Event Details

Maia Angelova is a Professor of Data Analytics and Machine Learning at Deakin University. Currently, she is the Director of the Data Analytics Research Lab at the School of IT. This talk is in person, Infolab, C60b,c

Precision healthcare is an emerging approach for personal healthcare and disease prevention that considers the individual’s variability in health, genes, lifestyle and environment. Sleep and nutrition are essential and repeating processes which are vital for the quality of life and wellbeing of every individual. These processes involve complex dynamics and regulation at multi-scale that reflect the developmental changes in mental and physical health, along with the day-to-day fluctuations.

In this talk, I will share our latest research and describe how our data-driven models, based on physical and physiological data, can assist in the early detection of insomnia and diabetes and provide a robust basis for pre-screening of individuals with wearable devices. I will consider the following case studies based on our most recent projects. (a) Classification of chronic back pain from multi-modal data: we use image, questionnaires and physical activities data to classify chronic low back pain [1]. (b) Classification of insomnia and sleep from physical activities (actigraphy) data. Our models can distinguish acute from chronic insomnia and healthy sleep [2,3,4]. They provide a robust basis for pre-screening of insomnia with wearable devices in a home environment. (c) Glucose-insulin regulation models: glucose-insulin dynamics is central for understanding the regulation mechanisms between different organs in the human body and is key to maintain healthy life and prevent diabetes. We combine dynamical systems approach with machine learning algorithms to model the regulation between glucose and insulin and predict glucose dynamics and insulin utilisation in healthy and pre-diabetic regimes [5,6].

References:

1.Tagliaferri, S.D., Wilkin, T., Angelova, M. et al. Chronic back pain sub-grouped via psychosocial, brain and physical factors using machine learning. Sci Rep 12, 15194 (2022). https://doi.org/10.1038/s41598-022-19542-5.

2.Rani, S., Shelyag, S., Karmakar, C., Zhu, Ye, Fossion, R., Ellis, Jason, Drummond, S. P. A. and Angelova, M. (2022) Differentiating acute from chronic insomnia with machine learning from actigraphy time series data. Frontiers in Network Physiology, 2. p. 1036832. ISSN 2674-0109.

3.M. Angelova, C. Karmakar, Y. Zhu, S. P. A. Drummond and J. Ellis, "Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data," in IEEE Access, vol. 8, pp. 74413-74422, 2020, doi: 10.1109/ACCESS.2020.2988722.

4.Kusmakar, Shitanshu, et al. "A machine learning model for multi-night actigraphic detection of chronic insomnia: development and validation of a pre-screening tool." Royal Society open science 8.6 (2021): 202264, https://doi.org/10.1109/TBME.2018.2845865.

5.Angelova, M., Beliakov, G., Ivanov, A. Shelyag, S. 2021. Global stability and periodicity in a glucose-insulin regulation model with a single delay. CNSNS 95, 105659, https://doi.org/10.1016/j.cnsns.2020.105659.

6. Huard, B., Bridgewater, A. and Angelova, M. 2017. Mathematical investigation of diabetically-impaired ultradian oscillations in the glucose-insulin regulation. J Theor Biol 418: 66-76, doi:10.1016/j.jtbi.2017.01.039.

Maia Angelova is a Professor of Data Analytics and Machine Learning at Deakin University. Currently, she is the Director of the Data Analytics Research Lab at the School of IT. Maia is the founding director of Data to Intelligence Research Centre, which she directed from 2019 to 2021. Maia’s expertise is in data science, applied mathematics and complex systems. She has interests in data-driven modelling of sleep and insomnia, diabetes, dementia, depression, ageing and decision making. She has strong expertise in time series, machine learning, data analytics, spectral analysis, dynamical systems and symmetry. Maia’s research is at the boundary between theory and applications. She is increasingly interested in translational research into the areas of health, medicine and healthcare. Her research is being funded by the National Health and Medical Research Council of Australia, Australian Defence, The Academy of Medical Sciences, EPSRC, MRC, European FP6 and FP7 Programs, The Royal Society, The London Mathematical Society, The Australian Mathematical Society and AMSI. Maia is a Fellow of The Institute of Physics, member of the Council of Complex Systems Society, member of Society of Mathematical Biology, The London Mathematical Society and The Australian Mathematical Society. She is an Associate Editor of the journals of Complexity, Frontiers of Physiology: Network Physiology, member of the Editorial Boards of Frontiers of Endocrinology, Frontiers of Physics, Bioinformatics and Biology Insights. She is a member of Program Committees and Technical Committees of several international conferences in data science and complex systems.