UC Berkeley researchers trained AI to detect hidden warning signs of sudden cardiac death in routine ECG tests, according to ...

aDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA bResearch Laboratory for Electronics, Massachusetts Institute of Technology ...

In previous work, we introduced an ‘invisible’ ECG system with electrodes integrated into a toilet seat, capturing signals from the thighs. Here, we present the tOLIet dataset with single-lead thigh ...

This repository includes the code of the ECG-DualNet for ECG classification proposed in the paper Exploring Novel Algorithms for Atrial Fibrillation Detection by Driving Graduate Level Education in ...

A few weeks ago, I asked what your go-to signal processing language is. Python & MATLAB clearly won the race, with Python taking the slight lead. You’ve seen the ...

A simple model with good features beats a complex model with poor features. Medical time series (MedTS), such as EEG and ECG, are critical for clinical diagnosis but face two main challenges: generic ...

Electrocardiogram (ECG) is a common non-invasive diagnostic tool for cardiovascular diseases. Adequate data is crucial in utilizing deep learning to achieve intelligent diagnosis of ECG. The existing ...

When we think of Convolutional Neural Networks (CNNs), we often associate them with image processing. However, CNNs are not limited to images—they can also be used for sequential data, such as time ...

Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to ...

More than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study ...

Studies have reported the use of photoplethysmography signals to detect atrial fibrillation; however, the use of photoplethysmography signals in classifying multiclass arrhythmias has rarely been ...