Automated Detection of COVID-19 Using Deep Learning Approaches with Paper-Based ECG Reports

Abstract

One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data.

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

“…Moreover, recent advancements in biometricbased human identification show great promise for accurate recognition based on ECG data [20,21]. In addition, ECG analysis can also be utilized for detecting emotions and stress [22], pain [23], sleep-apnea [24,25], identification of COVID-19 infections [26][27][28][29], assessment of signal quality [30,31], and many other potential applications. In this study, we considered all applications as long as they investigated on DA of ECG via AI techniques.…”

1D CNN model for ECG diagnosis based on several classifiers

“…To use the CNN model that relies on the convolutional layers for 1D signals, the convolutional layers must be redesigned to match the input. The proposed CNN model consists of an input layer, 3 convolutional layers, 3 ReLU layers, 3 batch normalization layers, 3 max pooling layers, and ending with 3 fully connected layers [17]. The structure of the proposed 1D CNN is shown in Fig.…”