Time adaptive ECG driven cardiovascular disease detector

Identifying the massage techniques of the masseuse is a prerequisite for guiding robotic massage. It is difficult to recognize multiple consecutive massage maps with a time series for current human action recognition algorithms. To solve the problem, a method combining a convolutional neural network, long-term neural network, and attention mechanism is proposed to identify the massage techniques in this paper. First, the pressure distribution massage map is collected by a massage glove, and the data are enhanced by the conditional variational auto-encoder. Then, the features of the massage map group in the spatial domain and timing domain are extracted through the convolutional neural network and the long- and short-term memory neural network, respectively. The attention mechanism is introduced into the neural network, giving each massage map a different weight value to enhance the network extraction of data features. Finally, the massage haptic dataset is collected by a massage data acquisition system. The experimental results show that a classification accuracy of 100% is achieved. The results demonstrate that the proposed method could identify sequential massage maps, improve the network overfitting phenomenon, and enhance the network generalization ability effectively.

The occlusion in one of the coronary arteries of the heart leads to the cardiac ailment, myocardial infarction (MI). The localization of MI based on the investigation of the morphology of the multi-lead electrocardiogram (ECG) is the initial task for the diagnosis of this ailment. In this paper, the multiscale convolutional neural network is proposed for the automated localization of MI ailment from multi-lead electrocardiogram (ECG) beats. The Fourier-Bessel (FB) series expansion based empirical wavelet transform (EWT) with fixed order ranges is introduced for the multiscale analysis of multi-lead ECG beat. The FB spectrum of each lead ECG beat is segregated into contiguous segments using the fixed order ranges. Furthermore, the order ranges from these contiguous segments are used to design an empirical wavelet filter bank for the extraction of subband signals from each lead ECG beat. The convolutional neural network (CNN) is used for the classification of various categories of MI as anterior MI (AMI), anterio-lateral MI (ALMI), anterio-septal MI (ASMI), inferior MI (IMI), inferio-lateral MI (ILMI), inferio-posterio-lateral MI (IPLMI) and normal sinus rhythm (NSR). The experimental results reveal that the lower-order range subband signal coupled with CNN attains higher average accuracy values of 99.92%, 99.34%, 99.95%, 99.95%, 99.91%, and 99.86% respectively, for AMI, ALMI, ASMI, IMI, ILMI, and IPLMI classes. The subband signal of multi-lead ECG beats with order range of [1-26] is highly affected during various categories of MI heart disease, and this band signal has higher performance as compared to the existing MI localization approaches.

Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities alongside normal signals. This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on diverse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting CVD. The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analysis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection.

Cardiovascular diseases are among the most prevalent and deadly diseases affecting humans. The most widely used diagnostic tool to interrogate cardiovascular physiology and function is an electrocardiogram (ECG). Despite its widespread availability and use, the ECG is subject to interobserver variability and suboptimal sensitivity for asymptomatic or early-stage disease. Artificial intelligence (AI), particularly deep learning (DL) approaches, has provided a suite of methods to improve both the diagnostic and prognostic utility of the ECG in multiple cardiovascular domains. AI-enabled automated ECG interpretation (most commonly using convolutional neural networks (CNNs)) has reached and even surpassed expert-level performance for arrhythmia detection and classification. Additional data-driven approaches to ECG analysis have identified paroxysmal atrial fibrillation from a record of sinus rhythm ECGs, identified left ventricular systolic dysfunction, and predicted cardiac structure and ischemic burden (e.g., acute coronary syndromes). Pragmatic implementation has demonstrated higher diagnostic yield for asymptomatic left ventricular dysfunction in the primary care setting (EAGLE). Other emerging indications include expanded data-derived outputs, such as electrolyte disturbances, biological age, and cardiovascular risk prediction. Despite a growing list of promising applications, numerous translational hurdles remain before routine implementation. Generalizability is limited due to differences in training and target populations. Bias related to sex, race, and comorbidities is an important limiting factor to fair and equitable implementation. Other considerations include “black box” concerns with DL, clinical interpretability and adoption, medicolegal liability, and integration with clinical workflows and infrastructure. Related to these factors, data privacy, algorithmic fairness, accountability, and transparency are important to consider as AI-ECG continues to undergo regulatory scrutiny and outcomes-based validation. In conclusion, AI and ECG represent a major shift towards precision cardiology by improving prediction, screening, and early detection of cardiovascular disease. We anticipate continued improvements with prospective outcome studies, transparent and explainable approaches, and careful regulatory review to ensure safe and effective implementation in the clinic.

An accurate electrocardiogram (ECG) beat classification can benefit the diagnosis of the cardiovascular disease. Deep convolutional neural networks (CNN) can automatically extract valid features from data, which is an effective way for the classification of the ECG beats. However, the fully-connected layer in CNNs requires a fixed input dimension, which limits the CNNs to receive fixed-scale inputs. Signals of different scales are generally processed into the same size by segmentation and downsampling. If information loss occurs during a uniformly-sized process, the classification accuracy will ultimately be affected. To solve this problem, this paper constructs a new CNN framework spatial pyramid pooling (SPP) method, which solves the deficiency caused by the size of input data. The Massachusetts Institute of Technology-Biotechnology (MIT-BIH) arrhythmia database is employed as the training and testing data for the classification of heartbeat signals into six categories. Compared with the traditional method, which may lose a large amount of important information and easy to be over-fitted, the robustness of the proposed method can be guaranteed by extracting data features from different sizes. Experimental results show that the proposed architecture network can extract more high-quality features and exhibits higher classification accuracy (94%) than the traditional deep CNNs (90.4%).

