNU), vol. 7, no. 4, pp. 223–227, 2018.

33. F. E. Samann and M. S. Hadi, “HUMAN TO TELEVISION INTERFACE FOR DISABLED PEOPLE BASED ON EOG,” Journal of University of Duhok, vol. 21, no. 1, pp. 54–64, 2018.

34. F. E. Samann, “SIMPLE AND ROBUST EYE MOVEMENTS DETECTION METHOD,” Journal of Duhok University, vol. 20, no. 1, pp. 152–163, 2017.

35. F. E. Samann, “INTERCHANNEL AND CROSS GAIN CROSSTALK EFFECTS IN WDM SYSTEMS WITH SOAs,” Ph.D. thesis, University of Nottingham, UK.

  • Biomedical signal processing; ECG denoising; neural networks; machine learning in healthcare

M.Sc. Thesis Co-Supervisor at THM:

  1. Neural Networks for Spike Sorting Applications
  2. Noisy ECG Cleaning Using Single- and Multi-layer Autoencoders Based on Signal Quality
  3. ML-CDAE: Multi-lead Convolutional Denoising Autoencoder for 12-lead ECG Signal Denoising
  4. Modeling and Localization of Electrical Signal Sources Using an L1-Regularized Multi-Monopole Approach