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The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogram (ECG), being one of the pivotal diagnostic tools for cardiovascular diseases, is increasingly gaining prominence in the field of machine learning. However, prevailing neural network models frequently disregard the spatial dimension features inherent in ECG signals. In this paper, we propose an ECG autoencoder network architecture incorporating low-rank attention (LRA-autoencoder). It is designed to capture potential spatial features of ECG signals by interpreting the signals from a spatial perspective and extracting correlations between different signal points. Additionally, the low-rank attention block (LRA-block) obtains spatial features of electrocardiogram signals through singular value decomposition, and then assigns these spatial features as weights to the electrocardiogram signals, thereby enhancing the differentiation of features among different categories. Finally, we utilize the ResNet-18 network classifier to assess the performance of the LRA-autoencoder on both the MIT-BIH Arrhythmia and PhysioNet Challenge 2017 datasets. The experimental results reveal that the proposed method demonstrates superior classification performance. The mean accuracy on the MIT-BIH Arrhythmia dataset is as high as 0.997, and the mean accuracy and F 1 -score on the PhysioNet Challenge 2017 dataset are 0.850 and 0.843. [ABSTRACT FROM AUTHOR]

The accurate identification of combined fetal and maternal electrocardiogram (ECG) signals plays a crucial role in assessing the health and well-being of both the pregnant woman and the fetus. This paper presents a novel framework that addresses key challenges in fetal ECG identification, including reducing the number of inputs/observations and optimizing the number of iterations required for reliable signal detection. The proposed approach leverages a combination of principal component analysis (PCA) and independent component analysis (ICA) on cardiological data obtained from implanted electrodes on the central abdomen of pregnant women.To mitigate the potential adverse effects associated with a large number of electrodes, which can impact maternal and fetal health, this framework emphasizes the importance of controlling the number of inputs. Furthermore, excessive algorithmic complexity is managed by optimizing the number of iterations. Classical statistical methods often exhibit redundancy, thus posing a significant challenge. To overcome this, an intelligent machine is introduced that evaluates the quality of estimated sources or extracted signals through time-scale images classification, employing the specially designed AlexNet Deep Neural Network Architecture. By utilizing minimal inputs, even as low as two, this intelligent machine assesses the performance of the model on test data, employing metrics such as accuracy, precision, recall, and F1 score. These metrics provide valuable insights into the blind separation model's capacity and enable the determination of the optimal number of iterations and inputs, or the simultaneous adjustment of both parameters. The proposed framework offers a comprehensive solution to enhance fetal ECG identification, promoting improved healthcare monitoring and diagnosis during pregnancy. [ABSTRACT FROM AUTHOR]

Stress is a common experience with significant impacts on our health, which has sparked an increase in research aimed at detecting stress. This paper explores the effectiveness of using Electrocardiogram (ECG) signals as a key indicator for stress detection. What distinguishes our research is the carefully constructed architecture of our deep neural network model, coupled with the meticulous optimization strategies employed. We trained and evaluated our model using the comprehensive WESAD[10] dataset, which includes PANAS[10] self-reports. Our proposed model demonstrates remarkable improvements in accuracy and F1 score compared to previous attempts, highlighting the potential of deep learning techniques in understanding and predicting stress levels based on physiological data and harness its power to offer an objective measure of the body's response to stress. The results of the DeepChill demonstrate the effectiveness of the deep learning model in detecting stress from ECG signals, highlighting its potential for health monitoring and intervention. This contributes to the field of affective computing by providing a reliable and efficient method for stress detection. The proposed fully connected neural network model has attained an impressive accuracy of 92% surpassing the 85% accuracy reported on the WESAD dataset. [ABSTRACT FROM AUTHOR]

