A Real-Time Remote Monitoring System for Cardiovascular Diseases

Currently, real-time recording and bio-signal-based early diagnosis are feasible solutions thanks to increasing progress in monitoring device development technology, including self-monitoring devices, integrated electronic systems, the Internet of Things, and edge computing. The pandemic emergency of coronavirus disease 2019 (COVID-19) activated the remote monitoring era and highlighted the need for innovative digital approaches to managing cardiovascular disease. The scientific community and health organizations have considered this new era confirming that remote consultation and monitoring systems have become indispensable in cardiovascular healthcare circumstances to enhance patient healthcare and offer personalized treatment. The paper aims to introduce a real-time remote monitoring system for cardiovascular diseases and to describe the proposed system modules and the ECG signal processing algorithms. The described approach can monitor the patient's cardiac activity, allowing the specialist to control the electronic instruments remotely without leaving their office. Therefore, this system aims at all cardiopathic patients with objective motor difficulties either because they are bedridden or geographically located in places distant from the health facility of interest. Furthermore, considering the real-time monitoring approach of this system, a future application scenario in a global pandemic context can be hypothesized. © 2022 Seventh Sense Research Group®

Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset.

The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists of P, QRS, and T waves. Information provided from the signal based on the intervals and amplitudes of these waves is associated with various heart diseases. The first step in isolating the features of an ECG begins with the accurate detection of the R-peaks in the QRS complex. The database was based on the PTB-XL database, and the signals from Lead I-XII were analyzed. This research focuses on determining the Few-Shot Learning (FSL) applicability for ECG signal proximity-based classification. The study was conducted by training Deep Convolutional Neural Networks to recognize 2, 5, and 20 different heart disease classes. The results of the FSL network were compared with the evaluation score of the neural network performing softmax-based classification. The neural network proposed for this task interprets a set of QRS complexes extracted from ECG signals. The FSL network proved to have higher accuracy in classifying healthy/sick patients ranging from 93.2% to 89.2% than the softmax-based classification network, which achieved 90.5-89.2% accuracy. The proposed network also achieved better results in classifying five different disease classes than softmax-based counterparts with an accuracy of 80.2-77.9% as opposed to 77.1% to 75.1%. In addition, the method of R-peaks labeling and QRS complexes extraction has been implemented. This procedure converts a 12-lead signal into a set of R waves by using the detection algorithms and the k-mean algorithm.