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Detection and delineation of P and T waves in 12‐lead electrocardiograms
Author(s) -
Mehta Sarabjeet,
Lingayat Nitin,
Sanghvi Sanjeev
Publication year - 2009
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2008.00486.x
Subject(s) - qrs complex , computer science , support vector machine , artificial intelligence , pattern recognition (psychology) , lead (geology) , interference (communication) , standard deviation , electrocardiography , telecommunications , mathematics , statistics , medicine , cardiology , geology , channel (broadcasting) , geomorphology
This paper presents an efficient method for the detection and delineation of P and T waves in 12‐lead electrocardiograms (ECGs) using a support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander. An SVM is used as a classifier for the detection and delineation of P and T waves. The performance of the algorithm is validated using original simultaneously recorded 12‐lead ECG recordings from the standard CSE (Common Standards for Quantitative Electrocardiography) ECG multi‐lead measurement library. A significant detection rate of 95.43% is achieved for P wave detection and 96.89% for T wave detection. Delineation performance of the algorithm is validated by calculating the mean and standard deviation of the differences between automatic and manual annotations by the referee cardiologists. The proposed method not only detects all kinds of morphologies of QRS complexes, P and T waves but also delineates them accurately. The onsets and offsets of the detected P and T waves are found to be within the tolerance limits given in the CSE library.