This thesis mainly studies the state-of-the-art methods and models of Precipitable Water Vapor (PWV) derived from ground-based Global Navigation Satellite Systems (GNSS) and its applications in rainfall forecast. Firstly, Convolutional Neural Networks (CNN) is utilized for feature extraction from GNSS-PWV data, leveraging their powerful image processing capabilities to identify spatial features in PWV. Subsequently, the Autoregressive Integrated Moving Average (ARIMA) model is applied for time series analysis, capturing the temporal dependencies in PWV variations. To further enhance the time series prediction performance, Long and Short Term Memory Neural Network (LSTM) is introduced to handle the long-term and short-term dependencies in PWV data, addressing the limitations of traditional time series models. Additionally, attention mechanisms are integrated into the LSTM models to improve their focus on critical time steps, thereby increasing prediction accuracy. To optimize the model parameters, the Slime Mold Algorithm (SMA) is employed. SMA, a novel optimization algorithm inspired by the foraging behavior of slime mold, efficiently searches for optimal solutions in high-dimensional spaces, thereby enhancing the convergence speed and prediction accuracy of the models. Experimental results demonstrate that the models integrating multiple deep learning techniques perform excellently in GNSS-PWV retrieval and rainfall forecasting, significantly outperforming single models. Keywords: global navigation satellite system, precipitable water vapor, rainfall forecasting, deep learning

Enhancing the detection of atrial fibrillation from wearable sensors with neural style transfer and convolutional recurrent networks

During a tokamak discharge, the plasma can vary between different confinement regimes: low (L), high (H) and, in some cases, a temporary (intermediate state), called dithering (D). In addition, while the plasma is in H mode, edge localized modes (ELMs) can occur. The automatic detection of changes between these states, and of ELMs, is important for tokamak operation. Motivated by this, and by recent developments in deep learning, we developed and compared two methods for automatic detection of the occurrence of L-D-H transitions and ELMs, applied on data from the TCV tokamak. These methods consist in a convolutional neural network and a convolutional long short term memory neural network. We measured our results with regards to ELMs using ROC curves and Youden’s score index, and regarding state detection using Cohen’s Kappa index.

Background Risk stratification of heart failure (HF) in individuals without prevalent heart disease is crucial for early intervention and prevention. Electrocardiogram (ECG)-based artificial intelligence models have shown promise when predicting HF events at long follow-up periods. However, they are still limited by imbalanced case-control data and small labelled datasets. Moreover, the electrophysiological mechanisms underlying HF remain understudied. Purpose We compared the performance of a baseline convolutional neural network (CNN) model designed to predict HF events from the ECG at 8 different timepoints (1, 2, 3, 4, 6, 8, 10 & 12 years after the ECG acquisition), with a fine-tuned self-supervised model, pre-trained using a large amount of unlabelled ECGs. Subsequently, we investigated the genetic architecture of the prediction from the strongest model. Methods The baseline model consisted of 6 convolutional layers, an attentive pooling layer and 3 fully connected layers. The fine-tuned self-supervised model was composed of a frozen self-supervised feature extraction block, and a trainable classification block. The self-supervised block, composed of 6 convolutional layers, aimed to extract relevant features from the ECG and was trained with 637,438 publicly available ECGs. The classification block, composed of 1 convolutional layer, an attentive pooling layer and 3 fully connected layers, used a loss function designed to optimize the prediction of HF at the 8 timepoints. The models were trained and tested using 86,567 ECGs from UK Biobank (69,257 for training and 17,310 for test). Then, we performed a genome-wide association study (GWAS) on the standardized output at 8 years of the model with the highest area under the curve (AUC), with sex, age, body mass index, genetic array and the RR interval. Results The fine-tuned self-supervised model showed a better performance across all follow-up times when compared to the baseline model, increasing the AUC 7% on average (Figure 1). The GWAS revealed 27 candidate genes at 23 genome-wide significant loci (P < 5 x 10-8), 19 previously proposed for ECG traits, 9 of these for cardiac structural or functional traits (6 for HF), and 4 new (PATJ, BOLL, NATA/PTGIS and CCDC63). We found these genes were significantly enriched in cardiac conduction and regulation of cardiac contraction processes (Figure 2). Conclusions The use of features derived from a pretrained self-supervised model improved the performance of the CNN model, allowing a better prediction of HF at long follow-up periods. Also, the genetics underlying the ECG-based prediction of HF indicated candidate genes previously reported for ECG and arrhythmia phenotypes, highlighting electrophysiological mechanisms implicated in HF. Our findings support the integration of deep learning with genetic analyses to develop more accurate and clinically valuable risk prediction tools for early intervention and prevention of HF.AUC of the two CNN models Significantly enriched mechanisms

