| Literature DB >> 31719243 |
Hamid Abbasi1, Charles P Unsworth1.
Abstract
Alongside clinical achievements, experiments conducted on animal models (including primate or non-primate) have been effective in the understanding of various pathophysiological aspects of perinatal hypoxic/ischemic encephalopathy (HIE). Due to the reasonably fair degree of flexibility with experiments, most of the research around HIE in the literature has been largely concerned with the neurodevelopmental outcome or how the frequency and duration of HI seizures could relate to the severity of perinatal brain injury, following HI insult. This survey concentrates on how EEG experimental studies using asphyxiated animal models (in rodents, piglets, sheep and non-human primate monkeys) provide a unique opportunity to examine from the exact time of HI event to help gain insights into HIE where human studies become difficult.Entities:
Keywords: EEG; HIE; animal models; automatic detection; clinical; fetal; hypoxic-ischemic encephalopathy; neonatal; non-human primates; review; seizure
Year: 2020 PMID: 31719243 PMCID: PMC6990791 DOI: 10.4103/1673-5374.268892
Source DB: PubMed Journal: Neural Regen Res ISSN: 1673-5374 Impact factor: 5.135
Automated strategies on the detection of epileptiform seizures post a hypoxic-ischemic event
| Reference | Subjects | Number of subjects | Epileptiform events | Number of events | Length of recordings (hours) | Number of experts | EEG acquisition | Sampling frequency (Hz) | |
|---|---|---|---|---|---|---|---|---|---|
| White et al. (2006) | Adult rats | 8 | Spike seizures | 75 | 312 | 1? | Bipolar electrode | 250 | |
| Walbran et al. (2009) | Preterm fetal sheep (0.7 gestation) | 1 | Spike | 374 | 0.5 | 1 | 2 channel ECoG | 64 | |
| Walbran et al. (2011) | Preterm fetal sheep (0.7 gestation) | 1 | Spike | 374 | 0.5 | 1 | 2 channel ECoG | 64 | |
| Cuaycong et al. (2011) | Neonatal rats | 12 | Seizure | 154 | 1080 | 1 | Two channels EEG | 400 | |
| Abbasi et al. (2014) | Preterm fetal sheep (0.7 gestation) | 1 | Spike | 374 | 0.5 | 1 | 2 channel ECoG | 64 | |
| Abbasi et al. (2014) | Preterm fetal sheep (0.7 gestation) | 1 | Sharp | 73 | 0.5 | 1 | 2 channel ECoG | 1024/256 | |
| Abbasi et al. (2015) | Preterm fetal sheep (0.7 gestation) | 1 | High frequency spike | 334 | 0.5 | 1 | 2 channel ECoG | 1024 | |
| Zwanenburg et al. (2015) | Preterm fetal lambs (0.7 gestation) | 17 | Short seizures | 3159 | 1976 | 3 | 2 channel EEG | 1000 down-sampled to 250 | |
| Abbasi et al. (2016a) | Preterm fetal sheep (0.7 gestation) | 1 | Stereotypic Evolving Micro-scale Seizures | 13.5 | 1 | 2 channel ECoG | 1024 | ||
| Tieng et al. (2016) | Neonatal mouse model of epilepsy (7–8 week old) | 4 | High frequency spike | 2014 | 8 | 1 | 2 channel ECoG | 256 | |
| Abbasi et al. (2017) | Preterm fetal sheep (0.7 gestation) | 5 | Sharp | 5186 | 30 | 1 | 2 channel ECoG | 1024/256 | |
| Abbasi et al. (2018) | Preterm fetal sheep (0.7 gestation) | 12 | Sharp | 3984 | 48 | 1 | 2 channel ECoG | 1024 | |
| Abbasi et al. (2019a) | Preterm fetal sheep (0.7 gestation) | 7 | High frequency spike | 3291 | 42 | 1 | 2 channel ECoG | 1024 | |
| Abbasi et al. (2019c) | Preterm fetal sheep (0.7 gestation) | 2 | Sharp | 690 | 3 | 1 | 2 channel ECoG | 1024/256 | |
| Reference | Feature extraction | Algorithm | Sensitivity (%) | Specificity (%) | Selectivity (%) | GDR (%) | ROC (%) | Accuracy (%) | FDR (h–1) |
| White et al. (2006) | Autocorrelation method | 100 | 99.98 | 95 | |||||
| Walbran et al. (2009) | Time-frequency analysis with power thresholding | 77.8 | 83.77 | ||||||
| Walbran et al. (2011) | Thresholded Haar Wavelet transform | 78.7 | 77.7 | ||||||
| Cuaycong et al. (2011) | 70–80 | ||||||||
| Abbasi et al. ( 2014) | A set of features | Fuzzy classifier | 89.06 | 90.3 | |||||
| Abbasi et al. (2014) | Wavelet-Type 2 Fuzzy classifier | 95.96 | 79.49 | ||||||
| Abbasi et al. (2015) | A set of spectral and time domain features from raw EEG | Thresholded reverse biorthogonal Wavelet transform and Fuzzy classifier | 99.1 | 99.4 | |||||
| Zwanenburg et al., (2015) | Non-linear energy operator & wavelet decomposition | Support Vector Machine | 59.5 | 0.033 | |||||
| Abbasi et al. (2016a) | Wavelet-Type 2 Fuzzy classifier | 73.59 | 86.92 | ||||||
| Tieng et al. (2016) | Five EEG features | Adapted CWT-based classifier | 96.72 | 94.69 | |||||
| Abbasi et al. (2017) | A set of time & freq. domain features | Wavelet-Type 2 Fuzzy classifier | 97.4 | ||||||
| Abbasi et al. (2018) | Wavelet-Type 2 Fuzzy classifier | 97.76 | 90.21 | 93.99 | |||||
| Abbasi et al. (2019a) | A set of spectral and time domain features from raw EEG | Thresholded reverse biorthogonal Wavelet transform and Type-1 Fuzzy classifier | 100 | 99.56 | 99.78 | ||||
| Abbasi et al. (2019c) | 2D Wavelet scalograms | Deep Convolutional Neural Network | 100 | 93.57 | 93.66 | 95.34 | |||
AUC: Area under the curve; ECoG: electrocorticography; EEG: electroencephalogram; FDR: false detection rate; GDR: good detection rate; ROC: receiver operating characteristic.