12-lead Arrhythmia Database Research Articles

Arrhythmia detection using multi-lead ECG spectra and Complex Support Vector Machine Classifiers

Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This work investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. Four beat types, Normal (N), Premature Ventricular Contraction (PVC), Atrial Premature Contraction (APC) and Right Bundle Branch Block Beat (RBBB) are simultaneously presented to a Complex Support Vector Machine (CSVM) classifier. The ECG signals are obtained from the St Petersburg INCART 12-lead Arrhythmia Database (INCARTDB). The detection of ECG Wave (P, QRS, T) is performed with the Wave Form Database (WFDB) Software Package which is used to read the annotation files and find the R (peak) location. For feature extraction, the Discrete Fourier Transform (DFT) is used. Fifty Fourier coefficients were selected for reconstructing individual ECG beats. This ensures moderate dimensionality reduction and de-noising of the input vector to the classifier. ECG beats classification is performed using CSVM with several training and test datasets. Sequential Minimal Optimization (SMO) is used to train the CSVM and compute the hyperplane parameters associated with both the real and complex hyperplanes. Cross validation is used for finding the best parameter values of the SVM and the two Gaussian RBK kernel functions. The aim of the study is to establish the advantage of CSVM over standard SVM in simultaneously detecting different types of arrhythmias on the basis of multi-lead recordings following signal compression in the Fourier domain. Implementation of the algorithms was performed in MATLAB. The CSVM classification algorithm provided better performance than the standard SVM classifier. The classification accuracy of the proposed scheme is 98.25% using CSVM. Future work will concentrate on the further development of ECG signal pre-processing using adaptive wavelet algorithms as well as classification with Clifford SVMs.

Interpretable morphological features for efficient single-lead automatic ventricular ectopy detection

ObjectiveWe designed an automatic, computationally efficient, and interpretable algorithm for detecting ventricular ectopic beats in long-term, single-lead electrocardiogram recordings. MethodsWe built five simple, interpretable, and computationally efficient features from each cardiac cycle, including a novel morphological feature which described the distance to the median beat in the recording. After an unsupervised subject-specific normalization procedure, we trained an ensemble binary classifier using the AdaBoost algorithm ResultsAfter our classifier was trained on subset DS1 of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database, our classifier obtained an F1 score of 94.35% on subset DS2 of the same database. The same classifier achieved F1 scores of 92.06% on the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead Arrhythmia database and 91.40% on the MIT-BIH Long-term database. A phenotype-specific analysis of model performance was afforded by the annotations included in the St. Petersburg INCART Arrhythmia database ConclusionThe five features this novel algorithm employed allowed our ventricular ectopy detector to obtain high precision on previously unseen subjects and databases SignificanceOur ventricular ectopy detector will be used to study the relationship between premature ventricular contractions and adverse patient outcomes such as congestive heart failure and death.

Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal

This work proposes a novel method for the detection of Left Ventricular Hypertrophy (LVH) from a multi-lead ECG signal. Left Ventricle walls become thick due to prolonged hypertension which may fail to pump heart effectively. The imaging techniques can be used as an alternative diagnose LVH; however, they are more expensive and time-consuming than proposed LVH. To overcome this issue, an algorithm to the diagnosis of LVH using ECG signal based on machine learning techniques were designed. In LVH detection, the pathological attributes such as R wave, S wave, inversion of QRS complex, changes in ST segment noticed in the ECG signal. This clinical information extracted as a feature by applying continuous wavelet transform. The signals were reconstructed with the frequency between 10 and 50 Hz from the wavelet. This followed by the detection of R wave and S wave peaks to obtain the relevant LVH diagnostic features. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Ensemble of Bagged Tree, AdaBoost classifiers were employed and the results are compared with four neural network classifiers including Multilayer Perceptron (MLP), Scaled Conjugate Gradient Backpropagation Neural Network (SCG NN), Levenberg–Marquardt Neural Network (LMNN) and Resilient Backpropagation Neural network (RPROP). The data source includes Left Ventricular Hypertrophy and healthy ECG signal from PTB diagnostic ECG database and St Petersburg INCART 12-Lead Arrhythmia Database. The results revealed that the proposed work can diagnose LVH successfully using neural network classifiers. The accuracy in detecting LVH is 86.6%, 84.4%, 93.3%,75.6%, 95.6%, 97.8%, 97.8%, 88.9% using SVM, KNN, Ensemble of Bagged Tree, AdaBoost, MLP, SCG NN, LMNN and RPROP classifiers, respectively.

