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
2048311188 2043268735 2805227459 1988183757
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 new technique for classifying patterns of movement via electromyographic (EMG) signals is presented. Two methods (conventional autoregressive (AR) coefficients and cepstral coefficients) for extracting features from EMG signals and three classification algorithms (Euclidean Distance Measure (EDM), Weighted Distance Measure (WDM), and Maximum Likelihood Method (MLM)) for discriminating signals representative of broad classes of movements are described and compared. These three classifiers are derived from Bayes classifier with some assumptions, the relationship among them is discussed. The conventional MLM is modified to avoid heavy matrix inversion. Six able-bodied subjects with two pairs of surface electrodes located on bilateral sternocleidomastoid and upper trapezius muscles were studied in the experiment. The EMG signals of 20 repetitions of 10 motions were analyzed for each subject. Experimental results showed that mean recognition rate of the cepstral coefficients was at least 5 superior to that of the AR coefficients. The improvement achieved by the cepstral method was statistically significant for all the three classifiers. Reasons for the superiority of cepstral features were investigated from the feature space and frequency domain, respectively. The cepstral coefficients owned better cluster separability in feature space and they emphasized the more informative part in the frequency domain. The discrimination rate of the MLM was the highest among three classifiers. Incorporation of the cepstral features with the MLM could reduce the misclassification rate by 10.6 when compared with the combination of AR coefficients and EDM. Proper choice of five of ten motions could further raise the recognition rate to more than 95 . > Abstract Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG . The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63 ) and of supraventricular ectopic beats (95.57 ) at a low sampling rate of 114 Hz . Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies. 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.