Performance Analysis of T-Wave-Offset Detection Algorithms on Patients With Cardiac Diseases

Deborah Nairn*, Pietro Bonizzi, Joël Karel, Alfons Raul Aranda Hernandez

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


Cardiovascular diseases are the number one cause of death in the world. The application of automatic processing algorithms can provide important information about these heart diseases. However, the design of these algorithms can be challenging due to the morphological variations in ECG signals, specifically in the T-wave-offset. This study proposes a comparison of several T-offset detection algorithms on healthy subjects and patients suffering from cardiac diseases. Seven state of the art algorithms were selected for implementation and were evaluated using the same dataset and benchmark to provide a fair comparison. Although no algorithm performs with 100% accuracy for all patients, most can perform well with regards to the healthy patients, with two algorithms having a high performance, above 70% accuracy, on all patients.
Original languageEnglish
Title of host publicationComputing in Cardiology
Number of pages4
ISBN (Print)9781728109589
Publication statusPublished - 25 Sept 2018
Event45th Computing in Cardiology Conference (CinC) - NETHERLANDS
Duration: 23 Sept 201826 Sept 2018


Conference45th Computing in Cardiology Conference (CinC)


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