Use of Time-Frequency Transforms and Kernel PCA to Classify Epileptic Patients from Control Subjects

2010 ◽  
pp. 465-472 ◽  
Author(s):  
Samaneh Kazemifar ◽  
Reza Boostani
Keyword(s):  
Kernel Pca ◽  
Time Frequency ◽  
Control Subjects ◽  
Use Of Time ◽  
Epileptic Patients

scholarly journals Cerebellar Atrophy in Epileptic Patients

1988 ◽  
Vol 15 (3) ◽  
pp. 299-303 ◽  
Author(s):  
M.I. Botez ◽  
Ezzedine Attig ◽  
Jean Lorrain Vézina
Keyword(s):  
Statistical Analysis ◽  
High Resolution ◽  
Posterior Fossa ◽  
De Novo ◽  
Cerebellar Atrophy ◽  
Ct Scans ◽  
Control Subjects ◽  
Generalized Seizures ◽  
Epileptic Patients ◽  
The Brain

ABSTRACT:High-resolution CT scans of the brain and posterior fossa were performed on 106 phenytoin (PHT)- treated epileptics, 28 de novo epileptics and 43 control subjects. A higher incidence of cerebellar and brainstem (CBS) atrophy was observed in chronic PHT- or PHT+ phenobarbital-treated epileptics compared to the two other groups. Some control subjects and de novo epileptics presented mild CBS atrophy, whereas moderate to severe atrophy was noted exclusively in chronically-treated patients. In attempting to delineate the etiology of CBS atrophy, epileptic patients were divided in three groups: 55 subjects with normal CT scans, 30 with both cerebral and CBS atrophy, and 49 with pure CBS atrophy. Their ages, length of illness, number of generalized seizures, number of other seizures, and amount of PHT received during their lifetime were assessed. Statistical analysis revealed that posterior fossa atrophy in epileptics was significantly correlated with both the length of the illness and the amount of PHT ingested during the patient's lifetime. The number of seizures appears to not be related to CBS atrophy.

The use of time-frequency domain electromagnetic (TFEM) technique to monitor hydraulic fracturing

2018 ◽  
Author(s):  
Wang Zhigang ◽  
Zhang Lin ◽  
Xu jianhua ◽  
Wang Chongyang ◽  
Liu Zihao
Keyword(s):  
Hydraulic Fracturing ◽  
Frequency Domain ◽  
Time Frequency ◽  
Use Of Time

Fiber grating synthesis by use of time–frequency representations

1998 ◽  
Vol 23 (19) ◽  
pp. 1526 ◽  
Author(s):  
Miguel A. Muriel ◽  
José Azaña ◽  
Alejandro Carballar
Keyword(s):  
Fiber Grating ◽  
Time Frequency ◽  
Use Of Time

scholarly journals 0452 Morlet Wavelet Transforms and Neural Networks in Polysomnography Analysis

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A173-A174
Author(s):  
R Stretch ◽  
M Zeidler
Keyword(s):  
Neural Networks ◽  
Wavelet Transforms ◽  
Sleep Stage ◽  
Scoring Systems ◽  
Time Frequency ◽  
Use Of Time ◽  
Lower Stage ◽  
Data Representations ◽  
Validation Set ◽  
Larger Sample

Abstract Introduction Manually scoring polysomnograms is both time-consuming and labor-intensive. It also increases variability in care. Generating features for use as components within larger models is an important part of building highly accurate auto-scoring systems. In this study, we examined the use of time-frequency data representations in combination with a convolutional neural networks (CNN). Methods We used just six (6) pre-scored polysomnograms from the MrOS dataset in this analysis. Only one electroencephalography (EEG) and one electrooculography (EOG) channel were extracted from each polysomnogram and split into 30 second epochs. Visual representations of each epoch in the time-frequency domain were generated using Morlet wavelets, then divided into training and validation sets in a 4:5 distribution. We then re-trained a ResNet-50 CNN using transfer learning to classify sleep stage based on the time-frequency representations. Results A total of 4971 epochs were generated. Of those, 1242 epochs formed the validation set. Performance was high for identifying Stage W with an accuracy of 94.2% (295/313 epochs). However, performance for other stages was considerably lower. Stage N3 was predicted correctly in 68.0% of cases (138/203 epochs), although in 60/75 cases of misclassification the predicted class was Stage N2. Similarly, Stage N2 was predicted correctly in 62.0% of cases (183/295 epochs), and in 63/112 cases of misclassification the predicted class was Stage N3. Accuracy for Stage REM was 64.9%. Stage N1 prediction was poor (22.0% accuracy), likely due to insufficient representation in the sample (< 10% of epochs). Conclusion This exploratory analysis of the use of time-frequency representations in conjunction with a CNN demonstrates some promise, especially with respect to prediction of Stage W using this technique. Inclusion of additional data channels and larger sample size would likely improve accuracy. Support RS - ASPIRE Fellowship (sponsored by the American Thoracic Association).

Use of time frequency analysis to follow transitory modulation of the cardiac autonomic system in clinical studies

2001 ◽  
Vol 90 (1-2) ◽  
pp. 24-28 ◽  
Author(s):  
Christophe Baillard ◽  
Paulo Gonçalves ◽  
Laurence Mangin ◽  
Bernard Swynghedauw ◽  
Pascale Mansier
Keyword(s):  
Frequency Analysis ◽  
Clinical Studies ◽  
Autonomic System ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Use Of Time
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