This paper proposes a novel approach for heartbeat classification from single-lead electrocardiogram (ECG) signals based on the novel adaptive Fourier decomposition (AFD). AFD is a recently developed signal processing tool that provides useful morphological features, referred to as AFD-derived instantaneous frequency (IF) features, that are different from those provided by traditional tools. A support vector machine (SVM) classifier is trained with the AFD-derived IF features, ECG landmark features, and RR interval features. To evaluate the performance of the trained classifier, the Association for the Advancement of Medical Instrumentation (AAMI) standard is applied to the publicly available benchmark databases, including MIT-BIH arrhythmia database and MIT-BIH supraventricular arrhythmia database, to classify heartbeats from single-lead ECG. The overall performance in terms of sensitivities and positive predictive values is comparable to the state-of-the-art automatic heartbeat classification algorithms based on two-leads ECG.
For methods in the first category, feature extraction is a critical process in automatic ECG classification analysis. An effective feature extraction method cannot only simplify computation, but also generate a superior classification performance. ECG features commonly employed for classification task are based on ECG landmarks including P, Q, R, S and T; for example, the landmark morphology @cite_22 @cite_35 and intervals between landmarks @cite_4 . Researchers also consider frequency domain features based on Fourier transform @cite_39 and other features based on Hermite coefficients @cite_15 , principal component analysis (PCA) @cite_7 , and independent component analysis (ICA) @cite_7 , etc. Yet another set of features focus on the non-stationarity of the ECG into account, and researchers take the ECG time-frequency representation into; for example, short time Fourier transform (STFT) @cite_14 , continuous wavelets transform (CWT) @cite_31 , Wigner-Ville distribution @cite_40 , and synchrosqueezing transform @cite_9 . Most studies choose support vector machine (SVM) as their classifier @cite_7 @cite_22 @cite_9 , while @cite_4 @cite_20 choose linear discriminant (LD).
@cite_35 @cite_31 @cite_14 @cite_4 @cite_22 @cite_7 @cite_9 @cite_39 @cite_40 @cite_15 @cite_20
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In this paper, we present a patient-adaptable algorithm for ECG heartbeat classification, based on a previously developed automatic classifier and a clustering algorithm. Both classifier and clustering algorithms include features from the RR interval series and morphology descriptors calculated from the wavelet transform. Integrating the decisions of both classifiers, the presented algorithm can work either automatically or with several degrees of assistance. The algorithm was comprehensively evaluated in several ECG databases for comparison purposes. Even in the fully automatic mode, the algorithm slightly improved the performance figures of the original automatic classifier; just with less than two manually annotated heartbeats (MAHB) per recording, the algorithm obtained a mean improvement for all databases of 6.9 in accuracy A, of 6.5 in global sensitivity S and of 8.9 in global positive predictive value P+. An assistance of just 12 MAHB per recording resulted in a mean improvement of 13.1 in A, of 13.9 in S, and of 36.1 in P+. For the assisted mode, the algorithm outperformed other state-of-the-art classifiers with less expert annotation effort. The results presented in this paper represent an improvement in the field of automatic and patient-adaptable heartbeats classification, concluding that the performance of an automatic classifier can be improved with an efficient handling of the expert assistance. The wavelet transform has emerged over recent years as a powerful time–frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn. In this paper, the useful features for clinical diagnosis from electrocardiogram (ECG) signals have been extracted to speed up the diagnosis decision from doctors. Based on the background information of ECG signals, after analyzing some presented methods for ECG feature extraction, algorithm for each feature extraction have been proposed. The major methods for feature extraction we proposed contain short-time Fourier transform (STFT), Gabor filter, and matching process using Gaussian models with various scales. According to the experimental results, less comparative error shows that the proposed algorithm surpasses state-of-arts as stated in the literature for extracting features on ECG signals. A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50 000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9 , a positive predictivity of 38.5 , and a false positive rate of 4.7 for the SVEB class. For the VEB class, the sensitivity was 77.7 , the positive predictivity was 81.9 , and the false positive rate was 1.2 . These results are an improvement on previously reported results for automated heartbeat classification systems. In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MIT-BIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3 (99.7 with 2.4 rejection) in the “class-oriented” evaluation and an accuracy of 86.4 in the “subject-oriented” evaluation, comparable to the state-of-the-art results for automatic heartbeat classification. The processing of ECG signal provides a wealth of information on cardiac function and overall cardiovascular health. While multi-lead ECG recordings are often necessary for a proper assessment of cardiac rhythms, they are not always available or practical, for example in fet al ECG applications. Moreover, a wide range of small non-obtrusive single-lead ECG ambulatory monitoring devices are now available, from which heart rate variability (HRV) and other health-related metrics are derived. Proper beat detection and classification of abnormal rhythms is important for reliable HRV