# Detection of Myocardial Infarction Based on Novel Deep Transfer Learning Methods for Urban Healthcare in Smart Cities

## TL;DR

This paper presents a novel deep transfer learning approach using CNNs, specifically modified VGG-Net models, for accurate myocardial infarction detection from ECG signals in smart city healthcare systems.

## Contribution

It introduces two new transfer learning-based CNN models, VGG-MI1 and VGG-MI2, optimized for MI detection with high accuracy, sensitivity, and specificity.

## Key findings

- Achieved over 99% accuracy in MI detection
- Enhanced performance through ECG data augmentation
- Validated effectiveness on PTB ECG database

## Abstract

. In this paper, an effective computer-aided diagnosis (CAD) system is presented to detect MI signals using the convolution neural network (CNN) for urban healthcare in smart cities. Two types of transfer learning techniques are employed to retrain the pre-trained VGG-Net (Fine-tuning and VGG-Net as fixed feature extractor) and obtained two new networks VGG-MI1 and VGG-MI2. In the VGG-MI1 model, the last layer of the VGG-Net model is replaced with a specific layer according to our requirements and various functions are optimized to reduce overfitting. In the VGG-MI2 model, one layer of the VGG-Net model is selected as a feature descriptor of the ECG images to describe it with informative features. Considering the limited availability of dataset, ECG data is augmented which has increased the classification performance. Physikalisch-technische bundesanstalt (PTB) Diagnostic ECG database is used for experimentation, which has been widely employed in MI detection studies. In case of using VGG-MI1, we achieved an accuracy, sensitivity, and specificity of 99.02%, 98.76%, and 99.17%, respectively and we achieved an accuracy of 99.22%, a sensitivity of 99.15%, and a specificity of 99.49% with VGG-MI2 model. Experimental results validate the efficiency of the proposed system in terms of accuracy sensitivity, and specificity.

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Source: https://tomesphere.com/paper/1906.09358