The Psychophysiological Diagnostics of the Functional State of the ATHLE TE. Preliminary Data

Ignat A. Dubynin, Evgenij S. Isaychev, Alexandr D. Korolev, Alexsander M. Chernorizov, Sergey A. Isaychev, Il'ya M. Zakharov
2012 Psychology in Russia: State of Art  
The original experimental scheme was developed to investigate athletes' functionalstates (FS) dynamics. The procedure allowed modeling various FS importantfor predicting the professional success of athletes: psychological and physiologicalstress, fatigue, and optimal FS (OFS). There were two main criteria fordifferentiation of the FS under study: efficiency rates and the psychological andphysiological costs of the achieved efficiency level. Analysis of the FS-dependentpsychophysiological
more » ... showed significant interindividual differences on anumber of parameters. Thus, no single indicator could be used as effective diagnosticsfor the FS criteria. A minimum number of indicators need to be recordedincluded cardiovascular indicators (heart rate, ECG), respiration, muscle tension(EMG), and brain activity (EEG) in the range of alpha and beta waves. The mainproblem can be artifacts induced by movement and muscle tension. The specialprocedure for artifact rejection and reduction of the artifacts was developed. Itallowed recording EEG, ECG, and EOG signals simultaneously. Another problemwas related to the development of the mathematical algorithm to analyze individualdata and differentiate patterns of the signals recorded from the athletes.An original approach to differentiate the FS – the k-means clustering algorithm –was offered based on seven psychophysiological indicators. Results of clusteringshowed that the k-means algorithm for seven-component vectors allows onewith confidence to differentiate state of quiet wakefulness, states of psychologicaland physiological stress. As the number of parameters used is attenuatedfrom seven to four (without the EEG parameters) the accuracy of distinguishing FS is significantly reduced. To construct a complete and accurate differentiationof an athlete's FS one should collect some statistical data on the dynamics ofeach FS in different time periods of the person's life – in the process of training,after successful competition, and after losing competition.
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