Journal of South China University of Technology(Natural Science) >

2023 , Vol. 51 >Issue 7: 139 - 150

DOI: https://doi.org/10.12141/j.issn.1000-565X.220571

Traffic & Transportation Engineering

Identification Model of Driver’s Cerebrovascular Diseases Based on PPG Pulse Signal Features

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  • 1.College of Design and Engineering，National University of Singapore，117575，Singapore
    2.Guangdong Road Transportation Affairs Center，Guangzhou 510101，Guangdong，China
    3.School of Civil Engineering and Transportation，South China University of Technology，Guangzhou 510640，Guangdong，China
    4.Modern Urban Transportation Technology Jiangsu University Collaborative Innovation Center，Southeast University，Nanjing 210096，Jiangsu，China
张嘉讯（2000-），女，博士生，主要从事智能交通检测预警技术研究。E-mail:e0954475@u.nus.edu

Received date: 2022-09-02

  Online published: 2023-01-19

Supported by

the Natural Science Foundation of Guangdong Province(2023A1515010742);the Key Project of Guangdong Regional Joint Fund(2020B1515120095)

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Abstract

The physical condition of drivers is closely related to traffic safety, especially the driver’s cardiovascular health condition. Real-time monitoring of drivers’ health can help drivers understand their physical condition in time and reduce traffic accidents caused by sudden illnesses. In this study, firstly, 657 PPG (Photoplethysmography Signal) pulse wave datasets from Guilin People’s Hospital, Guangxi Zhuang Autonomous Region, China, were dichotomized numerically for cerebrovascular diseases after the noise reduction by Chebyshev Ⅱ filter and the extraction of time domain features, frequency domain features and wavelet packet features by fast Fourier method. Then, the numerically labeled cerebrovascular disease types were used as output parameters to construct driver cerebrovascular disease dataset. To solve the problem of unbalanced classification of samples in actual dataset, an oversampling supplement was performed by the SMOTE algorithm and a driver cerebrovascular disease classification model, namely SSA-DELM, was constructed based on PPG feature values, followed with model training and testing on actual datasets. The results show that the proposed classification model can provide comparatively accurate early warning for drivers suffering from cerebral infarction or cerebrovascular disease, with an accuracy of 83%, an average precision of 80%, a completeness of 76.6%, an F1 score of 0.79, and a mean average precision of 0.80. This research can provide theoretical model basis and technical support for drivers’ dynamic health monitoring system based on PPG signal. The proposed model has a large application space in the software service and intelligent medical care of the new energy automobile industry, which is in line with the sales mode of the whole industry chain of “terminal + software + service” of new energy automobile enterprises, and is also in line with modern people’s attention to environmental protection, family health and intelligent transportation.

Key words： PPG signal; driver; disease identification; dynamic health monitoring; SMOTE algorithm; SSA-DELM model

Cite this article

ZHANG Jiaxun, ZHENG Qiuna, YU Zhenyu, et al . Identification Model of Driver’s Cerebrovascular Diseases Based on PPG Pulse Signal Features[J]. Journal of South China University of Technology(Natural Science), 2023 , 51(7) : 139 -150 . DOI: 10.12141/j.issn.1000-565X.220571

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