P- and T-wave delineation in ECG signals using parametric mixture Gaussian and dynamic programming

Abstract

Detection and tracking of the P-and T-waves are important issues in the analysis and interpretation of the ECG signals. This paper addresses the problem by using two mixture Gaussian function and the Dynamic programming. A key feature of the proposed algorithm is that it allows to incorporate the prior knowledge about the P/T wave location variations and robustness to errors in QRS detection. The proposed algorithm is evaluated on the annotated QT-database and compared against the algorithms based on differential evolution optimization strategy (DEOS) and generating blocks of interest (GBI). The experiments show that the proposed method determines the P-and T-peak locations with a root mean square error of 0.085 s and 0.091 s respectively. Both these values are better than the corresponding values from DEOS and GBI. Similarly, the proposed algorithm achieves a sensitivity of 96.13% and predictivity of 97.70%. While the predictivity is higher than both DEOS and GBI, the sensitivity is on par with GBOI and higher than that of DEOS.

An enhanced T-wave delineation method using phasor transform in the electrocardiogram

“…T-wave peak detection is a crucial step before the detection of the T off . This method has achieved the Se %=99.48 and +P %=99.54, which is better than the methods proposed in [20,22,33], while comparable with methods [18,30]. The T off point locations were detected with reference to the T-peak location, but T off point was considered as per the AAMI-ECAR guidelines.…”

“…T-wave is generally detected with respect to the location of QRS complex in one cardiac cycle. There are two procedures to detect the T-wave peak, first T-wave is detected in the presence of QRS complex [18,19]. In second procedure, T-wave is detected after removing QRS complex in the ECG [20,21].…”

Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts

“…For a more thorough comparison, proposed algorithm as well as various well-known methods 15,18,24,26 were tested on the manually annotated part of the QTDB. The methods 15 and 24 are based on dyadic WT (wavelet quadratic spline) and PT, respectively.…”

“…The method 18 uses nonlinear dynamic model and an extended Kalman filter for estimating the parameters of the model. Authors of 26 solve the problem of P wave detection by using two mixture Gaussian function and the dynamic programming.…”

“…This area is demarcated with respect to RR interval and QRS complex fiducial points (onset and offset). Once the area has been defined, the maximum of P wave is generally detected directly, e.g., by adaptive threshold estimation 12,13 , using wavelet transform (WT) 14–16 , through extraction of P wave template and the application of correlation 17 , Kalman filtering 18 , moving average 13 , support vector machine 19,20 , Prony’s method 21 , the hidden Markov models 22 , a neural network 23 , phasor transform (PT) 24,25 , dynamic programing 26 , or combination of several detection algorithms 27 . These approaches deliver good results only in the cases of ECGs with normal cardiac rhythm, but these approaches were not tested on pathological records.…”