Dr Oliver Faust

Oliver Faust For media enquiries please contact the Press Team
Associate Professor
Faculty:
Faculty of Science and Engineering
School:
Computing and Information Science
Location:
Cambridge
Areas of Expertise:
Artificial Intelligence , Machine learning
Research Supervision:
Yes

Oliver is an expert physiological and medical image analysis for computer-aided diagnosis.

[email protected]

Background

Oliver has worked at the forefront of artificial intelligence in biomedical engineering. He is an international expert for medical decision support systems, artificial intelligence, the Internet of Medical Things, and signal processing, substantiated by his considerable publication record which includes 100 impact factor journal papers. He is a founding member of the Transformative Artificial Intelligence Applications research cluster, and a member of the Computing, Informatics and Applications Research Group at ARU.

Oliver holds two doctoral degrees (Doctor of Philosophy in electronic engineering from the University of Aberdeen; Doctor of Engineering in biomedical science from Chiba University, Japan). He's a founding faculty member of Habib University in Karachi, Pakistan, and assisted the startup of Altreonic Inc, a software company in Belgium. He collaborates with more than 20 distinguished biomedical scientists worldwide. Previously, he was a visiting scholar at Tianjin University, Zhejiang University, and University of Electronic Science and Technology of China.

Spoken Languages
  • German
  • English
  • Dutch
Research interests
  • Medical image analysis
  • Medical signal analysis
  • eHealth and mHealth
  • Internet of Medical Things
Areas of research supervision
  • Medical image analysis
  • Medical signal analysis
  • eHealth and mHealth
  • Internet of Medical Things
Teaching
  • Apprenticeship Final Project
  • Professional Issues
  • Computer Systems
Qualifications
  • Fellow of the Higher Education Academy, UK
  • Chartered engineer, Institution of Engineering and Technology, UK 
  • Doctor of Engineering in medical engineering, Chiba University, Japan
  • PhD in Electronics, University of Aberdeen, Scotland
  • Diplom ingenieur in communication engineering, FH Dieburg, Germany
Memberships, editorial boards
  • Associate Editor, Computer Methods and Programs in Biomedicine, IF 5.428
  • Associate Editor, Biomedical Signal Processing (specialty section of Frontiers in Signal Processing) 
  • Editor, Computers in Biology and Medicine, IF 4.589
  • Editor, MDPI International Journal of Environmental Research and Public Health, IF 3.390
  • Editor, MDPI signals, since 2020
  • Editor, ICTACT Journal on Communication Technology, since 2006
  • Guest editor, Frontiers in signal processing: Evidence Based Rehabilitation through Physiological Signal Processing, 2021
  • Guest editor, MDPI Diagnostics: Machine Extractable Knowledge from the Shape of Anatomical Structures, 2021
  • Guest editor, IEEE Transactions on Industrial Informatics: Patient Driven Data Acquisition for Next Generation Healthcare Industry, 2021
  • Guest editor, IET Image Processing: Advanced AI Based Image Diagnostics for Cancer, 2021
  • Guest editor, Expert systems: Wireless applications for Biomedical Signals, 2021
  • Guest editor, MDPI International Journal of Environmental Research and Public Health: Application of Deep Learning for Neural Systems, 2020
  • Guest editor, Artificial Intelligence In Medicine: Deep Learning for Medical Applications, 2019
  • Guest editor, Pattern recognition letters: smart Pattern Recognition for Medical Informatics, 2018
  • Advisor, Swiss National Science Foundation
  • Advisor, Estonian research council 
Research grants, consultancy, knowledge exchange
  • 2021-2022: Sheffield Hallam Graduate Teaching Assistants scheme. Grant: £75,000
  • 2021-2022: PI, Innovate UK Innovation to Commercialisation of University Research (ICURe) Programme. Grant: £30,000
  • 2020-2021: Grow MedTech proof of feasibility booster funding for the stroke risk monitoring service. Grant: £10,000
  • 2020-2021: Grow MedTech proof innovation funding: £10,000
  • 2019-2022: Department research funding at Sheffield Hallam University: £4,000
  • 2019-2020: PI. Grow MedTech proof of feasibility funding for the stroke risk monitoring service. Grant: £40,000
  • 2019-2000: Transformative Artificial Intelligence Applications research cluster funding at Sheffield Hallam University: £1,550
  • 2018-2019: PI. Grow MedTech proof of market funding for the stroke risk monitoring service. Grant: £10,000
  • 2018-2022: PhD student with scholarship from the Libyan government. Grant: £49,800
  • 2017-2021: PhD student funding. Grant: £49,800
  • 2013-2015: Head of the lab construction committee at Habib University. Grant from the Habib University endowment: US$120,000
  • 2009-2012: Principal Investigator (PI) for the multi-standard radio system project. Grant: 180,000 S$
  • 2006-2008: Co-investigator for the European FP6 project WAter Risk Management EuRope (WARMER), Grant agreement ID: FP6-034472, Grant: €2,449,674
  • 2001-2004: CoPI for the NP SDR project. Grant: 500,000 S$
Selected recent publications

Published papers: total 140 (100 in journals, and 40 in refereed conferences). For an up-to-date publication record, visit Oliver's Google Scholar page.

