Emotion Recognition Technique Using Complex Biomedical Signal Analysis

2013 ◽  
pp. 631-640
Author(s):  
Guyoun Hwang ◽  
Heejun Cho ◽  
Dongkyoo Shin ◽  
DongIl Shin
Keyword(s):  
Emotion Recognition ◽  
Signal Analysis ◽  
Biomedical Signal ◽  
Biomedical Signal Analysis ◽  
Recognition Technique

Near-infrared biomedical signal analysis system

1999 ◽  
Author(s):  
Jiangsheng Yu ◽  
Qingming Luo ◽  
Dan Zhu ◽  
Qiang Lu ◽  
Ruan Yu
Keyword(s):  
Signal Analysis ◽  
Near Infrared ◽  
Biomedical Signal ◽  
Biomedical Signal Analysis ◽  
Analysis System

Practical Biomedical Signal Analysis Using MATLAB®

2021 ◽  
Author(s):  
Katarzyna J. Blinowska ◽  
Jarosław Żygierewicz
Keyword(s):  
Signal Analysis ◽  
Biomedical Signal ◽  
Biomedical Signal Analysis

Biomedical Signal Analysis and Its Physio-Clinical Perspectives

2018 ◽  
pp. 1-12
Author(s):  
Kamlesh Jha
Keyword(s):  
Biomedical Engineering ◽  
Signal Analysis ◽  
Clinical Settings ◽  
Engineering Science ◽  
Data Types ◽  
Biomedical Signal ◽  
Biomedical Signal Analysis ◽  
Prime Concern ◽  
Product Delivery ◽  
State Of Art

The field of Biomedical engineering has brought two apparently diagonally placed poles of academia of excellence, i.e., field of medicine and the field of state of art engineering science to a closed proximity. Now a day most if not all of the state of art diagnostics in the field of medicine are almost totally dependent upon biomedical signal analysis. Whole of the biological systems are run by nothing but the bio-signals. The process of signal analysis depends upon the types of signals, recording methods, data types, need of compression and portability and possibility of artifacts. The important areas of the clinician's prime concern are the reliability of the data generated, the utility of the data produced in the real clinical settings in making a diagnosis and interference of the diverse type of equipment's signals with each other and its impact upon the final output. Physiologists act as a bridge between the biomedical engineering and the clinician's need assessment and product delivery process.

scholarly journals F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
R. M. Dünki ◽  
M. Dressel
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Signal Analysis ◽  
Feature Vector ◽  
Spectral Power ◽  
Ratio Test ◽  
Test Statistics ◽  
Biomedical Signal ◽  
Statistical Assessment ◽  
Biomedical Signal Analysis

Reducing a feature vector to an optimized dimensionality is a common problem in biomedical signal analysis. This analysis retrieves the characteristics of the time series and its associated measures with an adequate methodology followed by an appropriate statistical assessment of these measures (e.g., spectral power or fractal dimension). As a step towards such a statistical assessment, we present a data resampling approach. The techniques allow estimating σ2(F), that is, the variance of an F-value from variance analysis. Three test statistics are derived from the so-called F-ratio σ2(F)/F2. A Bayesian formalism assigns weights to hypotheses and their corresponding measures considered (hypothesis weighting). This leads to complete, partial, or noninclusion of these measures into an optimized feature vector. We thus distinguished the EEG of healthy probands from the EEG of patients diagnosed as schizophrenic. A reliable discriminance performance of 81% based on Taken's χ, α-, and δ-power was found.

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