基于互信息心率变异性分析的心电疲劳检测

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Junartho Halomoan, Kalamullah Ramli, Dodi Sudiana, Teddy Surya Gunawan, Muhammad Salman
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引用次数: 0

摘要

世卫组织减少道路交通伤害和死亡的战略之一是加强车辆安全。驾驶疲劳检测可以提高车辆的安全性。我们之前的研究利用AdaBoost开发了一个基于心电图的驾驶疲劳检测框架,交叉验证准确率高达98.82%,测试准确率高达81.82%;然而,本研究在分类模型中并未考虑驾驶员与疲劳和冗余特征相关的认知状态。在本文中,我们提出了特征提取和特征选择阶段在驾驶疲劳检测框架的发展。在特征提取方面,采用心率碎片化提取非线性特征,分析驾驶员的认知状态。这些特征与心率变异性分析在时间、频率和非线性域获得的特征相结合。在特征选择上,采用互信息过滤冗余特征。为了找到具有最佳模型性能的选择特征的数量,我们进行了28个组合实验,包括从58个特征中选择7个可能的特征和4个集成学习。实验结果表明,选取44个特征的随机森林算法模型性能测试准确率为95.45%,交叉验证准确率为98.65%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ECG-Based Driving Fatigue Detection using Heart Rate Variability Analysis with Mutual Information
One of the WHO’s strategies to reduce road traffic injuries and fatalities is to enhance vehicle safety. Driving fatigue detection can be used to increase vehicle safety. Our previous study developed an ECG-based driving fatigue detection framework with AdaBoost, producing a high cross-validated accuracy of 98.82% and a testing accuracy of 81.82%; however, the study did not consider the driver’s cognitive state related to fatigue and redundant features in the classification model. In this paper, we propose developments in the feature extraction and feature selection phases in the driving fatigue detection framework. For feature extraction, we employ heart rate fragmentation to extract non-linear features to analyze the driver’s cognitive status. These features are combined with features obtained from heart rate variability analysis in the time, frequency, and non-linear domains. In feature selection, we employ mutual information to filter redundant features. To find the number of selected features with the best model performance, we carried out 28 combination experiments consisting of 7 possible selected features out of 58 features and 4 ensemble learnings. The results of the experiments show that the random forest algorithm with 44 selected features produced the best model performance testing accuracy of 95.45%, with cross-validated accuracy of 98.65%.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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