As healthcare systems around the world face challenges such as escalating costs, limited
access, and growing demand for personalized care, artificial intelligence (AI) is emerging as …

Electrocardiogram (ECG) is a widely used technique to diagnose cardiovascular diseases. It
is a non-invasive technique that represents the cyclic contraction and relaxation of heart …

Cardiac arrhythmias, characterized by deviations from the normal rhythmic contractions of
the heart, pose a formidable diagnostic challenge. Early and accurate detection remains an …

Abstract The appearance of Artificial Intelligence (IA) has improved our ability to process
large amount of data. These tools are particularly interesting in medical contexts, in order to …

We present an integrated approach by combining analog computing and deep learning for
electrocardiogram (ECG) arrythmia classification. We propose EKGNet, a hardware-efficient …

In this work, a novel meta-heuristic-based feature ranking and classification approach is
developed on the real-time ECG data. Initially, the data is captured using AD8232 …

In the realm of modern cardiology, the integration of computer-interpreted
electrocardiograms (CI-ECGs) has marked the beginning of a new era of diagnostic …

Wearable devices are often used to diagnose arrhythmia, but the electrocardiogram (ECG)
monitoring process generates a large amount of data, which will affect the detection speed …

In this study, the ECG Sensor was monitored using IoT based on LoRa 915 MHz to monitor a
Patient's, Heart Beat Status. Electrocardiogram or ECG is essential to determine normal or …

The scarcity of annotated data is a common issue in the realm of heartbeat classification
based on deep learning. Transfer learning (TL) has emerged as an effective strategy for …