3-Data Mining for Biomedical Applications

Annually, we participate a to PhysioNet's challenges (see our publications on Google Scholar):

Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021

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Classification of 12-lead ECGs: the PhysioNet - Computing in Cardiology Challenge 2020

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You Snooze, You Win: the PhysioNet/Computing in Cardiology Challenge 2018

My team and I are participating to this challenge 2018 (See more) and our proposed solution ranked 7th out of 624 entries submitted to the server of challenge.

AF Classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017

The 2017 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify, from a single short ECG lead recording (between 30 s and 60 s in length), whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.

Classification of Normal/Abnormal Heart Sound Recordings: the PhysioNet/Computing in Cardiology Challenge 2016

The 2016 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify heart sound recordings collected from a variety of clinical or nonclinical (such as in-home visits) environments. The aim is to identify, from a single short recording (10-60s) from a single precordial location, whether the subject of the recording should be referred on for an expert diagnosis.

Mining Fetal ECG Signals for R-Peak Detection

This project aims at proposing an efficient method for R-peak detection in noninvasive fetal electrocardiogram (ECG) signals which are acquired from multiple electrodes on mother's abdomen. See more