心电信号发生器是研发智能心电仪器的关键设备,心率连续可变是心电信号发生器研究的难点。提出了一种用DDS(direct digital frequently synthesis)算法合成心率连续可变的多导联心电信号的方法,并在基于ARM的嵌入式实时操作系统uC/OS-I...心电信号发生器是研发智能心电仪器的关键设备,心率连续可变是心电信号发生器研究的难点。提出了一种用DDS(direct digital frequently synthesis)算法合成心率连续可变的多导联心电信号的方法,并在基于ARM的嵌入式实时操作系统uC/OS-II上用软件实现了该方法。首先证明了用软件实现DDS算法产生心电信号的可行性;针对心电信号的频率特点给出了算法的基本参数;并根据DDS算法思想给出了一种查找表(LUT)长度调整的方法;最后分析了本方法误差的主要来源,通过理论推导得出系统误差为26×10-6。实验结果证明本方法行之有效,具有理论和实际意义。展开更多
Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular m...Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many lives.In existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of research.Hence,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals.The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently.The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.展开更多
文摘心电信号发生器是研发智能心电仪器的关键设备,心率连续可变是心电信号发生器研究的难点。提出了一种用DDS(direct digital frequently synthesis)算法合成心率连续可变的多导联心电信号的方法,并在基于ARM的嵌入式实时操作系统uC/OS-II上用软件实现了该方法。首先证明了用软件实现DDS算法产生心电信号的可行性;针对心电信号的频率特点给出了算法的基本参数;并根据DDS算法思想给出了一种查找表(LUT)长度调整的方法;最后分析了本方法误差的主要来源,通过理论推导得出系统误差为26×10-6。实验结果证明本方法行之有效,具有理论和实际意义。
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)Support Program(IITP-2023-2018-0-01799)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and also the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2022R1F1A1063134).
文摘Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many lives.In existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of research.Hence,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals.The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently.The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.