Numerous studies focus on diagnosing heart diseases using deep learning methods applied to 12-lead electrocardiographic (ECG) records. However, these studies often face limitations due to reliance on specific datasets, which vary in size and parameters such as patient metadata, the number of doctors annotating ECGs, types of devices used for ECG recording, data preprocessing techniques, etc. It is well-known that high-quality deep neural networks trained on one ECG dataset may not necessarily perform well on other datasets or in different clinical settings. In this paper, we propose a methodology designed to enhance the quality of heart disease prediction regardless of the dataset. We achieve this by first training neural networks on a variety of labeled datasets, then fine-tuning for specific datasets, significantly improving prediction accuracy. We demonstrate its applicability by training various neural networks on a large private dataset TIS, which contains a wide range of ECG records from multiple hospitals, and on a relatively smaller public dataset, PTB-XL. Our results show that networks trained on a large dataset improves classification performance. Furthermore, these networks fine-tuned on PTB-XL outperform those trained exclusively on smaller datasets. Additionally, we made the weights of our pre-trained models publicly available, enabling researchers and clinicians to adapt these models to their specific datasets. • Models trained on distinct ECG records may notably differ in quality across datasets. • Pre-training models on various ECG datasets improves the classification performance. • Fine-tuning networks on smaller ECG datasets significantly improves performance. • Researchers can use pre-trained model weights to adapt them for their tasks and data. [ABSTRACT FROM AUTHOR]

AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model's discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy. [ABSTRACT FROM AUTHOR]

Classification of ECG signals plays an important role in the field of medical diagnosis. Despite that much progress has been made in ECG classification in recent years, most of them focus on single-lead cardiac feature extraction. In this paper, we propose a new classification method based on twelve-lead surface ECG signal by fusing improved hand-crafted ECG features and deep learning classifiers for paroxysmal atrial fibrillation (PAF) detection. Three-dimensional dominated components are extracted without ECG signal segmentation operations, and continuous wavelet transform (CWT) is adopted for time–frequency representation to reflect essential characteristics of multi-resolution frequency domain at different scales. This kind of time–frequency representation has high discriminative power to detect PAF even before pathologic changes in surface twelve-lead ECG signals. Five pre-trained deep neural networks models are used to perform transfer learning and feature fusion. ECG signals are finally classified by using a feature fusion method based on multiple deep networks. • Hand-crafted ECG features and deep learning techniques are combined. • New time–frequency representation is proposed to detect PAF. • The proposed method can detect PAF before pathologic changes in ECGs. • Five pre-trained deep models are used to perform transfer learning. [ABSTRACT FROM AUTHOR]

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (VX, VY, and VZ) or EASI leads (VES, VAS, and VAI). The innovative ECG reconstruction network called M2Eformer is composed of a 2D-ECGblock and a ProbDecoder module. The 2D-ECGblock module adaptively segments EASI leads into multi-periods based on frequency energy, transforming the 1D time series into a 2D tensor representing within-cycle and between-cycle variations. The ProbDecoder module aims to extract Probsparse self-attention and achieve one-step output for the target leads. Experimental results from comparing recorded and reconstructed 12-lead ECG using Frank leads indicate that M2Eformer outperforms traditional ECG reconstruction methods on a public database. In this study, a self-constructed database (10 healthy individuals + 15 patients) was utilized for the clinical diagnostic validation of ECG reconstructed from EASI leads. Subsequently, both the ECG reconstructed using EASI and the recorded 12-lead ECG were subjected to a double-blind diagnostic experiment conducted by three cardiologists. The overall diagnostic consensus among three cardiology experts, reaching a rate of 96%, indicates the significant utility of EASI-reconstructed 12-lead ECG in facilitating the diagnosis of cardiac conditions. [ABSTRACT FROM AUTHOR]

Deep-learning algorithms hold promise in processing physiological signal data, including electrocardiograms (ECGs) and electroencephalograms (EEGs). However, healthcare often requires long-term monitoring, posing a challenge to traditional deep-learning models. These models are generally trained once and then deployed, which limits their ability to adapt to the dynamic and evolving nature of healthcare scenarios. Continual learning—known for its adaptive learning capabilities over time—offers a promising solution to these challenges. However, there remains an absence of consolidated literature, which reviews the techniques, applications, and challenges of continual learning specific to physiological signal analysis, as well as its future directions. Bridging this gap, our review seeks to provide an overview of the prevailing techniques and their implications for smart healthcare. We delineate the evolution from traditional approaches to the paradigms of continual learning. We aim to offer insights into the challenges faced and outline potential paths forward. Our discussion emphasizes the need for benchmarks, adaptability, computational efficiency, and user-centric design in the development of future healthcare systems. [ABSTRACT FROM AUTHOR]