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Analysis of the galvanic skin response
Caha, Martin ; Chmelař, Milan (referee) ; Ronzhina, Marina (advisor)
This work deals with the analysis of skin conductance for the study of the psychophysiological state of man, with a focus on the change in the level of arousal. It is important to study microsleep events during driving, where there are the changes in alertness and consequently the changes in skin conductivity. The experimental protocol is proposed. Skin conductance analysis is realized in Matlab. This program allows drowing reaction time and measured conductivities and calculating their parameters. These data are used for determination of vigilance level.
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ECG classification using methods of HRV analysis
Caha, Martin ; Vítek, Martin (referee) ; Ronzhina, Marina (advisor)
This paper deals with the classification of ECG measured from isolated rabbit hearts during the experiment with repeated ischemia. Classification features were calculated using the methods of heart rate variability analysis. The results were statistically evaluated. Heart rate variability parameters were calculated using Kubios HRV, other calculations were performed in MATLAB. Artificial neural network was created to classify the analyzed parameters to specific groups.
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Analysis of the galvanic skin response
Caha, Martin ; Chmelař, Milan (referee) ; Ronzhina, Marina (advisor)
This work deals with the analysis of skin conductance for the study of the psychophysiological state of man, with a focus on the change in the level of arousal. It is important to study microsleep events during driving, where there are the changes in alertness and consequently the changes in skin conductivity. The experimental protocol is proposed. Skin conductance analysis is realized in Matlab. This program allows drowing reaction time and measured conductivities and calculating their parameters. These data are used for determination of vigilance level.
|
|
|
ECG classification using methods of HRV analysis
Caha, Martin ; Vítek, Martin (referee) ; Ronzhina, Marina (advisor)
This paper deals with the classification of ECG measured from isolated rabbit hearts during the experiment with repeated ischemia. Classification features were calculated using the methods of heart rate variability analysis. The results were statistically evaluated. Heart rate variability parameters were calculated using Kubios HRV, other calculations were performed in MATLAB. Artificial neural network was created to classify the analyzed parameters to specific groups.
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