Search

Cardiac disorders are a leading cause of global casualties, emphasizing the need for the initial diagnosis and prevention of cardiovascular diseases (CVDs). Electrocardiogram (ECG) procedures are highly recommended as they provide crucial cardiology information. Telemedicine offers an opportunity to provide low-cost tools and widespread availability for CVD management. In this research, we proposed an IoT-based monitoring and detection system for cardiac patients, employing a two-stage approach. In the initial stage, we used a routing protocol that combines routing by energy and link quality (REL) with dynamic source routing (DSR) to efficiently collect data on an IoT healthcare platform. The second stage involves the classification of ECG images using hybrid-based deep features. Our classification system utilizes the "ECG Images dataset of Cardiac Patients", comprising 12-lead ECG images with four distinct categories: abnormal heartbeat, myocardial infarction (MI), previous history of MI, and normal ECG. For feature extraction, we employed a lightweight CNN, which automatically extracts relevant ECG features. These features were further optimized through an attention module, which is the method's main focus. The model achieved a remarkable accuracy of 98.39%. Our findings suggest that this system can effectively aid in the identification of cardiac disorders. The proposed approach combines IoT, deep learning, and efficient routing protocols, showcasing its potential for improving CVD diagnosis and management. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have gained popularity for categorizing hyperspectral (HS) images due to their ability to capture representations of spatial-spectral features. However, their ability to model relationships between data is limited. Graph convolutional networks (GCNs) have been introduced as an alternative, as they are effective in representing and analyzing irregular data beyond grid sampling constraints. While GCNs have traditionally been computationally intensive, minibatch GCNs (miniGCNs) enable minibatch training of large-scale GCNs. We have improved the classification performance by using miniGCNs to infer out-of-sample data without retraining the network. In addition, fuzing the capabilities of CNNs and GCNs, through concatenative fusion has been shown to improve performance compared to using CNNs or GCNs individually. Finally, support vector machine (SVM) is employed instead of softmax in the classification stage. These techniques were tested on two HS datasets and achieved an average accuracy of 92.80 using Indian Pines dataset, demonstrating the effectiveness of miniGCNs and fusion strategies. [ABSTRACT FROM AUTHOR]