Robust Detection of Premature Ventricular Contractions Using a Wave-Based Bayesian Framework

Abstract

Detection and classification of ventricular complexes from the electrocardiogram (ECG) is of considerable importance in Holter and critical care patient monitoring, being essential for the timely diagnosis of dangerous heart conditions. Accurate detection of premature ventricular contractions (PVCs) is particularly important in relation to life-threatening arrhythmias. In this paper, we introduce a model-based dynamic algorithm for tracking the ECG characteristic waveforms using an extended Kalman filter. The algorithm can work on single or multiple leads. A 'polargram' -a polar representation of the signal -is introduced, which is constructed using the Bayesian estimations of the state variables. The polargram allows the specification of a polar envelope for normal rhythms. Moreover, we propose a novel measure of signal fidelity by monitoring the covariance matrix of the innovation signals throughout the filtering procedure. PVCs are detected by simultaneous tracking the signal fidelity and the polar envelope. Five databases, including 40 records from MIT-BIH arrhythmia database, are used for differentiating normal, PVC, and other beats. Performance evaluation results show that the proposed method has an average detection accuracy of 99.10%, aggregate sensitivity of 98.77%, and aggregate positive predictivity of 97.47%. Furthermore, the method is capable of 100% accuracy for records that contain only PVCs and normal sinus beats. The results illustrate that the method can contribute to, and enhance the performance of clinical PVC detection.

Real-time premature ventricular contractions detection based on Redundant Discrete Wavelet Transform

“…It is frequently originated from the right ventricular outflow tract, but some can be originated from the left ventricular outflow tract, from aortic sinus cusp, from tricuspid and mitral valve annulus, and from coronary venous system, among other heart structures (Ge et al, 2012;Jia et al, 2011;Sayadi et al, 2010;Zheng et al, 2016).…”

“…Hence, in the last years, several PVC detection system have been proposed for this issue: based on Artificial Neural Network (ANN) (Bortolan et al, 1991;Dalvi et al, 2016;Hu et al, 1997;Inan et al, 2006), Heuristic algorithm (Dotsinsky and Stoyanov, 2004), Bayesian framework (Sayadi et al, 2010), Support Vector Machine (SVM) (Shen et al, 2011), morphology ECG features (Chazal and Reilly, 2006;Chazal et al, 2004;Lek-uthai et al, 2014), Fuzzy Neural Network System (FNNS) (Lim, 2009), Wavelet Transform (Inan et al, 2006;Martis et al, 2013;Nazarahari et al, 2015;Orozco-Duque et al, 2013;Shyu et al, 2004;Yochum et al, 2016) and adaptive filter (Nieminaki et al, 1999;Solosenko et al, 2015). The main feature of most detection methods is a real-time analysis, however some methods have high mathematical complexity, which demands a high computational cost.…”

ECG fiducial points extraction by extended Kalman filtering

“…Sayadi et al also described a Gaussian wave-based state space model [9] whose each characteristic wave, i.e. P, QRS and T has been considered as a state.…”

“…Sayadi et al modified the EKF2 framework and added parameters of ECG dynamical model as states to EKF2 and introduced the "EKF17" approach [8]. They also described a Gaussian wave-based state space model whose each characteristic wave of ECG has been considered as a state ("EKF4") [9].…”

Fiducial points extraction and characteristicwaves detection in ECG signal using a model-based bayesian framework

“…Sayadi et al modified the EKF2 framework and added parameters of ECG dynamical model as states to EKF2 and introduced the "EKF17" approach [8,9]. They also described a Gaussian wave-based state space model whose each characteristic wave of ECG has been considered as a state ("EKF4") [10].…”