Some heart diseases need long-term monitoring to diagnose. In this paper, we present a wearable single lead ECG monitoring device with low power consumption based on MSP430 and single-lead ECG front-end AD8232, which ...Some heart diseases need long-term monitoring to diagnose. In this paper, we present a wearable single lead ECG monitoring device with low power consumption based on MSP430 and single-lead ECG front-end AD8232, which could acquire and store patient’s ECG data for 7 days continuously. This device is available for long-term wearing with a small volume. Also, it could detect user’s motion status with an acceleration sensor and supports Bluetooth 4.0 protocol. So it could be expanded to be a dynamic heart rate monitor and/or sleep quality monitor combined with smart phone. The device has huge potential of application for health care of human daily life.展开更多
It is very important to redure the body surface electrodes as much as possible in recording ECG clinically. By constructing human heart profile model, based on the characteristic of electrocardic physiology, the autho...It is very important to redure the body surface electrodes as much as possible in recording ECG clinically. By constructing human heart profile model, based on the characteristic of electrocardic physiology, the authors put forward a new method of weighed epicardial potential to study the relationship between body surface potential and epicardial potential. Thus, the unknown body surface potential could be derived by the selected body surface potential. This method has been proved practical in diagnosing and locating myocardial ischemia and myocardial infarction with stardard 12-lead ECG.展开更多
The Lyapunov exponents of synchronous 12-lead ECG signals have been investigated for the first time using a multi-sensor (electrode) technique. The results show that the Lyapunov exponents computed from different loca...The Lyapunov exponents of synchronous 12-lead ECG signals have been investigated for the first time using a multi-sensor (electrode) technique. The results show that the Lyapunov exponents computed from different locations on the body surface are not the same, but have a distribution characteristic for the ECG signals recorded from coronary artery disease (CAD) patients with sinus rhythms and for signals from healthy older people. The maximum Lyapunov exponent L1 of all signals is positive. While all the others are negative, so the ECG signal has chaotic characteristics. With the same leads, L1 of CAD patients is less than that of healthy people, so the CAD patients and healthy people can be classified by L1, L1 therefore has potential values in the diagnosis of heart disease.展开更多
Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-s...Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.展开更多
针对家庭及社区的医疗监护系统需求,设计了一种基于物联网(Internet of Thing,IoT)的多导联心电信号(electrocardiogram,ECG)实时监测系统。本系统利用ADS1298前端实时采集ECG,通过蓝牙和Wi-Fi模块将信号分别发送给Android客户端和云服...针对家庭及社区的医疗监护系统需求,设计了一种基于物联网(Internet of Thing,IoT)的多导联心电信号(electrocardiogram,ECG)实时监测系统。本系统利用ADS1298前端实时采集ECG,通过蓝牙和Wi-Fi模块将信号分别发送给Android客户端和云服务器。在客户端中实现了ECG数据接收、数据解码、中值滤波、LMS自适应限波、QRS波检测以及ECG波形和诊断结果的显示;在云服务器中建立了数据库,用于保存用户的心电数据;还挂载了基于卷积神经网络的疾病诊断模型,可对用户心脏健康状态进行实时监测。展开更多
文摘Some heart diseases need long-term monitoring to diagnose. In this paper, we present a wearable single lead ECG monitoring device with low power consumption based on MSP430 and single-lead ECG front-end AD8232, which could acquire and store patient’s ECG data for 7 days continuously. This device is available for long-term wearing with a small volume. Also, it could detect user’s motion status with an acceleration sensor and supports Bluetooth 4.0 protocol. So it could be expanded to be a dynamic heart rate monitor and/or sleep quality monitor combined with smart phone. The device has huge potential of application for health care of human daily life.
文摘It is very important to redure the body surface electrodes as much as possible in recording ECG clinically. By constructing human heart profile model, based on the characteristic of electrocardic physiology, the authors put forward a new method of weighed epicardial potential to study the relationship between body surface potential and epicardial potential. Thus, the unknown body surface potential could be derived by the selected body surface potential. This method has been proved practical in diagnosing and locating myocardial ischemia and myocardial infarction with stardard 12-lead ECG.
基金This work was supported by Tsinghua University (Grant No. 0009).
文摘The Lyapunov exponents of synchronous 12-lead ECG signals have been investigated for the first time using a multi-sensor (electrode) technique. The results show that the Lyapunov exponents computed from different locations on the body surface are not the same, but have a distribution characteristic for the ECG signals recorded from coronary artery disease (CAD) patients with sinus rhythms and for signals from healthy older people. The maximum Lyapunov exponent L1 of all signals is positive. While all the others are negative, so the ECG signal has chaotic characteristics. With the same leads, L1 of CAD patients is less than that of healthy people, so the CAD patients and healthy people can be classified by L1, L1 therefore has potential values in the diagnosis of heart disease.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups(Grant Number RGP.2/246/44),B.B.,and https://www.kku.edu.sa/en.
文摘Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.
文摘针对家庭及社区的医疗监护系统需求,设计了一种基于物联网(Internet of Thing,IoT)的多导联心电信号(electrocardiogram,ECG)实时监测系统。本系统利用ADS1298前端实时采集ECG,通过蓝牙和Wi-Fi模块将信号分别发送给Android客户端和云服务器。在客户端中实现了ECG数据接收、数据解码、中值滤波、LMS自适应限波、QRS波检测以及ECG波形和诊断结果的显示;在云服务器中建立了数据库,用于保存用户的心电数据;还挂载了基于卷积神经网络的疾病诊断模型,可对用户心脏健康状态进行实时监测。
