Superiority of high frequency hypoxic ischemic EEG signals of fetal sheep for sharp wave detection using Wavelet-Type 2 Fuzzy classifiers

2014 ◽  
Author(s):  
Hamid Abbasi ◽  
Charles P. Unsworth ◽  
Alistair J. Gunn ◽  
Laura Bennet
Keyword(s):  
High Frequency ◽  
Eeg Signals ◽  
Fetal Sheep ◽  
Wave Detection ◽  
Sharp Wave ◽  
Fuzzy Classifiers

Robust Wavelet Stabilized ‘Footprints of Uncertainty’ for Fuzzy System Classifiers to Automatically Detect Sharp Waves in the EEG after Hypoxia Ischemia

2017 ◽  
Vol 27 (03) ◽  
pp. 1650051 ◽  
Author(s):  
Hamid Abbasi ◽  
Laura Bennet ◽  
Alistair J. Gunn ◽  
Charles P. Unsworth
Keyword(s):  
Sheep Model ◽  
Fetal Sheep ◽  
Large Variability ◽  
Sharp Wave ◽  
Sharp Waves ◽  
Hypoxia Ischemia ◽  
Footprint Of Uncertainty ◽  
Electroencephalogram Eeg ◽  
Logic System

Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroencephalogram (EEG) in order to determine if these micro-scale transients are potential biomarkers of HI. A major issue with sharp waves in the HI-EEG is that they possess a large variability in their sharp wave profile making it difficult to build a compact ‘footprint of uncertainty’ (FOU) required for ideal performance of a Type-2 fuzzy logic system (FLS) classifier. In this paper, we develop a novel computational EEG analysis method to robustly detect sharp waves using over 30[Formula: see text]h of post occlusion HI-EEG from an equivalent, in utero, preterm fetal sheep model cohort. We demonstrate that initial wavelet transform (WT) of the sharp waves stabilizes the variation in their profile and thus permits a highly compact FOU to be built, hence, optimizing the performance of a Type-2 FLS. We demonstrate that this method leads to higher overall performance of [Formula: see text] for the clinical [Formula: see text] sampled EEG and [Formula: see text] for the high resolution [Formula: see text] sampled EEG that is improved upon over conventional standard wavelet [Formula: see text] and [Formula: see text], respectively, and fuzzy approaches [Formula: see text] and [Formula: see text], respectively, when performed in isolation.

scholarly journals 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 ◽  
Vol 20 (5) ◽  
pp. 1424 ◽  
Author(s):  
Hamid Abbasi ◽  
Alistair J. Gunn ◽  
Laura Bennet ◽  
Charles P. Unsworth
Keyword(s):  
High Frequency ◽  
Phase Identification ◽  
Type I ◽  
Accurate Identification ◽  
Fetal Sheep ◽  
Micro Scale ◽  
Fuzzy Classifiers ◽  
The Right ◽  
Latent Phase

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.

scholarly journals Prevalent ALMS1 Pathogenic Variants in Spanish Alström Patients

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 282
Author(s):  
Brais Bea-Mascato ◽  
Carlos Solarat ◽  
Irene Perea-Romero ◽  
Teresa Jaijo ◽  
Fiona Blanco-Kelly ◽  
...  
Keyword(s):  
High Frequency ◽  
Haplotype Analysis ◽  
Genetic Diagnosis ◽  
Allelic Frequency ◽  
Structural Protein ◽  
Alström Syndrome ◽  
Pathogenic Variants ◽  
Novel Variants ◽  
Alstrom Syndrome

Alström syndrome (ALMS) is an ultrarare disease with an estimated prevalence lower than 1 in 1,000,000. It is associated with disease-causing mutations in the Alström syndrome 1 (ALMS1) gene, which codifies for a structural protein of the basal body and centrosomes. The symptomatology involves nystagmus, type 2 diabetes mellitus (T2D), obesity, dilated cardiomyopathy (DCM), neurodegenerative disorders and multiorgan fibrosis. We refined the clinical and genetic diagnosis data of 12 patients from 11 families, all of them from Spain. We also studied the allelic frequency of the different variants present in this cohort and performed a haplotype analysis for the most prevalent allele. The genetic analysis revealed 2 novel homozygous variants located in the exon 8, p.(Glu929Ter) and p.(His1808GlufsTer20) in 2 unrelated patients. These 2 novel variants were classified as pathogenic after an in silico experiment (computer analysis). On the other hand, 2 alleles were detected at a high frequency in our cohort: p.(Tyr1714Ter) (25%) and p.(Ser3872TyrfsTer19) (16.7%). The segregation analysis showed that the pathogenic variant p.(Tyr1714Ter) in 3 families is linked to a rare missense polymorphism, p.(Asn1787Asp). In conclusion, 2 novel pathological mutations have been discovered in homozygosis, as well as a probable founder effect in 3 unrelated families.

scholarly journals Assessment of Carotid Atherosclerosis in Type 2 Diabetes Mellitus Patients with Microalbuminuria by High-Frequency Ultrasonography

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yu-Hong Zhang ◽  
Yuan Gao ◽  
Xin Mao ◽  
Jing Shang ◽  
Ben-Li Su
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
High Frequency ◽  
Carotid Atherosclerosis ◽  
Univariate Analysis ◽  
Diabetic Patients ◽  
Multivariate Linear Regression Analysis ◽  
High Frequency Ultrasonography

The aim of this study is to evaluate carotid atherosclerosis in patients of type 2 diabetes mellitus with microalbuminuria (MA) by high-frequency ultrasonography. Two hundred and fifty patients of type 2 diabetes mellitus were divided into two groups according to urinary albumin excretion rate (UAER): normoalbuminuria group (130 cases) and microalbuminuria group (120 cases). The intimal-medial thickness (IMT) and the atherosclerotic plaques of carotid artery were observed in both groups by high-frequency ultrasound. Fasting blood glucose (FBG), hemoglobin A1c, and lipid profiles were measured. The values of IMT of microalbuminuria group were significantly higher than those of normoalbuminuria group (P<0.05). In univariate analysis, IMT was positively and significantly associated with age (r=0.265,P<0.05), waist circumference (r=0.263,P<0.05), body mass index (r=0.285,P<0.05), systolic blood pressure (r=0.276,P<0.05), UAER (r=0.359,P<0.05), HbA1c (r=0.462,P<0.05) and, duration of diabetes (r=0.370,P<0.05). In multivariate linear regression analysis, UAER and HbA1c were independent predictors of IMT (P<0.05for all). In the two groups, the rate of soft plaques was higher than that of dense plaques and calcified plaques. In conclusion, there is a significant association between microalbuminuria and IMT which is regarded as the early sign of carotid atherosclerosis in type 2 diabetic patients.

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