Alexander Neergaard Zahid

Alexander Neergaard Zahid

Postdoctoral researcher

Technical University of Denmark

Biography

I am a research scientist and biomedical engineer with special interests in signal processing and machine learning for computational sleep science. I design algorithms and data processing pipelines for modeling, analysis and visualisation of physiological signals such as EEG, EOG and EMG obtained from nocturnal PSG recordings. Through this work, I hope to further our collective understanding of sleep pathologies, and how they impact human health.

Currently, I work as a postdoctoral researcher at the Section for Cognitive Systems at the Department of Applied Mathematics and Computer Science, Technical University of Denmark, through a LF Postdoc grant from the Lundbeck Foundation.

Previously, I worked as a self-employed research scientist, where I provided consulting services to academic and industry research groups, which includes data processing and machine learning pipelines, algorithm development, and data visualization.

In 2020, I graduated with a PhD in Biomedical Engineering from the Technical University of Denmark under the supervision of the late Associate Professor Helge B. D. Sørensen, PhD; Professor Poul Jennum, MD, PhD, from the Danish Center for Sleep Medicine; and Professor Emmanuel Mignot, MD, PhD, from Stanford University.

Interests
  • Deep learning
  • Computational sleep science
  • Biomedical signal processing
Education
  • PhD in Biomedical Engineering, 2020

    Technical University of Denmark

  • MScEng in Biomedical Engineering, 2016

    Technical University of Denmark

  • BScEng in Biomedical Engineering, 2013

    Technical University of Denmark

Publications

(2024). Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Models. IEEE EMBC 2024.

Cite

(2023). MSED: A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis. IEEE TBME.

PDF Cite DOI arXiv

(2021). Automatic sleep stage classification with deep residual networks in a mixed-cohort setting. Sleep.

PDF Cite Code DOI arXiv

(2020). Proteomic biomarkers of sleep apnea. Sleep.

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