We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning papers. Using the database of a large telehealth network, we built a novel dataset with more than 2 million ECG tracings, orders of magnitude larger than those used in previous studies. Moreover, our dataset is more realistic, as it consist of 12-lead ECGs recorded during standard in-clinics exams. Using this data, we trained a residual neural network with 9 convolutional layers to map 7 to 10 second ECG signals to 6 classes of ECG abnormalities. Future work should extend these results to cover a large range of ECG abnormalities, which could improve the accessibility of this diagnostic tool and avoid wrong diagnosis from medical doctors.
Classical ECG software, such as University of Glasgow's ECG analysis program @cite_2 , extracts the main features of the ECG signal using signal processing techniques and use them as input for classifiers. A literature review of these methods is given by @cite_3 . @cite_27 a different approach is taken, where the ECG features are learned using an unsupervised method and then used as input to a supervised learning method.
@cite_27 @cite_3 @cite_2
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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. In this paper, we propose a novel approach based on deep learning for active classification of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation from the raw ECG data in an unsupervised way using stacked denoising autoencoders (SDAEs) with sparsity constraint. After this feature learning phase, we add a softmax regression layer on the top of the resulting hidden representation layer yielding the so-called deep neural network (DNN). During the interaction phase, we allow the expert at each iteration to label the most relevant and uncertain ECG beats in the test record, which are then used for updating the DNN weights. As ranking criteria, the method relies on the DNN posterior probabilities to associate confidence measures such as entropy and Breaking-Ties (BT) to each test beat in the ECG record under analysis. In the experiments, we validate the method on the well-known MIT-BIH arrhythmia database as well as two other databases called INCART, and SVDB, respectively. Furthermore, we follow the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation. The results obtained show that the newly proposed approach provides significant accuracy improvements with less expert interaction and faster online retraining compared to state-of-the-art methods. 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. The University of Glasgow 12 15 lead ECG analysis program has been in continuous development for over 20 years. It has been adapted to meet the needs of different users and keep abreast of changes in terminology as well as new morphological features described in the literature. It is applicable to neonates as well as adults and takes account of racial variation in wave amplitudes. It has a capability for comparing serially recorded ECGs using one of two different approaches. The many varying features of the software have led to the introduction of the descriptor Uni-G (unique) ECG analysis program