Mr Pranavan Somaskandhan

Postdoctoral Research Fellow

Child Health Research Centre

p.somaskandhan@uq.edu.au

Researcher biography

Pranavan Somaskandhan is currently a Postdoctoral Research Fellow at the Children's Health Research Centre, University of Queensland (UQ), where he is part of the Community Sleep Health Group led by Professor Simon Smith. His research expertise lies in applying artificial intelligence techniques to sleep research and physiological signal analysis.

His PhD thesis at UQ focused on developing deep learning methods for reliable and physiology-aligned sleep scoring. During his doctoral studies, he received the Richard Jago Memorial Prize from the School of Electrical Engineering and Computer Science at UQ (2022) and was recognised as a New Investigator Award Finalist at the Sleep DownUnder 2024 conference.

Pranavan holds a Bachelor of Science in Computer Engineering with First-Class Honours and has experience across both academic and industry settings. As a Research Fellow, he contributes to the Healthy Child program by implementing machine learning approaches to better understand how digital exposure influences sleep health.

Publications

Journal Articles (3)

Conference Papers (5)

Journal Articles

Sigurdardottir, Sigridur, Pitkänen, Henna, Korkalainen, Henri, Kainulainen, Samu, Serwatko, Marta, Olafsdottir, Kristin A., Sigurðardóttir, Sigurveig Þ., Clausen, Michael, Somaskandhan, Pranavan, Stražišar, Barbara G., Leppänen, Timo and Arnardottir, Erna Sif (2025). Validation of manually scored multichannel frontal electroencephalography against polysomnography in a paediatric cohort. Journal of Sleep Research e70012. doi: 10.1111/jsr.70012

Nikkonen, Sami, Somaskandhan, Pranavan, Korkalainen, Henri, Kainulainen, Samu, Terrill, Philip I., Gretarsdottir, Heidur, Sigurdardottir, Sigridur, Olafsdottir, Kristin Anna, Islind, Anna Sigridur, Óskarsdóttir, María, Arnardóttir, Erna Sif and Leppänen, Timo (2023). Multicentre sleep‐stage scoring agreement in the Sleep Revolution project. Journal of Sleep Research, 33 (1) e13956, 1-13. doi: 10.1111/jsr.13956

Somaskandhan, Pranavan, Leppänen, Timo, Terrill, Philip I., Sigurdardottir, Sigridur, Arnardottir, Erna Sif, Ólafsdóttir, Kristín A., Serwatko, Marta, Sigurðardóttir, Sigurveig Þ., Clausen, Michael, Töyräs, Juha and Korkalainen, Henri (2023). Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls. Frontiers in Neurology, 14 1162998, 1-12. doi: 10.3389/fneur.2023.1162998

Conference Papers

Somaskandhan, P., Korkalainen, H., Leppänen, T., Töyräs, J., Melehan, K., Wilson, D., Ruehland, W., Mann, D. and Terrill, P. (2024). O004 Incorporating arousals into sleep vs. wakefulness classification outperforms traditional binary classification at 1-second epoch resolution. Sleep DownUnder 2024, Gold Coast, QLD Australia, 16-19 October 2024. Oxford, United Kingdom: Oxford University Press. doi: 10.1093/sleepadvances/zpae070.004

Sigurdardottir, S., Pitkänen, H., Korkalainen, H., Kainulainen, S., Serwatko, M., Olafsdottir, K.A., Sigurðardóttir, S.þ., Clausen, M., Somaskandhan, P., Stražišar, B.G., Leppänen, T. and Arnardóttir, E.S. (2024). Multi-channel frontal EEG – validation on manual sleep staging in a pediatric cohort. 17th World Sleep Congress, Rio de Janeiro, Brazil, 20-25 October 2023. Amsterdam, Netherlands: Elsevier. doi: 10.1016/j.sleep.2023.11.749

Somaskandhan, P., Terrill, P., Korkalainen, H., Kainulainen, S., Leppänen, T., Islind, A., Grétarsdóttir, H. and Nikkonen, S. (2022). A detailed analysis of multicentric sleep staging inter-rater variabilities. 33rd annual scientific meeting of Australasian Sleep Association (ASA) & Australian and New Zealand Sleep Science Association (ANZSSA) Sleep DownUnder 2022, Brisbane, QLD Australia, 8-11 November 2022. Oxford, United Kingdom: Oxford University Press. doi: 10.1093/sleepadvances/zpac029.182

Somaskandhan, P., Korkalainen, H., Terrill, P., Sigurðardóttir, S., Arnardóttir, E., Ólafsdóttir, K., Sigurðardóttir, S., Clausen, M., Töyräs, J. and Leppänen, T. (2021). Deep learning enables accurate automatic sleep stage classification in a clinical paediatric population. Sleep Down Under 2021: Australasian Sleep Association Conference, Online, 10-13 October 2021. Oxford, United Kingdom: Oxford University Press. doi: 10.1093/sleepadvances/zpab014.178

Somaskandhan, Pranavan, Wijesinghe, Gihan, Wijegunawardana, Leshan Bashitha, Bandaranayake, Asitha and Deegalla, Sampath (2017). Identifying the optimal set of attributes that impose high impact on the end results of a cricket match using machine learning. 2017 IEEE International Conference on Industrial and Information Systems (ICIIS), Peradeniya, Sri Lanka, 15-16 December 2017. Danvers, MA USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/iciinfs.2017.8300399