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

Detection and classification of ventricular complexes from the 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.

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title: Abnormal Heart Rate Turbulence Predicts the Initiation of Ventricular Arrhythmias
abstract: Abnormal heart rate turbulence (HRT) reflects autonomic derangements predicting all‐cause mortality, yet has not been shown to predict ventricular arrhythmias in  patients. We hypothesized that HRT at programmed ventricular stimulation (PVS) would predict arrhythmia initiation in patients with left ventricular dysfunction.

title: QRS morphology representation and noise estimation using the Karhunen-Loeve transform
abstract: The authors describe robust methods for deriving Karhunen-Loeve (KL) basis functions, which can be used to represent the QRS complex. Using a five-term KL expansion of a 200-ms interval, which includes the QRS complex and part of the ST segment, one can represent morphology on two simultaneous ECG (electrocardiographic) leads with sufficient fidelity for beat classification. The residual error of the representation is an ideal estimate of the instantaneous noise content of the signal and permits identification of events for which the morphologic information is unreliable. The authors have compared the performance of the current KL-based arrhythmia analysis program with its predecessor (which uses a set of time-domain features for morphology representation but is otherwise identical to the newer program). In evaluations using the MIT-BIH and AHA (American Heart Association) databases, and a newly developed database containing approximately 2.5 million annotated beats (including over 80000 premature ventricular contractions) from 27 long-term ECG recordings, it was found that beat classification errors using the KL transform were as little as one-fourth of those for the older program.<<ETX>>

title: Classification of electrocardiogram using hidden Markov models
abstract: The objective of this project is to develop models for the characterization of electrocardiogram (EGG). A fast and reliable QRS detection algorithm based on a one-pole filter has been developed. Automatic ECG classification using hidden Markov models (HMMs) is investigated. Models representing various types of beat are trained using the American Heart Association (AHA) ventricular arrhythmia ECG data. The types of beat being selected in the study are: normal (N), premature ventricular contraction (V), and fusion of ventricular and normal beats (F). Artificial ECG generated from the model shows that each model truly characterizes that particular type of beat. In the testing phase, ECG signals are classified using the trained models. The average classification accuracy is 93% for N beat, 65.55% for V beat, and 56.38% for F beat respectively.

title: ECG signal analysis through hidden Markov models
abstract: This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application

title: Beat detection and classification of ECG using self organizing maps
abstract: A procedure for beat detection and classification was developed using ECG recordings. This procedure can be used for beat detection with one or two leads, and then a portion of each detected beat is used for classifying. This task is performed by a neural network. In the authors' work the morphology of the QRS portion of the ECG feeds a self organizing map (SOM). The SOM was previously trained with different QRS complexes such as normal and ectopic beat morphologies. The beat classification is very important in heart rate variability (HRV) analysis, because one must use only the normal beats and reject the ectopic ones for the construction of the RR intervals beat series.

title: Comparing wavelet transforms for recognizing cardiac patterns
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title: ECG beat detection using filter banks
abstract: The authors have designed a multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters.

title: The comparison of different feed forward neural network architectures for ECG signal diagnosis.
abstract: The electrocardiograms (ECGs) record the electrical activity of the heart and are used to diagnose many heart disorders. This paper proposes a two-stage feed forward neural network for ECG signal classification. The research is aimed at the design of an intelligent ECG diagnosis tool that can recognise heart abnormalities while reducing the complexity, cost, and response time of the system. A number of neural network architectures are designed and compared for their ability to classify six different heart conditions. Two network architectures based on one stage and two stage feed forward neural networks are chosen for this investigation. The training and testing ECG signals are obtained from MIT-BIH database. The network inputs are comprised of 12 ECG features and 13 compressed components of each heart beat signal. The performance of the different modules as well as the efficiency of the whole system is presented. Among different architectures, a proposed multi-stage network named NET_BST possesses the highest recognition rate of around 93%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems.

title: Classification of cardiac arrhythmias using fuzzy ARTMAP
abstract: The authors have investigated the QRS complex, extracted from electrocardiogram (EGG) data, using fuzzy adaptive resonance theory mapping (ARTMAP) to classify cardiac arrhythmias. Two different conditions have been analyzed: normal and abnormal premature ventricular contraction (PVC). Based on MIT/BIH database annotations, cardiac beats for normal and abnormal QRS complexes were extracted from this database, scaled, and Hamming windowed, after bandpass filtering, to yield a sequence of 100 samples for each QRS segment. From each of these sequences, two linear predictive coding (LPC) coefficients were generated using Burg's maximum entropy method. The two LPC coefficients, along with the mean-square value of the QRS complex segment, were utilized as features for each condition to train and test a fuzzy ARTMAP neural network for classification of normal and abnormal PVC conditions. The test results show that the fuzzy ARTMAP neural network can classify cardiac arrhythmias with greater than 99% specificity and 97% sensitivity.

