Simon Hartmann

Simon Hartmann

Adelaide Medical School

Faculty of Health and Medical Sciences

Eligible to supervise Masters and PhD - 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 is currently a Visiting Research Fellow at the Discipline of Psychiatry at the Adelaide Medical School and a Honorary Research Fellow at the Centre for Youth Mental Health at the University of Melbourne.

Predictive modelling and machine learning in mental illness

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 / PhD / Masters / Mphil

 

Speech and video biomarker extraction for mental health monitoring

The assessment of a patient’s mental health poses a complex challenge to clinicians. Structured interviews or questionnaires capturing a patient’s state are infrequently used in clinical practice resulting in a lack of standardised or systematically recorded data in mental health care. Hence, there is a need for objective measures that can be useful to identify mental illnesses. Due to the shift to online mental health assessment during Covid-19, there is increasing potential to facilitate care via extraction of video and speech features. This project aims to implement automated video and speech features extraction and processing to provide cross-sectional diagnostic and prognostic information.

Potential projects available for: Honours / PhD / 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

  • Appointments
    Date Position Institution name
    2025 - ongoing Visiting Research Fellow The University of Adelaide
    2022 - ongoing Honorary Research Fellow University of Melbourne
    2021 - 2024 NHMRC Grant Funded Researcher The University of Adelaide

  • Language Competencies
    Language Competency
    English Can read, write, speak, understand spoken and peer review
    German Can read, write, speak, understand spoken and peer review

  • Education
    Date Institution name Country Title
    2017 - 2021 The University of Adelaide Australia PhD
    2014 - 2017 Karlsruhe Institute of Technology, Karlsruhe Germany Master of Science
    2010 - 2014 University of Ulm Germany Bachelor of Science

  • Research Interests
    Biomedical Engineering Pattern Recognition and Data Mining Signal Processing
  • ECMS Travelling Scholarship, The University of Adelaide, 2020
  • Lab2Lab, TU Dresden, 2021

  • Current Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2023 Co-Supervisor Investigating the substrate processes underlying the relationship between cognitive functioning and Anomalous Self-Experience, in adolescents with first-episode psychosis. Doctor of Philosophy Doctorate Full Time Mr James Christopher Martin

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