With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardi...With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.展开更多
Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine.Detecting arrhythmia from ECG signals is considered a standard approach and hence,automating this...Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine.Detecting arrhythmia from ECG signals is considered a standard approach and hence,automating this process would aid the diagnosis by providing fast,costefficient,and accurate solutions at scale.This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography(ECG)signals causing arrhythmia.In this era of applied intelligence,automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions.In this research,our contributions are two-fold.Firstly,the Dual-Tree Complex Wavelet Transform(DT-CWT)method is implied to overhaul shift-invariance and aids signal reconstruction to extract significant features.Next,A neural attention mechanism is implied to capture temporal patterns from the extracted features of the ECG signal to discriminate distinct classes of arrhythmia and is trained end-to-end with the finest parameters.To ensure that the model’s generalizability,a set of five traintest variants are implied.The proposed model attains the highest accuracy of 98.5%for classifying 8 variants of arrhythmia on the MIT-BIH dataset.To test the resilience of the model,the unseen(test)samples are increased by 5x and the deviation in accuracy score and MSE was 0.12%and 0.1%respectively.Further,to assess the diagnostic model performance,AUC-ROC curves are plotted.At every test level,the proposed model is capable of generalizing new samples and leverages the advantage to develop a real-world application.As a note,this research is the first attempt to provide neural attention in arrhythmia classification using MIT-BIH ECG signals data with state-of-the-art performance.展开更多
Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the m...Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the majority,it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments,and then take the left segments to the doctors or the computer programs for further diagnosis.In this paper,we propose a preliminary abnormal segment screening method for Holter data.Based on long short-term memory(LSTM)networks,the prediction model is established and trained with the normal data of a monitored object.Then,on the basis of kernel density estimation,we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data.Based on these,the preliminary abnormal ECG segment screening analysis is carried out without R wave detection.Experiments on the MIT-BIH arrhythmia database show that,under the condition of ensuring that no abnormal point is missed,53.89% of normal segments can be effectively obviated.This work can greatly reduce the workload of subsequent further processing.展开更多
根据统计心律失常是引起心源性猝死的最主要原因.对此提出了一个改进型的一维U型网络(one-dimensional Unet,1D-Unet)对分割后的心电信号进行分类识别.该网络基于MIT-BIH的心律失常数据库,采用了美国医疗仪器促进协会(Association for t...根据统计心律失常是引起心源性猝死的最主要原因.对此提出了一个改进型的一维U型网络(one-dimensional Unet,1D-Unet)对分割后的心电信号进行分类识别.该网络基于MIT-BIH的心律失常数据库,采用了美国医疗仪器促进协会(Association for the Advancement of Medical Instrumentation,AAMI)制定的分类标准,改进型的1D-UNet选取合适的卷积层和卷积核数目,在上采样与拼接的过程中尽量保存原始信号的特征.该网络在心电信号小波去噪的情况下,识别准确率达到99.46%,F1分数为97.57%.网络分类的平均准确率为99.73%,精确度98.23%,敏感度96.92%,特异度99.17%,无去噪处理的网络识别准确率为99.4%,F1分数为97.12%.此网络因为是全卷积层的网络,输出层并没有采用经典的全连接层,神经网络的参数大大减少.该网络对于临床上通过心电图诊断心律失常有很大辅助作用.展开更多
This paper proposes a different method to eliminate base wander and power line interference in electrocardiogram, which introduces the integer coefficient filter theory and gives the detail for designing digital filte...This paper proposes a different method to eliminate base wander and power line interference in electrocardiogram, which introduces the integer coefficient filter theory and gives the detail for designing digital filter to remove these two normal noise signals. Signal from the MIT-BIH electrocardiogram database was used to test the performance of the filter. From the test results, the performance of the digital filer is reDT good. The filter coefficient is an integer number, therefore, the filtering algorithm can be successfully implemented on the microprocessor.展开更多
基金supported by Faculty of Computing and Informatics,University Malaysia Sabah,Jalan UMS,Kota Kinabalu Sabah 88400,Malaysia.
文摘With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.
基金This research was partially supported by JNTU Hyderabad,India under Grant proceeding number:JNTUH/TEQIP-III/CRS/2019/CSE/08.The authors are grateful for the support provided by the TEQIP-III team.
文摘Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine.Detecting arrhythmia from ECG signals is considered a standard approach and hence,automating this process would aid the diagnosis by providing fast,costefficient,and accurate solutions at scale.This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography(ECG)signals causing arrhythmia.In this era of applied intelligence,automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions.In this research,our contributions are two-fold.Firstly,the Dual-Tree Complex Wavelet Transform(DT-CWT)method is implied to overhaul shift-invariance and aids signal reconstruction to extract significant features.Next,A neural attention mechanism is implied to capture temporal patterns from the extracted features of the ECG signal to discriminate distinct classes of arrhythmia and is trained end-to-end with the finest parameters.To ensure that the model’s generalizability,a set of five traintest variants are implied.The proposed model attains the highest accuracy of 98.5%for classifying 8 variants of arrhythmia on the MIT-BIH dataset.To test the resilience of the model,the unseen(test)samples are increased by 5x and the deviation in accuracy score and MSE was 0.12%and 0.1%respectively.Further,to assess the diagnostic model performance,AUC-ROC curves are plotted.At every test level,the proposed model is capable of generalizing new samples and leverages the advantage to develop a real-world application.As a note,this research is the first attempt to provide neural attention in arrhythmia classification using MIT-BIH ECG signals data with state-of-the-art performance.
文摘Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the majority,it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments,and then take the left segments to the doctors or the computer programs for further diagnosis.In this paper,we propose a preliminary abnormal segment screening method for Holter data.Based on long short-term memory(LSTM)networks,the prediction model is established and trained with the normal data of a monitored object.Then,on the basis of kernel density estimation,we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data.Based on these,the preliminary abnormal ECG segment screening analysis is carried out without R wave detection.Experiments on the MIT-BIH arrhythmia database show that,under the condition of ensuring that no abnormal point is missed,53.89% of normal segments can be effectively obviated.This work can greatly reduce the workload of subsequent further processing.
文摘根据统计心律失常是引起心源性猝死的最主要原因.对此提出了一个改进型的一维U型网络(one-dimensional Unet,1D-Unet)对分割后的心电信号进行分类识别.该网络基于MIT-BIH的心律失常数据库,采用了美国医疗仪器促进协会(Association for the Advancement of Medical Instrumentation,AAMI)制定的分类标准,改进型的1D-UNet选取合适的卷积层和卷积核数目,在上采样与拼接的过程中尽量保存原始信号的特征.该网络在心电信号小波去噪的情况下,识别准确率达到99.46%,F1分数为97.57%.网络分类的平均准确率为99.73%,精确度98.23%,敏感度96.92%,特异度99.17%,无去噪处理的网络识别准确率为99.4%,F1分数为97.12%.此网络因为是全卷积层的网络,输出层并没有采用经典的全连接层,神经网络的参数大大减少.该网络对于临床上通过心电图诊断心律失常有很大辅助作用.
文摘This paper proposes a different method to eliminate base wander and power line interference in electrocardiogram, which introduces the integer coefficient filter theory and gives the detail for designing digital filter to remove these two normal noise signals. Signal from the MIT-BIH electrocardiogram database was used to test the performance of the filter. From the test results, the performance of the digital filer is reDT good. The filter coefficient is an integer number, therefore, the filtering algorithm can be successfully implemented on the microprocessor.
