Electrocardiosignal-based mental workload classification method and system

Abstract

The invention relates to an electrocardiosignal-based mental workload classification method and system. The method comprises the following steps: acquiring electrocardiosignals of a to-be-tested person; preprocessing the electrocardiosignals; extracting time domain features of the R-R interval of the preprocessed electrocardiosignals; according to the preprocessed electrocardiosignals, extracting frequency domain features of the electrocardiosignals by utilizing power spectral density under different frequencies; according to the preprocessed electrocardiosignals, extracting nonlinear dynamic features by utilizing a sample entropy algorithm; fusing the time domain features, the frequency domain features and the nonlinear dynamic features of the preprocessed electrocardiosignals, and determining fusion features of the preprocessed electrocardiosignals; and according to the fusion features, performing mental workload classification by employing a support vector machine classifier, and determining a mental workload classification result. According to the method and the system, on the basis of improving the mental workload classification precision, the time required for feeding back the collected electrocardiosignals each time is greatly shortened, thereby achieving the rapid and accurate effect.

Description

technical field [0001] The present invention relates to the field of mental load identification, in particular to a mental load classification method and system based on electrocardiographic signals 。 Background technique [0002] At present, the method of mental load classification based on ECG signals at home and abroad is mainly the Heart Rate Variability (HRV) analysis method. HRV refers to the small changes in the continuous cardiac cycle (R-R interval) or the small fluctuations in the continuous instantaneous heart rate. , mainly divided into time domain analysis, frequency domain analysis and nonlinear analysis. [0003] The existing ECG signal mental load classification method HRV represents a quantitative mapping, that is, by measuring the variability of continuous normal R-R interval changes to reflect the degree and regularity of heart rate changes, so as to judge its impact on cardiovascular activity . All indicators of HRV are based on the R-R interval, and t...

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Problems solved by technology

[0004] Therefore, in view of the shortcomings of the prior art that require a long signal time, it is difficult to respond in real time in practical applications, and the accuracy of mental load classification is low, a new mental load classification method or system is urgently needed, which can improve the accuracy of mental load classification. On the basis of this, the time required to collect ECG signals for each feedback is greatly shortened to achieve fast and accurate results

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