Abnormal ECG Beat Detection Based on Convolutional Neural Networks

2020 ◽  
Author(s):  
Mehmet Akif Ozdemir ◽  
Onan Guren ◽  
Ozlem Karabiber Cura ◽  
Aydin Akan ◽  
Aytug Onan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Beat Detection

scholarly journals Complex-Pan-Tompkins-Wavelets: Cross-channel ECG beat detection and delineation

2021 ◽  
Vol 66 ◽  
pp. 102450
Author(s):  
Thomas Thurner ◽  
Christoph Hintermueller ◽  
Hermann Blessberger ◽  
Clemens Steinwender
Keyword(s):  
Beat Detection

ECG beat detection using filter banks

1999 ◽  
Vol 46 (2) ◽  
pp. 192-202 ◽  
Author(s):  
V.X. Afonso ◽  
W.J. Tompkins ◽  
T.Q. Nguyen ◽  
Shen Luo
Keyword(s):  
Filter Banks ◽  
Beat Detection

scholarly journals Automatic Real-time Beat-to-beat Detection of Arrhythmia Conditions

2021 ◽  
Author(s):  
Giovanni Rosa ◽  
Gennaro Laudato ◽  
Angela Colavita ◽  
Simone Scalabrino ◽  
Rocco Oliveto
Keyword(s):  
Real Time ◽  
Beat Detection

scholarly journals Applications of Machine Learning in Ambulatory ECG

Hearts ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 472-494
Author(s):  
Joel Xue ◽  
Long Yu
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Wide Spectrum ◽  
Rapid Development ◽  
Ambulatory Ecg ◽  
Event Prediction ◽  
The People ◽  
Important Diagnostic Tool ◽  
Beat Detection ◽  
Applications Of Machine Learning

The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections. Over the years, there has been huge progress for algorithm development and implementation thanks to great efforts by researchers, engineers, and physicians, alongside the rapid development of electronics and signal processing, especially machine learning (ML). The current efforts and progress in machine learning fields are unprecedented, and many of these ML algorithms have also been successfully applied to AECG applications. This review covers some key AECG applications of ML algorithms. However, instead of doing a general review of ML algorithms, we are focusing on the central tasks of AECG and discussing what ML can bring to solve the key challenges AECG is facing. The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction. Each AECG device/system might have different portions and forms of those signal components depending on its application and the target, but these are the topics most relevant and of greatest concern to the people working in this area.

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