assessment and can be challenging in single-lead ECG monitoring devices. In this manuscript, we modelled the heart rate signal as an adaptive non-harmonic model and used the newly developed synchrosqueezing transform (SST) to characterize ECG patterns. We show how the proposed model can be used to enhance heart beat detection and classification between normal and abnormal rhythms. In particular, using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and the Association for the Advancement of Medical Instrumentation (AAMI) beat classes, we trained and validated a support vector machine (SVM) classifier on a portion of the annotated beat database using the SST-derived instantaneous phase, the R-peak amplitudes and R-peak to R-peak interval durations, based on a single ECG lead. We obtained sentivities and positive predictive values comparable to other published algorithms using multiple leads and many more features. Cardiac Arrhythmias shows a condition of abnor-mal electrical activity in the heart which is a threat to humans. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the fea-tures, for the classification of heart beats according to different arrhythmias. Data were obtained from 40 records of the MIT-BIH arrhythmia database (only one lead). Cardiac arrhythmias which are found are Tachycardia, Bradycardia, Supraventricular Tachycardia, Incomplete Bundle Branch Block, Bundle Branch Block, Ventricular Tachycardia. A learning dataset for the neural network was obtained from a twenty records set which were manually classified using MIT-BIH Arrhythmia Database Directory and docu- mentation, taking advantage of the professional experience of a cardiologist. Fast Fourier transforms are used to identify the peaks in the ECG signal and then Neural Networks are applied to identify the diseases. Levenberg Marquardt Back-Propagation algorithm is used to train the network. The results obtained have better efficiency then the previously proposed methods. It is argued that the Wigner-Ville time-frequency distribution (WVD) of ECG (electrocardiography) signals reveals additional information that is not apparent in the time domain. The energy distribution and the presence of cross terms in the WVD images are heuristically interpreted on the basis of underlying electrophysiology of the human heart. Using these interpretations, time-frequency domain detection of P waves is examined, and the WVD cross terms are shown to be useful. > This paper introduces a methodology for the detection of sleep apnea based on single-lead electrocardiogram (ECG) of the patient. In the proposed technique, each QRS complex of the ECG signal is approximated using a linear combination of the lower order Hermite basis functions. The coefficients of the Hermite expansion are then used to discriminate the apnea and normal segments along with three features based on R-R time series (mean of R-R intervals, the standard deviation of R-R intervals) and energy in the error of the QRS approximation. To perform classification between the apnea and normal segments, four different types of classifiers (K-nearest neighbor (KNN), multilayer perceptron neural network (MLPNN), support vector machine (SVM), and least-square support vector machine (LS-SVM)) are used in this work.In total, 70 ECG recordings from Apnea-ECG dataset are used in this study and the performance of the proposed algorithm is evaluated based on the minute-by-minute (per-segment) classification, and per-recording (where the entire ECG recording of a subject is discriminated as the apnea or normal one) classification. By considering the events of apnea and hypopnea together, an accuracy of about 84 is achieved on the minute-by-minute basis classification using the LS-SVM classifier with the Gaussian radial basis function (RBF) kernel.On the other hand, an accuracy of about 97.14 is achieved for per-recording classification using the SVM, and LS-SVM classifiers. From the results, it is observed that the proposed methodology provides comparable accuracy with the methods existing in the literature at reduced computational cost due to the lesser number of features selected for the classification. Sleep apnea detection from single-lead ECG based on QRS morphology.Hermite basis functions for tracking respiratory induced morphological variations in the QRS complexes.We performed the analysis on the Apnea-ECG database. In this paper, we studied and validated a simple heart beat classifier based on ECG feature models selected with the focus on an improved generalization capability. We considered features from the RR series, as well as features computed from the ECG samples and different scales of the wavelet transform, at both avail able leads. The classification performance and generalization were studied using publicly available databases: the MIT-BIH Arrhythmia, the MIT-BIH Supraventricular Arrhythmia, and the St. Pe tersburg Institute of Cardiological Technics (INCART) databases. The Association for the Advancement of Medical Instrumentation recommendations for class labeling and results presentation were followed. A floating feature selection algorithm was used to obtain the best performing and generalizing models in the training and validation sets for different search configurations. The best model found comprehends eight features, was trained in a partition of the MIT-BIH Arrhythmia, and was evaluated in a completely disjoint partition of the same database. The results obtained were: global accuracy of 93 ; for normal beats, sensitivity (S) 95 , positive predictive value (P+) 98 ; for supraventricular beats, S 11 , P+ 39 ; and for ventricular beats S 81 , P+ 87 . In order to test the generalization capability, performance was also evaluated in the INCART, with results comparable to those obtained in the test set. This classifier model has fewer features and performs better than other state-of-the-art methods with results suggesting better generalization capability.