Electric vehicles have been rapidly developing worldwide due to the use of new energy.However,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread atte...Electric vehicles have been rapidly developing worldwide due to the use of new energy.However,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread attention.The lack of datasets in real vehicle test environments has always been a bottleneck in the research of driver fatigue in electric vehicles.Therefore,this study establishes a dataset from real vehicle test,applies the Bayesian optimization support vector machine(BOA-SVM)algorithm to take features of electromyography(EMG)and electrocardiography(ECG)signals as input and develop an early warning model for driving fatigue detection.Firstly,the driver’s EMG and ECG signals are collected through real vehicle testing experiments and then combined with the driver’s subjective fatigue evaluation scores to establish the dataset.Secondly,the study establishes a driver fatigue early warning model for emergency situations.Time-domain and frequency-domain features are extracted from the EMG signals.Principal component analysis(PCA)is applied for dimensionality reduction of these features.The experimental results show that based on the input of dimensionality reduced EMG features and ECG features,the BOA-SVM algorithm achieved an accuracy of 94.4%in classification.展开更多
This paper describes the development of a new ECG tele-monitoring method and system based on the embedded web server. The system consists of ECG recorders with network interface and the embedded web server, internet n...This paper describes the development of a new ECG tele-monitoring method and system based on the embedded web server. The system consists of ECG recorders with network interface and the embedded web server, internet networks and computers, with the system operating on browser/server(B/S) mode. The ECG recorder was designed by ARM9 (S3C2410X) and embedded operating system (Linux). Once the ECG recorder has been connected to the internet network, medical experts can use the internet to access the server of the ECG recorder, monitor ECG signals, and diagnose patients by browsing the dynamic web pages in the embedded web server. The experimental results reveal that the designed system is stable, reliable, and suitable for the use in real-time ECG tele-monitoring for both family and community health care.展开更多
In this study,a machine learning algorithm is proposed to be used in the detection of Obstructive Sleep Apnea(OSA)from the analysis of single-channel ECG recordings.Eighteen ECG recordings from the PhysioNet Apnea-ECG...In this study,a machine learning algorithm is proposed to be used in the detection of Obstructive Sleep Apnea(OSA)from the analysis of single-channel ECG recordings.Eighteen ECG recordings from the PhysioNet Apnea-ECG dataset were used in the study.In the feature extraction stage,dynamic time warping and median frequency features were obtained from the coefficients obtained from different frequency bands of the ECG data by using the wavelet transform-based algorithm.In the classification phase,OSA patients and normal ECG recordings were classified using Random Forest(RF)and Long Short-Term Memory(LSTM)classifier algorithms.The performance of the classifiers was evaluated as 90% training and 10%testing.According to this evaluation,the accuracy of the RF classifier was 82.43% and the accuracy of the LSTM classifier was 77.60%.Considering the results obtained,it is thought that it may be possible to use the proposed features and classifier algorithms in OSA classification and maybe a different alternative to existing machine learning methods.The proposed method and the feature set used are promising because they can be implemented effectively thanks to low computing overhead.展开更多
目的:设计一种心电信号无线传输系统,以提高动态心电监护仪中心电信号的无线传输性能。方法:该系统由心电信号采集模块、无线收发模块、无线通信协议模块和数字滤波模块组成。心电信号采集模块由数字接口电路、A/D转换器微处理器、心电...目的:设计一种心电信号无线传输系统,以提高动态心电监护仪中心电信号的无线传输性能。方法:该系统由心电信号采集模块、无线收发模块、无线通信协议模块和数字滤波模块组成。心电信号采集模块由数字接口电路、A/D转换器微处理器、心电放大电路组成。无线收发模块由无线射频单元、晶振电路、射频电路、调试串口、寄存器、电源模块和复位电路组成。无线通信协议模块由数据链路层与物理层组成,其中数据链路层设计自动应答和调频2种机制。数字滤波模块主要由数字滤波器、信号输入模块、延时单元、系数寄存器等组成,其中数字滤波器采用等波纹法设计,并将心电信号转换为输出序列,实现信号去噪。将基于ZigBee组网和通用分组无线业务(general packet radio service,GPRS)的心电信号无线传输方法和基于无线组网模块的心电信号无线传输方法作为对比方法,验证该系统在不同近程传输距离和不同远程传输距离下的心电信号无线传输性能。结果:相比其他2种方法,在不同近程传输距离和不同远程传输距离下,该系统的心电信号无线传输平均速率和成功传输比例较高,平均用时及平均重传数较低。结论:该系统能够实现动态心电监护仪中心电信号高效、平稳、清晰的无线传输。展开更多
基金Supported by the Key Research and Development Program of Ningbo(No.2023Z218)the Joint Funds of the National Natural Science Founda-tion of China(No.U21A20121)+1 种基金the National Natural Science Foundation of China(No.51775325)the Young Eastern Scholars Program of Shanghai(No.QD2016033).