Automatic ventricular parasystole detection based on Gaussian mixture distribution test

Ventricular parasystole is a kind of cardiac disease that can induce sudden death. To diagnose this disease, cardiologists often spend a long time tracking dynamic electrocardiogram to identify parasystolic episodes. It is urgent to develop automatic detection based on artificial intelligence to help doctors effectively improve the diagnosis rate in the early stage. In this paper, an identification algorithm based on the Gaussian mixture distribution test is proposed. Firstly, the Gaussian mixture (GM) model is established to characterize the intervals of adjacent ectopic ventricular beats, modulated by sinus rhythms. Secondly, the characters of model parameters are summarized by analyzing the relative regularity of the ectopic beats. Then a clustering algorithm is proposed to detect the class number of ventricular intervals, and the expectation-maximization (EM) algorithm is used to obtain the expectation and variance of each sub-distribution. Lastly, the existence of ventricular parasystole is determined by hypothesis testing of the model parameters. Two cardiologists have marked manual annotations of ventricular parasystole to the open-access INCART 12-lead arrhythmia database as gold standards. On 75 records of the dataset, the proposed algorithm is tested using the five-fold cross-validation scheme and achieves a sensitivity of (100.00 ± 0.0)%, positive predictive (93.33 ± 11.93)%, specificity (98.46 ± 2.75)%, and overall accuracy (98.67 ± 2.39)%. These results reflect that our method is superior to the current inverted Y model of Lorenz scatterplot.

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering.

In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together. The algorithm has been validated in the electrocardiographic domain for heartbeat clustering, extracting positive evidence from the heartbeat morphology and negative evidence from the distances between heartbeats. The best result obtained on the MIT-BIH Arrhythmia database yielded an error of 1.44%. In the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database database (INCARTDB), an error of 0.601% was obtained when using two electrocardiogram (ECG) leads. When increasing the number of leads to 4, 6, 8, 10 and 12, the algorithm obtains better results (statistically significant) than with the previous number of leads, reaching an error of 0.338%. To the best of our knowledge, this is the first clustering algorithm that is able to process simultaneously any number of ECG leads. Our results support the use of PN-EAC to combine different sources of information and the value of the negative evidence.

A cascaded classifier for multi-lead ECG based on feature fusion

Background and ObjectiveElectrocardiogram (ECG) is an important diagnostic tool for the diagnosis of heart disorders. Useful features and well-designed classification method are crucial for automatic diagnosis. However, most of the contributions were in single lead or two-lead ECG signal and only features from single lead were used to classify the ECG beats. In this paper, a cascaded classification system is proposed to extract features and classify heartbeats in order to improve the performance of ECG beat classification via multi-lead ECG. MethodsIn contrast with most of the literatures, ten signal features were chosen and run on each of the 12 leads separately. Based on these features, we developed a novel feature fusion method combining information from all available leads, and then implemented a cascaded classifier utilizing random forest (RF) and multilayer perceptron (MLP). Besides, in order to reduce the feature space dimension, principal component analysis (PCA) was applied in the method. MaterialsFour open source databases including MIT-BIH Arrythmia Database, QT Database, MIT-BIH Supraventricular Arrhythmia Database and St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database (INCART Database) were used in this work. These four databases are different in classes of beats, volume of dataset, number of individual volunteers. Except INCART database, in which the recording format is 12−lead, the other three databases consist of 2−lead recordings. Above all, they all have annotations for every single beat including the type of each beat. ConclusionsExtensive experimental results shown that the average accuracy achieved 99.3%, 99.8%, 97.6% and 99.6% on four databases respectively. Compared with most state-of-the-art methods, our work has better performance and strong generalization capability.