title: Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks.
abstract: Detection and classification of ventricular complexes from a limited number of ECG leads is of considerable importance in critical care or operating room patient monitoring. Beat-to-beat detection allows the heart rhythm evolution to be followed and various types of arrhythmia to be recognized. A quantitative analysis is proposed of pattern recognition parameters for classification of normal QRS complexes and premature ventricular contractions (PVC). Twenty-six parameters have been defined: the width of the QRS complex, three vectorcardiogram parameters and 11 from two ECG leads. These parameters include: amplitudes of positive and negative peaks, area of positive and negative waves, various time-interval durations, amplitude and angle of the QRS vector, etc. They are measured for all QRS complexes annotated as 'normals' and 'PVCs' from the 48 ECG recordings of the MIT-BIH arrhythmia database. Neural networks (NN) are shown to be a useful instrument for the analysis of large quantities of parameters. Separate ranking of any parameter and homogeneous group ranking (amplitude, area, interval, slope and vector) were performed. From the two ECG leads, the first three ranked parameter groups for clustering of PVCs are amplitude, slope and interval, while for N clustering they are vector, amplitude and area. Considering the entire parameter set, we obtained N = 99.7% correct detection of normal QRS complexes and PVC = 98.5% of premature ventricular complexes. The study also shows that simultaneous analysis of two ECG channels yields better accuracy compared to using a single channel: the improvement is 0.1% in the classification of N beats and 4.5% for PVC beats.

title: Comparison of four methods for premature ventricular contraction and normal beat clustering
abstract: The learning capacity and the classification ability for normal beats and premature ventricular contractions clustering by four classification methods were compared: neural networks (NN), K-th nearest neighbour rule (Knn), discriminant analysis (DA) and fuzzy logic (FL). Twenty-six morphology feature parameters, which include information of amplitude, area, specific interval durations and measurement of the QRS vector in a VCG plane, were defined. One global and two local learning sets were used. The local classifiers achieved better accuracies because of their good adaptability to the patients, while the capacity of the global classifier to process new records without additional learning was expectedly balanced by lower accuracies. NN assure the best results (high and balanced indices for specificity and sensitivity) using one of the local learning set, while the Knn provides the best results with the other local learning set. Using the global learning set DA and the FL methods perform better than the NN and Knn

title: Premature ventricular contraction classification by the Kth nearest-neighbours rule.
abstract: An analysis of electrocardiographic pattern recognition parameters for premature ventricular contraction (PVC) and normal (N) beat classification is presented. Twenty-six parameters were defined: 11 x 2 for the two electrocardiogram (ECG) leads, width of the complex and three parameters derived from a single-plane vectorcardiogram (VCG). Some of the parameters include amplitudes of maximal positive and maximal negative peaks, area of absolute values, area of positive values, area of negative values, number of samples with 70% higher amplitude than that of the highest peak, amplitude and angle of the QRS vector in a VCG plane. They were measured for all heartbeats annotated as N or PVC in all 48 ECG recordings of the MIT-BIH arrhythmia database. Two reference sets for the Kth nearest-neighbours rule were used-global and local. The classification indices obtained with the global reference set were 75.4% specificity and 80.9% sensitivity. Using the local reference set we increased the specificity to 96.7% and the sensitivity to 96.9%. The achieved specificity and sensitivity are comparable with, and greater than, the results reported in the literature.

title: Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features
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title: Automated patient-specific classification of premature ventricular contractions
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title: Detecting premature ventricular contractions in ECG signals with Gaussian processes
abstract: The aim of this work is twofold. First, we propose to investigate the capabilities of a new Bayesian approach for detecting premature ventricular contractions (PVCs), namely the Gaussian process (GP) approach. Second, we report an experimental comparison of different kinds of ECG signal representations, which are the standard temporal signal morphology, the discrete wavelet transform domain, the S-transform characteristics and the high-order statistics. In general, the obtained classification results show that the GP detector can compete seriously with state-of-the-art methods since it allows to yield better overall accuracy as well as better sensitivity. In addition, among the different kinds of features explored, those based on high-order statistics appear to be the best compromise between accuracy and computational time for PVC detection.

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title: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
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title: Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG
abstract: A novel method for detecting ventricular premature contraction (VPC) from the Holter system is proposed using wavelet transform (WT) and fuzzy neural network (FNN). The basic ideal and major advantage of this method is to reuse information that is used during QRS detection, a necessary step for most ECG classification algorithm, for VPC detection. To reduce the influence of different artifacts, the filter bank property of quadratic spline WT is explored. The QRS duration in scale three and the area under the QRS complex in scale four are selected as the characteristic features. It is found that the R wave amplitude has a marked influence on the computation of proposed characteristic features. Thus, it is necessary to normalize these features. This normalization process can reduce the effect of alternating R wave amplitude and achieve reliable VPC detection. After normalization and excluding the left bundle branch block beats, the accuracies for VPC classification using FNN is 99.79%. Features that are extracted using quadratic spline wavelet were used successfully by previous investigators for QRS detection. In this study, using the same wavelet, it is demonstrated that the proposed feature extraction method from different WT scales can effectively eliminate the influence of high and low-frequency noise and achieve reliable VPC classification. The two primary advantages of using same wavelet for QRS detection and VPC classification are less computation and less complexity during actual implementation.