文摘Electric vehicles have been rapidly developing worldwide due to the use of new energy.However,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread attention.The lack of datasets in real vehicle test environments has always been a bottleneck in the research of driver fatigue in electric vehicles.Therefore,this study establishes a dataset from real vehicle test,applies the Bayesian optimization support vector machine(BOA-SVM)algorithm to take features of electromyography(EMG)and electrocardiography(ECG)signals as input and develop an early warning model for driving fatigue detection.Firstly,the driver’s EMG and ECG signals are collected through real vehicle testing experiments and then combined with the driver’s subjective fatigue evaluation scores to establish the dataset.Secondly,the study establishes a driver fatigue early warning model for emergency situations.Time-domain and frequency-domain features are extracted from the EMG signals.Principal component analysis(PCA)is applied for dimensionality reduction of these features.The experimental results show that based on the input of dimensionality reduced EMG features and ECG features,the BOA-SVM algorithm achieved an accuracy of 94.4%in classification.
基金Education Committee Foundation of Beijing grant number: KM200610005022+1 种基金Young Backbone Teacher Foundation of Beijing grant number: 102KB00845
文摘This paper describes the development of a new ECG tele-monitoring method and system based on the embedded web server. The system consists of ECG recorders with network interface and the embedded web server, internet networks and computers, with the system operating on browser/server(B/S) mode. The ECG recorder was designed by ARM9 (S3C2410X) and embedded operating system (Linux). Once the ECG recorder has been connected to the internet network, medical experts can use the internet to access the server of the ECG recorder, monitor ECG signals, and diagnose patients by browsing the dynamic web pages in the embedded web server. The experimental results reveal that the designed system is stable, reliable, and suitable for the use in real-time ECG tele-monitoring for both family and community health care.
文摘In this study,a machine learning algorithm is proposed to be used in the detection of Obstructive Sleep Apnea(OSA)from the analysis of single-channel ECG recordings.Eighteen ECG recordings from the PhysioNet Apnea-ECG dataset were used in the study.In the feature extraction stage,dynamic time warping and median frequency features were obtained from the coefficients obtained from different frequency bands of the ECG data by using the wavelet transform-based algorithm.In the classification phase,OSA patients and normal ECG recordings were classified using Random Forest(RF)and Long Short-Term Memory(LSTM)classifier algorithms.The performance of the classifiers was evaluated as 90% training and 10%testing.According to this evaluation,the accuracy of the RF classifier was 82.43% and the accuracy of the LSTM classifier was 77.60%.Considering the results obtained,it is thought that it may be possible to use the proposed features and classifier algorithms in OSA classification and maybe a different alternative to existing machine learning methods.The proposed method and the feature set used are promising because they can be implemented effectively thanks to low computing overhead.
文摘目的:设计一种心电信号无线传输系统,以提高动态心电监护仪中心电信号的无线传输性能。方法:该系统由心电信号采集模块、无线收发模块、无线通信协议模块和数字滤波模块组成。心电信号采集模块由数字接口电路、A/D转换器微处理器、心电放大电路组成。无线收发模块由无线射频单元、晶振电路、射频电路、调试串口、寄存器、电源模块和复位电路组成。无线通信协议模块由数据链路层与物理层组成,其中数据链路层设计自动应答和调频2种机制。数字滤波模块主要由数字滤波器、信号输入模块、延时单元、系数寄存器等组成,其中数字滤波器采用等波纹法设计,并将心电信号转换为输出序列,实现信号去噪。将基于ZigBee组网和通用分组无线业务(general packet radio service,GPRS)的心电信号无线传输方法和基于无线组网模块的心电信号无线传输方法作为对比方法,验证该系统在不同近程传输距离和不同远程传输距离下的心电信号无线传输性能。结果:相比其他2种方法,在不同近程传输距离和不同远程传输距离下,该系统的心电信号无线传输平均速率和成功传输比例较高,平均用时及平均重传数较低。结论:该系统能够实现动态心电监护仪中心电信号高效、平稳、清晰的无线传输。
