Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determi...Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is time-consuming.In addition,the diagnosis requires experienced medical experts and is error-prone.However,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures.This study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational complexity.ECG dataset containing averaged signals with window size 10 is used as an input.Several competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the results.Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy score.The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.展开更多
Heart arrhythmia is a group of irregular heartbeat conditions and is usually detected by electrocardiograms(ECG)signals.Over the past years,deep learning methods have been developed to classify different types of hear...Heart arrhythmia is a group of irregular heartbeat conditions and is usually detected by electrocardiograms(ECG)signals.Over the past years,deep learning methods have been developed to classify different types of heart arrhythmias through ECG based on computer-aided diagnosis systems(CADs),but these deep learning methods usually cannot trade-off between classification performance and parameters of deep learning methods.To tackle this problem,this work proposes a convolutional neural network(CNN)model named PDNet to recognize different types of heart arrhythmias efficiently.In the PDNet,a convolutional block named PDblock is devised,which is comprised of a pointwise convolutional layer and a depthwise convolutional layer.Furthermore,an improved loss function is utilized to improve the results of heart arrhythmias classification.To verify the proposed CNN model,extensive experiments are conducted on publicMIT-BIH ECG databases.The experimental results demonstrate that the proposed PDNet achieves an accuracy of 98.2%accuracy and outperforms state-of-the-art methods about 2%.展开更多
文摘Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is time-consuming.In addition,the diagnosis requires experienced medical experts and is error-prone.However,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures.This study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational complexity.ECG dataset containing averaged signals with window size 10 is used as an input.Several competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the results.Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy score.The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.
文摘Heart arrhythmia is a group of irregular heartbeat conditions and is usually detected by electrocardiograms(ECG)signals.Over the past years,deep learning methods have been developed to classify different types of heart arrhythmias through ECG based on computer-aided diagnosis systems(CADs),but these deep learning methods usually cannot trade-off between classification performance and parameters of deep learning methods.To tackle this problem,this work proposes a convolutional neural network(CNN)model named PDNet to recognize different types of heart arrhythmias efficiently.In the PDNet,a convolutional block named PDblock is devised,which is comprised of a pointwise convolutional layer and a depthwise convolutional layer.Furthermore,an improved loss function is utilized to improve the results of heart arrhythmias classification.To verify the proposed CNN model,extensive experiments are conducted on publicMIT-BIH ECG databases.The experimental results demonstrate that the proposed PDNet achieves an accuracy of 98.2%accuracy and outperforms state-of-the-art methods about 2%.
