Search

Solving individual differences between subjects is critical for the promotion of electrocardiogram (ECG) classification algorithms in the intelligent health monitoring industry. Popular inter-subject-based solutions usually require the manual labeling of heartbeats and frequent updating of the model for new subjects. To track these problems, we propose a hypergraph and cross-attention-based unsupervised domain adaptation (HGCA-UDA) framework for the myocardial infarction localization. Specifically, we first build a hypergraph-based dual-channel network, that can simultaneously learn specific feature representations from an ECG lead and disease category levels for samples from different domains. We then design a cross-attention module to align cross-domain locally similar samples. Subsequently, a domain alignment strategy based on the Wasserstein distance is proposed to align the global edge feature distribution. Finally, a pseudo-label generation scheme is proposed to further align fine-grained category information. We conduct extensive experiments on two public benchmark datasets (the Physikalisch-Technische Bundesanstalt (PTB) and PTB_XL database), and the results show that the proposed HGCR-UDA (with unlabeled patients) achieves comparable results compared with state-of-the-art inter-patient-based methods (with labeled patients) and has excellent applications prospects in the field of intelligent health monitoring. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG) is a sophisticated tool for the diagnosis of myocardial infarction (MI). Deep learning approaches can support MI diagnosis based on ECG data. However, due to privacy issues and the sensitive nature of ECG data, the use of deep learning methods to access large amounts of ECG data for training remains challenging. Insufficient data will lead to a decrease in diagnosis performance. In addition, most deep learning approaches are trained with multiple-lead ECG data, thereby limiting the extension to portable single-lead ECG monitoring devices. Investigating automated single-lead ECG interpretation is a continuing concern in the development of mobile and wearable ECG monitoring devices. This paper proposes an automated myocardial infarction detection model — SLC-GAN — that synthesizes single-lead ECG data with high morphological similarity through generative adversarial networks (GANs). It automatically detects MI using convolutional neural networks (CNNs) with real ECG data and synthetic ECGs from the GAN. The empirical results indicate that our new SLC-GAN method performs impressively compared with other MI classification methodologies on single-lead ECG from the PTB Diagnostic ECG Database. The MI classification accuracy of SLC-GAN reaches 99.06% with 5-fold cross-validation. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is a useful diagnostic tool to diagnose various cardiovascular diseases (CVDs) such as myocardial infarction (MI). The ECG records the heart's electrical activity and these signals are able to reflect the abnormal activity of the heart. However, it is challenging to visually interpret the ECG signals due to its small amplitude and duration. Therefore, we propose a novel approach to automatically detect the MI using ECG signals. In this study, we implemented a convolutional neural network (CNN) algorithm for the automated detection of a normal and MI ECG beats (with noise and without noise). We achieved an average accuracy of 93.53% and 95.22% using ECG beats with noise and without noise removal respectively. Further, no feature extraction or selection is performed in this work. Hence, our proposed algorithm can accurately detect the unknown ECG signals even with noise. So, this system can be introduced in clinical settings to aid the clinicians in the diagnosis of MI. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the main cause of cardiac death worldwide. The Coronary Artery Disease (CAD) is one of the leading causes of these CVD deaths. CAD condition progresses rapidly, if not diagnosed and treated at an early stage may eventually lead to an irreversible state of heart muscle death called Myocardial Infarction (MI). Normally, the presence of these cardiac conditions is primarily reflected on the electrocardiogram (ECG) signal. However, it is challenging and requires rich experience to manually interpret the visual subtle changes occurring in the ECG waveforms. Thus, many automated diagnostic systems are developed to overcome these limitations. In this study, the performance of an automated diagnostic system developed for detection of CAD and MI using three methods such as Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD) and Discrete Cosine Transform (DCT) are compared. In this study, ECG signals are subjected to DCT, DWT and EMD to obtain respective coefficients. These coefficients are reduced using Locality Preserving Projection (LPP) data reduction method. Then, the LPP features are ranked using F-value. Finally, the highly ranked coefficients are fed into the K-Nearest Neighbor (KNN) classifier to achieve the best classification performance. Our proposed system yielded highest classification results of 98.5% accuracy, 99.7% sensitivity and 98.5% specificity using only seven features obtained using DCT technique. The screening system can help the cardiologists in detecting the CAD and hence presents any possible MI by prescribing suitable medications. It can be employed in routine community screening, old age homes, polyclinics and hospitals. [ABSTRACT FROM AUTHOR]