Multi-lead model-based ECG signal denoising by guided filter

The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but individual differences bring great difficulties for ECG denoising, especially for Dynamic Electrocardiography (DCG). In this paper, a multi-lead model-based ECG signal denoising method is proposed, in which a guided filter is inherently adapted to denoise ECG signal. For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE) which can effectively preserve the detailed signal features. Thus, the guided signal producing by the statistical model can perform well in the guided filter. Especially, even the sudden morphological changes, the denoised ECG signals can still be conserved. The results on the 12-lead Arrhythmia Database and the MIT-BIH Arrhythmia Database demonstrate that the signal-to-noise ratio (SNR) improvement of the proposed method can reach as high as 21.54 dB, and the mean squared error (MSE) is less than 0.0401. Besides achievement of minimum signal distortion in comparisons with the major of the current denoising algorithms for complex noise environment, the proposed method demonstrate robustness in the complex interferences, especially in tracing the sudden morphological changes of ECG signals. Due to the remarkable superiority in preserving diagnostic and detail features of ECG signals, the proposed method can handle ECG signals with abnormal heart beats, and then can improve the accuracy detection of the disease.

QRS Detection and Measurement Method of ECG Paper Based on Convolutional Neural Networks.

In this paper, we propose an end-to-end approach to addressing QRS complex detection and measurement of Electrocardiograph (ECG) paper using convolutional neural networks (CNNs). Unlike conventional detection solutions that convert images to digital data, our method can directly detect QRS complex in images using Faster-RCNN, then the R-peak can be located and measured through a CNN. Validated by clinical ECG data in the St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database and real ECG paper from Peking University People's Hospital, the proposed method can achieve the recall of 98.32%, the precision of 99.01% in detecting and 0.012 mv of mean absolute error in measuring. Experimental results demonstrate the superior performance of our method over conventional solutions, which would pave the way to detect and measure ECG paper using CNNs.

Multi-window detection for P-wave in electrocardiograms based on bilateral accumulative area

Background and objectiveP-wave detection is one of the most challenging aspects in electrocardiograms (ECGs) due to its low amplitude, low frequency, and variable waveforms. This work introduces a novel multi-window detection method for P-wave delineation based on the bilateral accumulative area. MethodThe bilateral accumulative area is calculated by summing the areas covered by the P-wave curve with left and right sliding windows. The onset and offset of a positive P-wave correspond to the local maxima of the area detector. The position drift and difference in area variation of local extreme points with different windows are used to systematically combine multi-window and 12-lead synchronous detection methods, which are used to screen the optimization boundary points from all extreme points of different window widths and adaptively match the P-wave location. ResultsThe proposed method was validated with ECG signals from various databases, including the Standard CSE Database, T-Wave Alternans Challenge Database, PTB Diagnostic ECG Database, and the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database. The average sensitivity Se was 99.44% with a positive predictivity P+ of 99.37% for P-wave detection. Standard deviations of 3.7 and 4.3ms were achieved for the onset and offset of P-waves, respectively, which is in agreement with the accepted tolerances required by the CSE committee. ConclusionCompared with well-known delineation methods, this method can achieve high sensitivity and positive predictability using a simple calculation process. The experiment results suggest that the bilateral accumulative area could be an effective detection tool for ECG signal analysis.

Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier

In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of different statistical features were extracted from the obtained frequency bands to denote the distribution of wavelet coefficients. This study shows that RF classifier achieves superior performances compared to other decision tree methods using 10-fold cross-validation for the ECG datasets and the obtained results suggest that further significant improvements in terms of classification accuracy can be accomplished by the proposed classification system. Accurate ECG signal classification is the major requirement for detection of all arrhythmia types. Performances of the proposed system have been evaluated on two different databases, namely MIT-BIH database and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. For MIT-BIH database, RF classifier yielded an overall accuracy 99.33 % against 98.44 and 98.67 % for the C4.5 and CART classifiers, respectively. For St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, RF classifier yielded an overall accuracy 99.95 % against 99.80 % for both C4.5 and CART classifiers, respectively. The combined model with multiscale principal component analysis (MSPCA) de-noising, discrete wavelet transform (DWT) and RF classifier also achieves better performance with the area under the receiver operating characteristic (ROC) curve (AUC) and F-measure equal to 0.999 and 0.993 for MIT-BIH database and 1 and 0.999 for and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, respectively. Obtained results demonstrate that the proposed system has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).