Cardiovascular diseases accounted for approximately 836,546 deaths in the United States in 2018. Nearly 2,300 Americans die of cardiovascular disease each day, an average of one death every 38 seconds. To this end, research has been reported in the literature on Electrocardiogram (ECG) signal analysis to determine arrhythmia and other cardiac conditions. This work introduces a classifier that will detect abnormalities of the ECG signal with its analysis as a 2-D image fed to a Convolutional Neural Network (CNN) classifier.The proposed method classifies the ECG signal as normal or ST-change, V-change by transforming the single-lead ECG signal into images and then applying CNN classification. Images are taken from the European ST-T dataset on PhysioNet databank. Our method yields an accuracy of 99.26%

Abuzaghleh, Omar

Abuzneid, Abdelshakour

Elleithy, Khaled M.

Faezipour, Miad

Wasimuddin, Muhammad

Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.https://doi.org/10.3390/electronics1002017

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Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

AbstractMulticlass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network ModelingMuhammad Wasimuddin, Khaled Elleithy, Abdelshakour Abuzneid, Miad Faezipour and Omar AbuzaghlehDepartment of Computer Science and EngineeringUniversity of Bridgeport, Bridgeport, CT Cardiovascular diseases accounted for approximately 836,546 deaths in the UnitedStates in 2018. Nearly 2,300 Americans die of cardiovascular disease each day, anaverage of one death every 38 seconds. To this end, research has been reported in theliterature on Electrocardiogram (ECG) signal analysis to determine arrhythmia andother cardiac conditions. This work introduces a classifier that will detectabnormalities of the ECG signal with its analysis as a 2-D image fed to aConvolutional Neural Network (CNN) classifier. The proposed method classifies theECG signal as normal or ST-change, V-change by transforming the single-lead ECGsignal into images and then applying CNN classification. Images are taken from theEuropean ST-T dataset on PhysioNet databank. Our method yields an accuracy of99.26%.[1] M. Wasimuddin, K. Elleithy, A. Abuzneid, M. Faezipour and O. Abuzaghleh, “Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling,” MDPI Electronics, 14 Jan 2021, 10, no. 2: 170.[2] M. Wasimuddin, K. Elleithy, A. Abuzneid, M. Faezipour and O. Abuzaghleh, “Stages-Based ECG Signal Analysis from Traditional Signal Processing to Machine Learning Approaches: A Survey,” IEEE Access, pp. 1-22 Sep 2020.[3] M. Wasimuddin, K. Elleithy, A. Abuzneid, M. Faezipour and O. Abuzaghleh, “ECG Signal Analysis Using 2-D Image Classification with Convolutional Neural Network,” IEEE International Conference on Computational Science and Computational Intelligence(CSCI), At Las Vegas, NV, 05-07 December 2019.Problem Definition and DatasetConclusion and Future WorkResults and EvaluationIdentifying acute myocardial infarctions early-on, and treating them promptly,increases the clinical outcomes significantly. In general, the analysis of the ECG-ST-segment, is one of the methods for identifying myocardial ischemia, involves ST-Segment Detection, ST Deviation measurement, and ST-Episode Detection. NeuralNetwork (NN) based machine learning can be used to detect such ST episodes butrequires feature extraction engineering. It is both tedious and also increases thecomplexity of the system. We propose a method to detect abnormalities of the heartsignal that can later be used in a real-time heart attack alert system. This method usesdeep learning techniques developed as a 2-D CNN-based image classifier that istrained on images of the ECG signal recorded by the single lead L3. This advancedmonitoring system will lead to the determination of arrhythmia and early onset ofmyocardial infarction (mainly heart attack), a disease that is gaining widespreadattention.• The European ST-T Database is used(www.PhysioNet.org). Each record of apatient has two ECG signals recorded,one from Lead 3 (L3) and the other fromLead 5 (L5) (Figure 1).• Our proposed transformed 2-D ECGimage is a screenshot of the ECG signal’swaveform in different ECG cycles.Methodology𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 =∑𝑇𝑇𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑇𝑇𝑉𝑉𝑉𝑉𝑚𝑚𝑉𝑉𝑉𝑉𝑉𝑉𝑚𝑚𝑉𝑉𝑉𝑉𝑚𝑚𝑚𝑇𝑇𝑉𝑉𝑚𝑚𝑉𝑉(2)The miniBatchSize in Equation 2is selected as 10, and the trainingset is 70% of the entire trainingset of images. TheBackpropagation algorithm usedin this work is ADAM. Figure 5shows the test results of ECGimages with labels. These aretwo randomly selected imagesfrom the testing dataset andtested against the trainednetwork by performing forwardpass to classify as normal orabnormal. Though both the ECGsignal images in Figure 5 lookvery similar; our method iscapable of classifying them asnormal and abnormal,successfully.Figure 5This