Multi-lead QRS detection using window pairs.
We designed a novel approach for multi-lead QRS detection. The algorithm uses one equation with two different window widths to generate a feature signal and a detection threshold. This enables it to adapt to various changes in QRS morphology and noise levels, resulting in a detection error rate of just 0.29% on the MIT-BIH Arrhythmia Database. The algorithm is also computationally efficient and capable of resolving differences between multiple leads by automatically attaching a confidence value to each QRS detection.
Queen’s University, Kingston, Ontario, Canada.
Sami
S
Torbey
Selim G
SG
Akl
Damian P
DP
Redfearn
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D012815:Signal Processing, Computer-Assisted