Smart wearable devices enable personalized at-home healthcare by unobtrusively
collecting patient health data and facilitating the development of intelligent platforms to …

Atrial fibrillation (AF) is a serious medical condition of the heart potentially leading to stroke,
which can be diagnosed by analyzing electrocardiograms (ECG). Technologies of Artificial …

Mobile health devices with automatic electrocardiogram diagnosis models facilitate long-
term cardiac monitoring and enhance the sensitivity of detecting paroxysmal cardiovascular …

The visual interpretation of electrocardiogram (ECG) data is driven by human pattern
recognition and requires in-depth medical knowledge. Although state-of-the-art deep …

Convolutional neural networks (CNNs) have been widely deployed in computer vision tasks.
However, the computation and resource intensive characteristics of CNN bring obstacles to …

Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal
arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to …

This paper presents an explainable, low-complexity binary electrocardiogram (ECG)
classifier to be deployed in a resource-limited wearable edge device. The presented …

In this article, we propose a multistage electrocardiogram (ECG) classifier for distributed
machine learning (ML) inferencing across the edge-cloud continuum for wearable systems …

Cardiac disease, also referred to as cardiovascular disease, is a collection of conditions that
affect the heart and blood vessels. Medical professionals typically use a combination of …

Electrocardiogram (ECG) monitoring is a critical and intricate task in cardiac healthcare.
While large models supported by the remote cloud servers with abundant computational …