Simon Hartmann

Simon Hartmann

Adelaide Medical School

Faculty of Health and Medical Sciences

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.

Simon Hartmann is a biomedical engineer interested in the fascinating intersection of advanced signal processing and human physiology. He is passionate about developing tools to enhance the understanding of the role of the human brain with a particular expertise in biomedical devices, physiological signals, and state-of-the-art signal processing methods including machine learning techniques.

He graduated with a B.Sc. degree in Electrical and Electronic Engineering from the University of Ulm, Germany, in 2014. In 2017, he received the M.Sc. degree in Electrical and Electronic Engineering from the Karlsruhe Institute of Technology, Germany. For his Master thesis, he developed a chest-worn sensor-patch to enable convenient screening for sleep-disordered breathing. Following his master studies, he started his Ph.D. degree with the School of Electrical and Electronic Engineering at the University of Adelaide, Australia, under the supervision of M. Baumert, and D. Abbott. In 2021, he successfully finished his Ph.D. degree.

He is currently a NHMRC Grant Funded Researcher at the Discipline of Psychiatry, The University of Adelaide, Australia, working on multi-modal risk prediction of early psychosis onset as part of the Centre of Research Excellence (CRE) in PREdiction of Early Mental Disorder and Preventive Treatment (PRE-EMPT).

Multimodal prediction of transition to the first psychotic episode

Only 30% of patients identified as at high risk of a psychotic episode transition to first episode psychosis. Improved accuracy of prediction is required to efficiently and safely intervene to prevent or minimise the impact of psychosis. As part of PRE-EMPT, the CRE for PREdiction of Early Mental Disorder and Preventive Treatment, we have access to detailed national and international data sets to develop novel Bayesian and machine learning techniques to combine clinical and biological variables to improve prediction accuracy.

Potential projects available for: Honours / HDR / Masters / Mphil

 

Big data analysis of cyclic alternating pattern (CAP) using machine learning

With the surge in wearable devices in recent years, the topic of what is high-quality sleep, how can it be determined and how can it be achieved attracted increasing interest. In the last two decades, cyclic alternating pattern (CAP) was introduced as a scoring alternative to traditional sleep staging. CAP is known as a synonym for sleep microstructure and describes oscillating brain waves defined as short EEG amplitude increases (<60 s) during NREM stages that are in tune with the rest of the body. In collaboration with leading research laboratories all over the world, we work on developing an automated CAP scoring algorithm which can be applied on large population based studies to investigate the role of CAP.

Potential projects available for: Honours

  • ECMS Travelling Scholarship, The University of Adelaide, 2020
  • Lab2Lab, TU Dresden, 2021

  • Other Supervision Activities
    Date Role Research Topic Location Program Supervision Type Student Load Student Name
    2020 - 2021 Co-Supervisor Coupling between QT-interval variability and heart rate variability during sleep TU Dresden Other Full Time Xinjing Jiang

  • Memberships
    Date Role Membership Country
    2019 - 2020 Member IEEE United States
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