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Article

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

1
Department of Engineering Science, Faculty of Engineering, University of Auckland, Auckland 1142, New Zealand
2
Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
*
Author to whom correspondence should be addressed.
Joint senior authors.
Sensors 2020, 20(5), 1424; https://doi.org/10.3390/s20051424
Received: 29 January 2020 / Revised: 27 February 2020 / Accepted: 3 March 2020 / Published: 5 March 2020
(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)
Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic–ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia–ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80–120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms. View Full-Text
Keywords: hypoxic–ischemic encephalopathy (HIE); automatic detection and quantification; high-frequency micro-scale gamma spikes; spectral Fourier transform analysis; fuzzy; electroencephalogram (EEG); electrocorticogram (ECoG) hypoxic–ischemic encephalopathy (HIE); automatic detection and quantification; high-frequency micro-scale gamma spikes; spectral Fourier transform analysis; fuzzy; electroencephalogram (EEG); electrocorticogram (ECoG)
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MDPI and ACS Style

Abbasi, H.; Gunn, A.J.; Bennet, L.; Unsworth, C.P. 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 2020, 20, 1424. https://doi.org/10.3390/s20051424

AMA Style

Abbasi H, Gunn AJ, Bennet L, Unsworth CP. 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. 2020; 20(5):1424. https://doi.org/10.3390/s20051424

Chicago/Turabian Style

Abbasi, Hamid, Alistair J. Gunn, Laura Bennet, and Charles P. Unsworth. 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, no. 5: 1424. https://doi.org/10.3390/s20051424

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