Detecting premature ventricular contractions in ECG signals with Gaussian processes

Melgani, Farid; Bazi, Yakoub

doi:10.1109/cic.2008.4749021

Cited by 12 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…recurrent NN [15], support vector machines (SVMs) [1,25], least square SVM [17], path forest [38] and Gaussian processes (GPs) [1,42]. Despite these great efforts, it has been shown recently [12,25,62] that automatic methods do not perform well if the recommendations of the Association for the Advancement of Medical Instrumentation 3 (AAMI) for class labeling and results presentation are closely followed as a possible solution of standardization.…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach for active classification of electrocardiogram signals

Rahhal

Bazi

Alhichri

et al. 2016

Information Sciences

536

276

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Deep learning approach for active classification of electrocardiogram signals

Rahhal

Bazi

Alhichri

et al. 2016

Information Sciences

536

276

View full text Add to dashboard Cite

“…These six records are the most difficult for PVC detection (sensitivity for records 105 and 215 is less than 5%, while others result in Sn ≈ 40% [19]). Hence, they are usually excluded from the database [21], [25]. To show the PVC detection capability of our proposed method, we have compared its performance to the file-by-file results of the NN-based approach in [19].…”

Section: Resultsmentioning

confidence: 99%

“…Second, since there are various choices for selecting the network structure to achieve an acceptable performance, finding the optimum architecture has not a unique solution [20]–[22]. The use of symbolic dynamics analysis [24] and Gaussian processes [25] for PVC detection has also been reported.…”

Section: Introductionmentioning

confidence: 99%

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

Sayadi

Shamsollahi

Clifford

2010

IEEE Trans. Biomed. Eng.

130

View full text Add to dashboard Cite

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.

show abstract

“…Introduction: Accurate detection of premature ventricular contraction (PVC) beats in electrocardiogram (ECG) signal is essential for predicting life-threatening ventricular arrhythmias [1][2][3]. Many methods were presented based on spline wavelet [1], Gaussian process classifiers (GPCs) and support vector machines (SVMs) [2], wave-based Bayesian framework [4], fuzzy neural network (FNN) [5], wavelet transform (WT) and timing interval (TI) features [6], Haar wavelet coefficients [7], Gaussian process and S-transform [8], WT and discrete cosine transform (DCT) [9], SVM and particle swarm optimisation (PSO) [10], principal component analysis (PCA) and feed-forward artificial neuron network (ANN) using the multi-dimensional PSO scheme [11], high-order statistics (HOS) and GPC [12], 26 features and Kth nearest-neighbours (KNN) rule [13], and the quadratic spline wavelet and FNN [14].…”

mentioning

confidence: 99%

Robust detection of premature ventricular contractions using sparse signal decomposition and temporal features

Manikandan

Ramkumar

Deshpande

et al. 2015

Healthcare Technology Letters

View full text Add to dashboard Cite

An automated noise-robust premature ventricular contraction (PVC) detection method is proposed based on the sparse signal decomposition, temporal features, and decision rules. In this Letter, the authors exploit sparse expansion of electrocardiogram (ECG) signals on mixed dictionaries for simultaneously enhancing the QRS complex and reducing the influence of tall P and T waves, baseline wanders, and muscle artefacts. They further investigate a set of ten generalised temporal features combined with decision-rule-based detection algorithm for discriminating PVC beats from non-PVC beats. The accuracy and robustness of the proposed method is evaluated using 47 ECG recordings from the MIT/BIH arrhythmia database. Evaluation results show that the proposed method achieves an average sensitivity of 89.69%, and specificity 99.63%. Results further show that the proposed decision-rule-based algorithm with ten generalised features can accurately detect different patterns of PVC beats (uniform and multiform, couplets, triplets, and ventricular tachycardia) in presence of other normal and abnormal heartbeats.

show abstract

Detecting premature ventricular contractions in ECG signals with Gaussian processes

Abstract: The aim of this work is twofold. First, we

Cited by 12 publications

References 6 publications

Deep learning approach for active classification of electrocardiogram signals

Deep learning approach for active classification of electrocardiogram signals

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

Robust detection of premature ventricular contractions using sparse signal decomposition and temporal features

Contact Info

Product

Resources

About