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Field of Research : Diagnostic Applications
Field of Research : Signal Processing
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  • Funded Activity

    Discovery Projects - Grant ID: DP1094498

    Funder
    Australian Research Council
    Funding Amount
    $150,000.00
    Summary
    Detection and Quantification of General Fetal Movements from Accelerometer Measurements using Nonstationary Signal Processing Techniques. There are approximately 1,750 fetal deaths per year in Australian with about one-third occurring late in gestation and without an apparent cause. The development of an automated system capable of long-term monitoring of fetal health will result in accurate diagnoses and prediction of future outcome. This will, in turn, allow early intervention by the clinicia .... Detection and Quantification of General Fetal Movements from Accelerometer Measurements using Nonstationary Signal Processing Techniques. There are approximately 1,750 fetal deaths per year in Australian with about one-third occurring late in gestation and without an apparent cause. The development of an automated system capable of long-term monitoring of fetal health will result in accurate diagnoses and prediction of future outcome. This will, in turn, allow early intervention by the clinician to reduce fetal deaths and enhance the chances of good outcomes with resultant savings in social and financial costs to the community. The development of such equipment would spawn future research into intervention treatments and contribute to Australia's position as a world leader in computerised health monitoring systems.
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    Funded Activity

    Discovery Projects - Grant ID: DP0665697

    Funder
    Australian Research Council
    Funding Amount
    $240,000.00
    Summary
    Multi-Channel Time-Frequency Analysis for EEG Neonatal Seizure Characterization. This project researches new signal processing methodologies for a multi-channel characterization of seizures for use in diagnosing newborn brain dysfunctions. The outcomes will result in important immediate clinical benefits for sick newborn babies and will fundamentally facilitate research progress in the development of neuroprotectants and anticonvulsants. The success of this project will contribute in minimizing .... Multi-Channel Time-Frequency Analysis for EEG Neonatal Seizure Characterization. This project researches new signal processing methodologies for a multi-channel characterization of seizures for use in diagnosing newborn brain dysfunctions. The outcomes will result in important immediate clinical benefits for sick newborn babies and will fundamentally facilitate research progress in the development of neuroprotectants and anticonvulsants. The success of this project will contribute in minimizing the social financial costs by diagnosing brain disorders in the initial stage of life and preventing further damage. This has the potential to result in a standard diagnostic equipment in neonatal intensive care units and medical research centres.
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    Funded Activity

    Linkage Projects - Grant ID: LP0562317

    Funder
    Australian Research Council
    Funding Amount
    $190,000.00
    Summary
    Design of Neonatal Seizure Diagnosis Methods Using Time-Frequency Signal Processing Techniques. Seizures occur in approximately 0.5% of all newborns. They are often the only indicator of an early dysfunction in central nervous system (CNS). Their occurrence raises concerns about the underlying cause, its effect on the brain, and the appropriate treatment. Newborn seizures are mostly sub-clinical and only detected through the Electroencephalogram. For an efficient diagnosis, seizure classificatio .... Design of Neonatal Seizure Diagnosis Methods Using Time-Frequency Signal Processing Techniques. Seizures occur in approximately 0.5% of all newborns. They are often the only indicator of an early dysfunction in central nervous system (CNS). Their occurrence raises concerns about the underlying cause, its effect on the brain, and the appropriate treatment. Newborn seizures are mostly sub-clinical and only detected through the Electroencephalogram. For an efficient diagnosis, seizure classification systems were proposed based on visual observations. This project proposes developing a novel approach to automate the classification process using time-frequency (TF) signal processing techniques based on the multi-channel characteristics of the seizure; namely: A) TF signature B) origin, and C) propagation behaviour.
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