Papers with over 100 citations in Google Scholar (as of 26 September 2022):

R. Acharya, O. Faust, N. Kannathal, T. Chua, and S. Laxminarayan. Non-linear analysis of eeg signals at various sleep stages. Computer Methods and Programs in Biomedicine, 80(1), pp. 37–45, 2005.

O. Faust, U. R. Acharya, L. C. Min, and B. H. Sputh. Automatic identification of epileptic and background eeg signals using frequency domain parameters. International Journal of Neural Systems, 20(2), pp. 159–176, 2010.

O. Faust, R. Acharya, A. R. Allen, and C. Lin. Analysis of eeg signals during epileptic and alcoholic states using ar modeling techniques. Irbm, 29(1), pp. 44–52, 2008.

O. Faust, R. Acharya U, E. Y.-K. Ng, K.-H. Ng, J. S. Suri et al. Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. Journal of Medical Systems, 36(1), pp. 145–157, 2012.

U. R. Acharya, O. Faust, S. V. Sree, F. Molinari, R. Garberoglio, and J. Suri. Cost- effective and non-invasive automated benign & malignant thyroid lesion classification in 3d contrast-enhanced ultrasound using combination of wavelets and textures: a class of thyroscan™ algorithms. Technology in Cancer Research & Treatment, 10(4), pp. 371–380, 2011.

R. U. Acharya, O. Faust, A. P. C. Alvin, S. V. Sree, F. Molinari, L. Saba, A. Nico- laides, and J. S. Suri. Symptomatic vs. asymptomatic plaque classification in carotid ultrasound. Journal of Medical Systems, 36(3), pp. 1861–1871, 2012.

S. V. Sree, E. Y.-K. Ng, R. U. Acharya, and O. Faust. Breast imaging: A survey. WJCO, 2(4), p. 7, 2011.

U. R. Acharya, E. C.-P. Chua, O. Faust, T.-C. Lim, and L. F. B. Lim. Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters. Physiological Measurement, 32(3), p. 287, 2011.

U. R. Acharya, O. Faust, S. V. Sree, F. Molinari, and J. S. Suri. Thyroscreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform. Computer Methods and Programs in Biomedicine, 107(2), pp. 233–241, 2012.

F. Oliver, A. U. Rajendra, S. Krishnan, and M. Lim. Analysis of cardiac signals using spatial filling index and time-frequency domain. BioMedical Engineering OnLine, 3(30), pp. 1–11, 2004.

O. Faust and M. G. Bairy. Nonlinear analysis of physiological signals: a review. Journal of Mechanics in Medicine and Biology, 12(4), p. 1240015, 2012.

U. R. Acharya, O. Faust, N. A. Kadri, J. S. Suri, and W. Yu. Automated identification of normal and diabetes heart rate signals using nonlinear measures. Computers in Biology and Medicine, 43(10), pp. 1523–1529, 2013.

U. R. Acharya, O. Faust, V. Sree, G. Swapna, R. J. Martis, N. A. Kadri, and J. S. Suri. Linear and nonlinear analysis of normal and cad-affected heart rate signals. Computer Methods and Programs in Biomedicine, 113(1), pp. 55–68, 2014.

O. Faust, U. R. Acharya, E. Ng, T. J. Hong, and W. Yu. Application of infrared thermography in computer aided diagnosis. Infrared Physics & Technology, 66, pp. 160–175, 2014.

U. R. Acharya, O. Faust, F. Molinari, S. V. Sree, S. P. Junnarkar, and V. Sudarshan. Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm. Knowledge-Based Systems, 75, pp. 66–77, 2015.

O. Faust, U. R. Acharya, H. Adeli, and A. Adeli. Wavelet-based eeg processing for computer-aided seizure detection and epilepsy diagnosis. Seizure, 26, pp. 56–64, 2015.

U. R. Acharya, S. Bhat, O. Faust, H. Adeli, E. C.-P. Chua, W. J. E. Lim, and J. E. W. Koh. Nonlinear dynamics measures for automated eeg-based sleep stage detection. European Neurology, 74(5-6), pp. 268–287, 2015.

O. Faust, Y. Hagiwara, T. J. Hong, O. S. Lih, and U. R. Acharya. Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161, pp. 1–13, 2018.

O. Faust, A. Shenfield, M. Kareem, T. R. San, H. Fujita, and U. R. Acharya. Auto- mated detection of atrial fibrillation using long short-term memory network with rr interval signals. Computers in Biology and Medicine, 102, pp. 327–335, 2018.
O. S. Lih, V. Jahmunah, T. R. San, E. J. Ciaccio, T. Yamakawa, M. Tanabe, M. Kobayashi, O. Faust, and U. R. Acharya. Comprehensive electrocardiographic diagnosis based on deep learning. Artificial Intelligence in Medicine, 103, p. 101789, 2020.