Mr Hamid Abbasi

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Research Assistant
Doctoral Candidate - Doctor of Philosophy

Research | Current

PhD Thesis

Title: Early prediction of perinatal brain injury in the EEG using wavelets and artificial neural networks

Abstract:

Hypoxia around the time of birth is a significant cause of death among preterm infants, but survival is often associated with debilitating morbidity and lifelong disabilities such as learning difficulties, ADHD, and cerebral palsy.

Experiments have shown that brain injury evolves exponentialy over time. However, fortunately, there is a window of opportunity during the first 3-6 hours after birth, in which the brain cells are still recoverable and we call it the latent phase. Neuroprotective treatments such as therapeutic hypothermia (or brain cooling) could only be effective to stop the spread of brain injury if started early on in this phase.

This research focuses on the significant advantage that could be obtained using advanced signal processing of high frequency sampled Electroencephalography (EEG) signals to automatically detect subtle abnormal brain activities that occur during the latent phase post asphyxia, right when the brain injury is still treatable. The key outcomes of this study will help clinicians to treat at-risk babies during which the neural injury is in its primitive levels and before it becomes irreversible.
 

Supervisors:

 

Research interests

  • Control and Instrumentation,
  • Signal Processing,
  • Neural Networks, Fuzzy systems, Wavelets and Wavelet-Based Neural Networks,
  • Intelligent and Adaptive systems,
  • Energy Systems,
  • Data Monitoring and Analysis,
  • Non-Linear Model Predictive Control.
     

Research group

Signal processing

Teaching | Current

ENGSCI 313 - Mathematical Modelling 3ECE

Teaching Assistant - ENGSCI211
Teaching Assistant - ENGSCI313
Teaching Assistant - ENGSCI712

Distinctions/Honours

New Zealand Health Research Council (HRC) Scholarship 

UniServices Commercialization Prize, 2014 Spark $100k Challenge (with BabyAware)
http://www.des.auckland.ac.nz/en/about/newseventsandnotices/news/news-2014/doctoral-students-win-spark-prize-with-babyaware.html
Engineering Postgraduate Poster Competition 2014, 2nd place in Engineering Science

University of Auckland Doctoral Scholarship - 2016

Areas of expertise

  • Control and Instrumentation
  • Signal Processing
  • Neural Networks, Fuzzy systems, Wavelets and Wavelet-Based Neural Networks
  • Intelligent and Adaptive systems
  • Energy Systems
  • Data Monitoring and Analysis
  • Non-Linear Model Predictive Control

Committees/Professional groups/Services

  • 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 Spark, the University of Auckland, 2015 (Research Team Lead)

Selected publications and creative works (Research Outputs)

  • Abbasi, H., Bennet, L., Gunn, A. J., & Unsworth, C. P. (2017). Robust wavelet stabilized footprints of uncertainty for fuzzy system classifiers to automatically detect sharp waves in the EEG after hypoxia ischemia. International Journal of Neural Systems, 27 (3).10.1142/S0129065716500519
    URL: http://hdl.handle.net/2292/32112
    Other University of Auckland co-authors: Alistair Gunn, Laura Bennet, Charles Unsworth
  • Lakadia, M. J., Abbasi, H., Gunn, A. J., Unsworth, C. P., & Bennet, L. (2016). Examining the effect of MgSO4 on sharp wave activity in the hypoxic-ischemic fetal sheep model. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), proceedings of the conference held 16 - 20 August 2016, Orlando, Florida Orlando, Florida, US: IEEE - Institute of Electrical and Electronics Engineers. 10.1109/EMBC.2016.7590848
    URL: http://hdl.handle.net/2292/35927
    Other University of Auckland co-authors: Charles Unsworth, Alistair Gunn, Laura Bennet
  • Abbasi, H., Bennet, L., Gunn, A. J., & Unsworth, C. P. (2016). Identifying stereotypic evolving micro-scale seizures (SEMS) in the hypoxic-ischemic EEG of the pre-term fetal sheep with a wavelet type-II fuzzy classifier. Paper presented at 38th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBC 2016), Orlando, Florida, US. 16 August - 20 August 2016. 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC). (pp. 4). 10.1109/EMBC.2016.7590864
    URL: http://hdl.handle.net/2292/36475
    Other University of Auckland co-authors: Charles Unsworth, Laura Bennet, Alistair Gunn
  • Abbasi, H., Gunn, A. J., Bennet, L., & Unsworth, C. P. (2015). Reverse bi-orthogonal wavelets & fuzzy classifiers for the automatic detection of spike waves in the EEG of the hypoxic ischemic pre-term fetal sheep.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference. 10.1109/embc.2015.7319613
    Other University of Auckland co-authors: Laura Bennet, Alistair Gunn, Charles Unsworth
  • Abbasi, H., Unsworth, C. P., Gunn, A. J., & Bennet, L. (2014). Superiority of high frequency hypoxic ischemic EEG signals of fetal sheep for sharp wave detection using Wavelet-Type 2 Fuzzy classifiers.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference. 10.1109/embc.2014.6943980
    Other University of Auckland co-authors: Charles Unsworth, Laura Bennet, Alistair Gunn
  • Abbasi, H., Unsworth, C. P., McKenzie, A. C., Gunn, A. J., & Bennet, L. (2014). Using type-2 fuzzy logic systems for spike detection in the hypoxic ischemic EEG of the preterm fetal sheep.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference. 10.1109/embc.2014.6943746
    Other University of Auckland co-authors: Charles Unsworth, Laura Bennet, Alistair Gunn