proposed research used deep learning CNN as a computer vision tool to detectabnormality in the ECG signal and mainly the ST changes for myocardial infarction.The ECG signal recorded from a single lead was transformed into a 2-D imagerepresentation and then classified using CNN. It achieved better accuracy for theECG recorded with single lead rather than the 12-lead ECG signals reported in priorwork in the literature. This approach reduces the requirement of a multiple-leadsignal and can work on a single-lead ECG signal record. Our method provides99.26% accuracy, as observed in Figures 3. This proposed research can serve as anearly detection tool for cardiovascular diseases and can provide early warning alertsfor users who might be susceptible to heart attack.We will further improve the classifier to further detect different types ofabnormalities for both intra and inter-patient scheme. The application of this researchwill be implemented with the an app on a smart-phone with a two-wire attachmentand can instead be connected to the Smart Watch. ECG signal can be captured andthen analyzed via the proposed method and will be classified using the trainednetwork. Building the ECG framework application using Smart Watch as input datais our future development plan.Figure 2Figure 1Our proposed CNN model consists of 7 layers, including convolutional layer. Thearchitecture is illustrated in Figure 2. Furthermore, the data has been split into 70/30for training and testing. Table I summarizes the options and parameters used in eachlayer. In the model architecture, the input image size is 28×28, which is fed to theinput layer and then followed by the CNN layer with a kernel size of 5×5 and sixfeature maps. This is followed by the Relu activation function and then followed by aglobal average pooling layer, as illustrated in Table I. The output of the pooling layeris fed to the fully connected neural network. Cross entropy is the loss functiondeployed in our model. The output is a 3-class layer with labels of Normal, ST-changeand V-change. Table II depicts a comparison of the proposed work with prior related work inthe literature. Related work have not reported all the metrics. We have outperformed the workin the literature by achieving a higher accuracy of 99.26%. This is an improvement being donein this field.Figure 3We have conducted multiple simulation experiments and the best accuracy of 97.47%was obtained, as shown in Figure 3. The loss function can be observed in Figure 4,where the error has converged within fifty epochs. The initial learning rate is 0.001,and the input images are being shuffled after every epoch to improve the validation.The Validation Frequency is calculated from Equation 2.Figure 4• The ECG signal’s waveform for every two consecutive R peaks in a two-dimensional (2-D) image of size 28×28 pixels. In order to derive a 2-D image ofthe ECG signal between an R-R interval, a conversion from the 1-D signal to a2-D image is required. This is achieved by a Python code that plots the ECGsignal between the R-R interval and saves it as a 2-D image. The R peaks and R-R intervals themselves are detected by a derivative-based approach in real-time.Table ITable IIBesides complexity, SVM and other Neural Networks (NN) require features to beidentified prior to training, as these networks strictly depend upon these featureswhen optimizing weights during backpropagation. This is while CNN does notrequire features for training, but rather learns the features by itself with properparameters selection.Name Layer Type Size LearnablesImageinput Image input 28×28 -Layer1_ConvConvolution 5×5 Weights 5×5×3×4, Bias 1×1×4, Total (304)Layer1_ReluActivation Function- -Layer1_PoolingGlobal Average 1×1 -FullyConnectedNeural Network 1×1×3 Weights 3×4, Bias 3×1, Total (15)Softmax Activation Function- -Output LayerClassification Output- -ApproachClass, Year Detection Method Performance Metrics DatasetMachine LearningIschemic , 2002 ANN+PCA 90%sen, 90%speESCDBIschemic , 2004 MDA-based GA 91%sen, 91%speNormal, Ischemic , 2007DT+FuzzyModel 91.7%acc, 91.2%sen, 92.2%speQRS-Complex delineation , 2008DWT 90.75%sen, 89.2%ppvST-Segment changes, Multiclass , 2011 SVM[93.33%acc]ST, [96.35%acc]MulticlassNormal, Abnormal , 2014 ANN 98.73%accN, V, S, F, 2015 MSVM+CSVM [86%acc]MSVM, [94%acc]CSVMST-Segment and T-Wave anomalies , 2016DT andRUSBoost 86%sen, 94.85%ppv, 77%acc, 0.6f1Control, AF, VF, ST , 2018 CNN97.23%acc, 97.02%sen, 97.76%ppv,97.35%f1Normal, ST-Changes , 2018 RF86.9%acc, 85.18%sen[ST-Normal],87.35%sen[ST-depressed],88.06%sen[ST-elevated]S, V , 2019 ANN+MMNNS98.8%acc, 91%sen, 99.3%spe, 90%ppvNormal, ST-Change, V-Change, 2021Proposed (2-D CNN) 99.26%acc, [100%sen,100%spe]N,[97.8%sen,100%spe]ST,[100%sen,97.8%spe]V

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Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

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