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Electrocardiogram (ECG) is an authoritative source to diagnose and countercritical cardiovascular syndromes such as arrhythmia and myocardial infarction(MI). Current machine learning techniques either depend on manually extractedfeatures or large and complex deep learning networks which merely utilize the1D ECG signal directly. Since intelligent multimodal fusion can perform at thestateof-the-art level with an efficient deep network, therefore, in this paper,we propose two computationally efficient multimodal fusion frameworks for ECGheart beat classification called Multimodal Image Fusion (MIF) and MultimodalFeature Fusion (MFF). At the input of these frameworks, we convert the raw ECGdata into three different images using Gramian Angular Field (GAF), RecurrencePlot (RP) and Markov Transition Field (MTF). In MIF, we first perform imagefusion by combining three imaging modalities to create a single image modalitywhich serves as input to the Convolutional Neural Network (CNN). In MFF, weextracted features from penultimate layer of CNNs and fused them to get uniqueand interdependent information necessary for better performance of classifier.These informational features are finally used to train a Support Vector Machine(SVM) classifier for ECG heart-beat classification. We demonstrate thesuperiority of the proposed fusion models by performing experiments onPhysioNets MIT-BIH dataset for five distinct conditions of arrhythmias whichare consistent with the AAMI EC57 protocols and on PTB diagnostics dataset forMyocardial Infarction (MI) classification. We achieved classification accuracyof 99.7% and 99.2% on arrhythmia and MI classification, respectively.

ECG Heartbeat Classification Using Multimodal Fusion