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A Fatigue Driving Detection Method Fused with EEG and ECG Signals

A technology of ECG signal and detection method, applied in diagnostic recording/measurement, medical science, sensor, etc., to achieve the effect of improving accuracy and emphasizing comprehensiveness

Active Publication Date: 2016-10-05
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that the fatigue monitoring method with only a single fatigue monitoring index has limitations, the present invention proposes a method of directly fusing EEG and ECG features on the feature layer of the data and then classifying them with a support vector machine to obtain a single feature Fusion feature method with good classification effect

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  • A Fatigue Driving Detection Method Fused with EEG and ECG Signals
  • A Fatigue Driving Detection Method Fused with EEG and ECG Signals
  • A Fatigue Driving Detection Method Fused with EEG and ECG Signals

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Embodiment Construction

[0015] Such as figure 1 As shown, the raw data of 15 channels Fz, Pz, Oz, Fp1, Fp2, F7, F3, F4, F8, C3, C4, P7, P3, P4 and P8 are recorded by the gUSBamp amplifier.

[0016] The processing unit of EEG data is 1s, select the original EEG data x(n)={x(0),x(1),..., x(N-1)}, use the Welch method to estimate the power spectrum, select the Hanning window function w(n) for window processing, then calculate the power spectrum estimation of the M frequency band of the channel w:

[0017] I M ( w ) = 1 MU | Σ n = 0 M - 1 x ( n ) w ...

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Abstract

The invention relates to a method for detecting fatigue driving by fusing EEG and ECG signals. The present invention obtains the original data of EEG and ECG through the 16-channel gUSBamp amplifier, and extracts the power spectrum characteristic data of EEG——EEG fatigue index and , after the phase synchronization feature data of EEG—Pz‑Fz and P3‑P4 in the MPC of the delta frequency band, the time domain feature data of ECG—HR and the frequency domain feature data of ECG—LF / HF, the EEG heart The electrical feature data are directly fused at the feature data level, and then classified using SVM. The invention emphasizes the comprehensiveness of decision-making information, and the classification effect of fusion features is generally better than that of single features, and two orthogonal physiological indicators are used to detect driving fatigue, which helps to improve the accuracy of detection.

Description

technical field [0001] The invention relates to a driver fatigue driving detection method in road traffic, in particular to a method for directly merging EEG and ECG features on the feature layer of data and then using a support vector machine for classification so as to obtain fusion with a better classification effect than a single feature Feature method, and the recognition rate obtained by this method is relatively improved compared with the recognition rate of single signal classification. Background technique [0002] When the driver is driving with fatigue, it will be manifested physiologically: the EEG signal will change with the change of the mental load of the human body, and the heart rate will decrease with the deepening of fatigue. [0003] It has been proved by experiments that the current fatigue driving monitoring basically uses a single fatigue detection index, and the classification error rate itself is high, and the traditional classification methods (such...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/18
Inventor 孔万增周凌霄周慧敏徐飞鹏周展鹏
Owner HANGZHOU DIANZI UNIV
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