scholarly journals Characterization of Brain Stroke Using Image and Signal Processing Techniques

2021 ◽  
Author(s):  
Abdullah Alamoudi ◽  
Yousif Abdallah
Keyword(s):  
Signal Processing ◽  
Brain Injuries ◽  
Cross Sectional ◽  
Manual Contour ◽  
Cross Sectional Imaging ◽  
Brain Stroke ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques

Cross-sectional imaging approaches play a key role in assessing bleeding brain injuries. Doctors commonly determine bleeding size and severity in CT and MRI. Separating and identifying artifacts is extremely important in processing medical images. Image and signal processing are used to classify tissues within images closely linked to edges. In CT images, a subjective process takes a stroke ‘s manual contour with less precision. This chapter presents the application of both image and signal processing techniques in the characterization of Brain Stroke field. This chapter also summarizes how to characterize the brain stroke using different image processing algorithms such as ROI based segmentation and watershed methods.

scholarly journals Demystifying signal processing techniques to extract resting-state EEG features for psychologists

2020 ◽  
Vol 6 (3) ◽  
pp. 189-209 ◽  
Author(s):  
Zhenjiang Li ◽  
Libo Zhang ◽  
Fengrui Zhang ◽  
Ruolei Gu ◽  
Weiwei Peng ◽  
...  
Keyword(s):  
Signal Processing ◽  
Resting State ◽  
Neural Dynamics ◽  
Complex Signal ◽  
Eeg Signal ◽  
Microstate Analysis ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques ◽  
Eeg Signal Processing

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

Analysis of atrial fibrillation: from electrocardiogram signal processing to clinical management

2008 ◽  
Vol 367 (1887) ◽  
pp. 235-253 ◽  
Author(s):  
Leif Sörnmo ◽  
Martin Stridh ◽  
Daniela Husser ◽  
Andreas Bollmann ◽  
S. Bertil Olsson
Keyword(s):  
Atrial Fibrillation ◽  
Signal Processing ◽  
Clinical Management ◽  
Advanced Characterization ◽  
Non Invasive ◽  
Therapy Efficacy ◽  
Electrocardiogram Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

The analysis of atrial fibrillation in non-invasive ECG recordings has received considerable attention in recent years, spurring the development of signal processing techniques for more advanced characterization of the atrial waveforms than previously available. The present paper gives an overview of different approaches to the extraction of atrial activity in the ECG and to the characterization of the resulting atrial signal with respect to its spectral properties. So far, the repetition rate of the atrial waves is the most studied parameter and its significance in clinical management is briefly considered, including the identification of pathomechanisms and prediction of therapy efficacy.

scholarly journals EEG Calmness Index Establishment Using Computational of Z-Score

2018 ◽  
Vol 7 (4.11) ◽  
pp. 44
Author(s):  
S. A. M. Aris1 ◽  
N. A. Bani ◽  
M. N.Muhtazaruddin ◽  
M. N. Taib
Keyword(s):  
Signal Processing ◽  
Asymmetry Index ◽  
Eeg Signal ◽  
Z Score ◽  
Fuzzy C Means ◽  
Processing Techniques ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Signal Processing Techniques ◽  
Brain Condition

A lot of useful information can be obtained through observation of the electroencephalogram (EEG) signal such as the human psychophysiology. It has been proven that EEG is handy in human diagnosis and tools to observe the brain condition. The study aims to establish a calmness index, which can differentiate the calmness level of an individual. Alpha waves were selected as the data features and computed into asymmetry index. The data features were clustered using Fuzzy C-Means (FCM) and resulted in three clusters. Wilcoxon Signed Ranks test was applied to determine the significance of the data features clustered by FCM. The Z-score obtained successfully distinguish three level of calmness index from the lower index until the higher index. With the advancement of signal processing techniques, the feature extractions for calmness index establishment computation is achievable.  

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