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

Wearable intelligent health monitoring devices with on-device biomedical AI processor can
be used to detect the abnormity in users' biomedical signals (eg, ECG arrythmia …

This paper presents an electrocardiogram (ECG) signal classification model based on
Recurrent Convolutional Neural Network (RCNN). With recurrent connections and data …

Electrocardiogram (ECG) processors for healthcare have been widely used, however most
of them can only adapt to specific applications, lacking flexibility. For achieving scalable on …

Many electrocardiogram (ECG) processors have been widely used for cardiac monitoring.
However, most of them have relatively low energy efficiency, and lack configurability in …

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP)
consumption is proposed for continuous cardiac monitoring applications. The detector is …

Ventricular arrhythmias (VA) are the leading cause of sudden cardiac death. VAs,
particularly ventricular tachycardia (VT) and ventricular fibrillation (VF), are potentially life …

This paper presents a pulse-arrival-time (PAT) estimation scheme using Extreme Gradient
Boosting (XGBoost) regression and its implementation with hardware description language …

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage,
low-power cardiac arrhythmia classifiers owing to their high weight compression rate …

Ventricular arrhythmia (VA) is the most critical cardiac anomaly among all arrhythmia beats.
Thus, it becomes imperative to predict the occurrence of VA to avoid sudden casualties …