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
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 firs...
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MDPI AG
2020-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/5/1424 |
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| author | Hamid Abbasi Alistair J. Gunn Laura Bennet Charles P. Unsworth |
| author_facet | Hamid Abbasi Alistair J. Gunn Laura Bennet Charles P. Unsworth |
| author_sort | Hamid Abbasi |
| collection | DOAJ |
| description | 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. |
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| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T01:44:30Z |
| publishDate | 2020-03-01 |
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| series | Sensors |
| spelling | doaj.art-e87e0cae7b614653bd8b40e74b6533002022-12-22T02:19:36ZengMDPI AGSensors1424-82202020-03-01205142410.3390/s20051424s20051424Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy ClassifiersHamid Abbasi0Alistair J. Gunn1Laura Bennet2Charles P. Unsworth3Department of Engineering Science, Faculty of Engineering, University of Auckland, Auckland 1142, New ZealandDepartment of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New ZealandDepartment of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New ZealandDepartment of Engineering Science, Faculty of Engineering, University of Auckland, Auckland 1142, New ZealandPremature 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.https://www.mdpi.com/1424-8220/20/5/1424hypoxic–ischemic encephalopathy (hie)automatic detection and quantificationhigh-frequency micro-scale gamma spikesspectral fourier transform analysisfuzzyelectroencephalogram (eeg)electrocorticogram (ecog) |
| spellingShingle | Hamid Abbasi Alistair J. Gunn Laura Bennet Charles P. Unsworth 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 hypoxic–ischemic encephalopathy (hie) automatic detection and quantification high-frequency micro-scale gamma spikes spectral fourier transform analysis fuzzy electroencephalogram (eeg) electrocorticogram (ecog) |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | hypoxic–ischemic encephalopathy (hie) automatic detection and quantification high-frequency micro-scale gamma spikes spectral fourier transform analysis fuzzy electroencephalogram (eeg) electrocorticogram (ecog) |
| url | https://www.mdpi.com/1424-8220/20/5/1424 |
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