基于心电信号联合特征的脑力负荷分类算法研究_毕业论文.pdf
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2023-10-27
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II
Research on Mental Workload Classification Algorithm
Based on ECG Co-dimension Features
Abstract
Nowadays, the working scenes of human-computer interaction are more and
more abundant. Identifying the workload level of the operator's brain and giving
timely warning can effectively reduce the incidence of accidents, ensure life safety
and improve work efficiency to a certain extent. Electrocardiogram (ECG) can not be
artificially controlled and forged, and the acquisition of ECG is safe, cheap,
noninvasive, easy to use, and will not affect the daily operation of personnel engaged
in dangerous types of work. The technology of automatic ECG signal analysis is
currently an important topic in the classification of mental workload. However, due to
individual ECG signal differences and noise impact, it is still faced with many
difficulties to identify it accurately and efficiently. Based on this, the main research
and analysis work of this thesis are as follows: First, with regard to ECG signal
preprocessing, aiming at the sudden abnormalities in ECG signal, this paper proposes
a frequency correlation method according to the similar frequency domain
composition of each heartbeat of ECG signal. This method accurately and
automatically eliminates the interference signal segments without manual intervention
by setting the threshold value, which lays the foundation for subsequent feature
extraction and mental workload classification, It also provides a new idea for other
similar ECG signal processing. Wavelet transform soft threshold denoising is applied
to ensure the overall quality of ECG signal. Combined with notch filter and
differential threshold method, accurate R-wave position is obtained, which lays a
foundation for subsequent feature extraction. Secondly, in terms of ECG feature
extraction, this paper extracts the features of ECG signals under low, medium and
high mental workload levels, and extracts 14 features in total. It includes the more
mature time domain, frequency domain and nonlinear features of Heart Rate
Variability (HRV) in the field of mental workload research, as well as the innovative
frequency domain feature TP wave power, QRS complex power and nonlinear feature
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