Dr Hamid Abbasi

PhD, MSE, BSE

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Research Fellow

Biography

Hamid is a post-doctoral Research Fellow in the Auckland Bioengineering Institute (ABI) and an active member of Early Career Researchers Committees at the Center for Brain Research and the ABI at the University of Auckland.

Hamid is a machine-learning expert with a strong interest in leveraging the utility of artificial intelligence in neuroscience and healthcare. He is passionate about multidisciplinary research and developing practical solutions that translate from the lab to the clinic to improve patient experience and enhance the way clinicians practice.

He received his Ph.D. in 2018 in Engineering Science and holds Masters and Bachelor degrees in control, electronics, and instrumentation Engineering. He is also an active member of the IEEE Brain community and the IEEE Engineering in Medicine and Biology Society (EMBS).

Research | Current

Injury to the developing fetal or infant brain (i.e., due to hypoxic-ischemic encephalopathy, perinatal stroke, and infection) can cause severe impairments that lead to various life-long neurodevelopmental disorders. My current research is mainly focused on the game-changing applications of novel signal and image processing techniques, namely deep-learning, to find prognostic solutions.

A robust automatic scheme can assist with a well-timed diagnosis that can further help with early administration of intervention during the period in which the infant’s brain’s plasticity is still high.

In collaboration with the Department of Physiology at the University of Auckland, I have successfully developed deep-learning algorithms that can accurately identify translational EEG biomarkers of hypoxic ischemic brain injury at birth. The designed pattern identifier can precisely classify EEG biomarkers from background activity and artifact, in real-time, with highly competitive accuracies compared to clinical experts (tested on TBs of data). My findings justify why the early administration of neuroprotective treatment (Hypothermia), within the first 2 hours of birth, is optimal to prevent the spread of brain injury.

I am also collaborating with researchers from the Department of Exercise Sciences and the Liggins Institute where we have developed deep-learning algorithms that can automatically track infants' motions in standard video recordings with remarkable accuracies. The key outcome of this research will be a clinical platform for early diagnosis of neurodevelopmental disorders (including CP) in at-risk infants.

Finally, I am  interested in developing further insights for future expansions, including further capabilities of deep-learning strategies in biomedical engineering and neuroscience, along with employing high-tech facilities to achieve ultimate outcomes.

 

Research interests

  • Computational Neuroscience,
  • Computational Neurophysiology,
  • Advanced signal and image processing,
  • Machine learning, deep-learning, and convolutional neural networks (CNN)
  • Biomedical and medical sciences
  • Brain, neurophysiology, neuroscience, and cardiology
  • Infant General Movement Assessment
  • Neural networks, wavelets and wavelet-based neural networks, fuzzy systems
  • Control and instrumentation,
  • Data monitoring and analysis,
  • Non-linear predictive models
     

Google scholar profile

 

Research group

Signal processing
Musculoskeletal system
Fetal Physiology and Neuroscience Group
Infant General Movement Assessment

Teaching | Current

  • ENGSCI 313 - Mathematical Modelling 3ECE
  • ENGGEN 140 - Engineering Biology and Chemistry
  • ENGSCI211
  • ENGSCI313
  • ENGSCI712

Distinctions/Honours

  • 2021, Royal Society of New Zealand's Award to present an innovative idea at Falling Walls Lab New Zealand competition
  • 2021, Most innovative poster at Auckland Bioengineering institute Research Forum (Honourable mention)
  • 2016, University of Auckland Doctoral Scholarship
  • 2013-2017 New Zealand Health Research Council (HRC) Scholarship, 
  • 2014, UniServices Commercialization Prize, Spark $100k Challenge (with BabyAware)
  • 2014, Engineering Postgraduate Poster Competition, 2nd place in Engineering Science

Areas of expertise

  • Computational Neurophysiology,
  • Advanced signal and image processing,
  • Biomedical engineering,
  • Machine learning, deep-learning, and deep Convolutional Neural Networks (CNN)
  • Neural networks, wavelets and wavelet-based neural networks, fuzzy systems
  • Data monitoring and analysis,
  • Electrical, control and instrumentation engineering,

Committees/Professional groups/Services

  • Member of Early Career Researchers committee at the Center for Brain Research

  • Member of Early Career Researchers committee at the Auckland Bioengineering Institute

  • Former member of Faculty of Medical and Health Sciences Post-doctoral Society at the University of Auckland - 2018

  • Member of the Institute of Electrical and Electronics Engineers (IEEE)

  • Member of IEEE Signal Processing Society

  • Member of Journal of Biomedical and Health Informatics, IEEE

  • Member of IEEE Young Professionals
  • Member of the Exposure organizing committee, the University of Auckland, 2014 and 2015 (Posters Team Lead)
  • Member of the executive committee of the Velocity/Spark, the University of Auckland, 2015 (Research Team Lead)

Selected publications and creative works (Research Outputs)

As of 29 October 2020 there will be no automatic updating of 'selected publications and creative works' from Research Outputs. Please continue to keep your Research Outputs profile up to date.
  • Abbasi, H., Saberi, S., Zarvani, M., Amiri, P., & Azmi, R. (2020). Deep Learning Classification Schemes for the Identification of COVID-19 Infected Patients using Large Chest X-ray Image Dataset. IEEE Engineering in Medicine and Biology Society (EMBC) Montreal, Canada. Related URL.
    URL: http://hdl.handle.net/2292/51786
  • Abbasi, H., Gunn, A. J., Bennet, L., & Unsworth, C. P. (2020). Wavelet Spectral Deep-training of Convolutional Neural Networks for Accurate Identification of High-Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG of Preterm Sheep. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 1011-1014. Montreal, Canada: IEEE. 10.1109/EMBC44109.2020.9176397
    Other University of Auckland co-authors: Alistair Gunn, Laura Bennet
  • Abbasi, H., Gunn, A. J., Bennet, L., & Unsworth, C. P. (2020). Deep Convolutional Neural Network and Reverse Biorthogonal Wavelet Scalograms for Automatic Identification of High Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 1015-1018. Montreal, Canada: IEEE. 10.1109/EMBC44109.2020.9176499
    Other University of Auckland co-authors: Alistair Gunn, Laura Bennet
  • Abbasi, H., Gunn, A. J., Unsworth, C. P., & Bennet, L. (2020). Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Montreal, Canada: IEEE. 10.1109/EMBC44109.2020.9176057
    Other University of Auckland co-authors: Alistair Gunn, Laura Bennet
  • Abbasi, H., Gunn, A. J., Unsworth, C. P., & Bennet, L. (2020). Deep Convolutional Neural Networks for the Accurate Identification of High-Amplitude Stereotypic Epileptiform Seizures in the Post-Hypoxic-Ischemic EEG of Preterm Fetal Sheep. Paper presented at 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20), Montreal, Canada. 20 July - 24 July 2020. Related URL.
    Other University of Auckland co-authors: Alistair Gunn, Laura Bennet
  • Abbasi, H., & Unsworth, C. P. (2020). Electroencephalogram studies of hypoxic ischemia in fetal and neonatal animal models. Neural Regeneration Research, 15 (5), 828-837. 10.4103/1673-5374.268892
  • Abbasi, H., Gunn, A. J., Bennet, L., & Unsworth, C. P. (2020). Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers. Sensors, 20 (5)10.3390/s20051424
    Other University of Auckland co-authors: Alistair Gunn, Laura Bennet
  • Abbasi, H., & Unsworth, C. P. (2020). Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram. Neural Regeneration Research, 15 (2), 222-231. 10.4103/1673-5374.265542
    Other University of Auckland co-authors: Charles Unsworth