Heart diseases are a major illness worldwide. There is a need for an accurate and reliable diagnosis procedure, which should not put heavy burden on the already overwhelmed medical staff, always available, and easily accessible, for people with high risk of heart diseases. Machine learning has the ability to learn from large amounts of data, and it may offer accurate and reliable diagnosis of new data. In this paper, two convolutional neural network (CCN) architectures have been evaluated, i.e AlexNet and GoogleNet, in order to help diagnosing four heart conditions: Arrhythmia (ARR), Atrial Fibrillation (AF), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). A dataset of Electrocardiogram (ECG) Media files for heart related problems is fed into a deep learning (CNN) module to learn features and link them to corresponding labels. Results have showed that this technique is promising and could provide reliable solution to quick and reliable diagnosis of heart conditions, with an accuracy of 97.6%.

Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular, deep learning methods have been proven to be effective and have been widely used. The attention mechanism has attracted extensive attention in many fields in a series of deep learning methods. Off-the-shelf solutions based on deep learning and attention mechanism for ECG classification mostly give weights to time points. None of the existing methods were considered using the attention mechanism dealing with ECG signals at the level of heartbeats. In this paper, we propose a beat-level fusion net (BLF-Net) for multiclass arrhythmia classification by assigning weights at the heartbeat level, according to the contribution of the heartbeat to diagnostic results. This algorithm consists of three steps: (1) segmenting the long ECG signal into short beats; (2) using a neural network to extract features from heartbeats; and (3) assigning weights to features extracted from heartbeats using an attention mechanism. We test our algorithm on the PTB-XL database and have superiority over state-of-the-art performance on six classification tasks. Besides, the principle of this architecture is clarified by visualizing the weight of the attention mechanism. The proposed BLF-Net is shown to be useful and automatically provides an effective network structure for arrhythmia classification, which is capable of aiding cardiologists in arrhythmia diagnosis.

Cardiovascular diseases (CVDs) remain a leading cause of global mortality, underscoring the urgent need for early and accurate diagnosis. Electrocardiograms (ECGs) are a widely used, noninvasive tool for detecting cardiac abnormalities, but manual interpretation can be time- consuming and prone to error. This study explores advanced machine learning (ML) and deep learning (DL) methods to improve the detection of CVDs from ECG images, leveraging their ability to analyze complex patterns with high precision. Models such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and ensemble frameworks are utilized, alongside traditional ML models like XGBoost, CatBoost, and LightGBM. The performance of these models is evaluated based on precision, recall, F1-score, and accuracy. Results show that DL models, particularly an ensemble of CNN and ViT, achieve superior accuracy , outperforming standalone ML methods. Furthermore, an ensemble of XGBoost, CatBoost, and LightGBM achieves competitive results, while Optuna-tuned XGBoost further enhances ML performance. The findings highlight the potential of combining ML and DL techniques for robust and reliable CVD detection in ECG images. This research demonstrates the promise of AI-driven solutions in advancing medical diagnostics and supports the integration of automated systems for early detection of cardiovascular diseases.

To satisfy the need to accurately monitor emotional stress, this paper explores the effectiveness of the attention mechanism based on the deep learning model CNN (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory) As different attention mechanisms can cause the framework to focus on different positions of the feature map, this discussion adds attention mechanisms to the CNN layer and the BiLSTM layer separately, and to both the CNN layer and BiLSTM layer simultaneously to generate different CNN–BiLSTM networks with attention mechanisms. ECG (electrocardiogram) data from 34 subjects were collected on the server platform created by the Institute of Psychology of the Chinese Academy of Science and the researches. It verifies that the average accuracy of CNN–BiLSTM is up to 0.865 without any attention mechanism, while the highest average accuracy of 0.868 is achieved using the CNN–attention–based BiLSTM.

Millions of clinicians rely daily on automated preliminary electrocardiogram (ECG) interpretation. Critical comparisons of machine learning-based automated analysis against clinically accepted standards of care are lacking. To use readily available 12-lead ECG data to train and apply an explainability technique to a convolutional neural network (CNN) that achieves high performance against clinical standards of care. This cross-sectional study was conducted using data from January 1, 2003, to December 31, 2018. Data were obtained in a commonly available 12-lead ECG format from a single-center tertiary care institution. All patients aged 18 years or older who received ECGs at the University of California, San Francisco, were included, yielding a total of 365 009 patients. Data were analyzed from January 1, 2019, to March 2, 2021. A CNN was trained to predict the presence of 38 diagnostic classes in 5 categories from 12-lead ECG data. A CNN explainability technique called LIME (Linear Interpretable Model-Agnostic Explanations) was used to visualize ECG segments contributing to CNN diagnoses. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated for the CNN in the holdout test data set against cardiologist clinical diagnoses. For a second validation, 3 electrophysiologists provided consensus committee diagnoses against which the CNN, cardiologist clinical diagnosis, and MUSE (GE Healthcare) automated analysis performance was compared using the F1 score; AUC, sensitivity, and specificity were also calculated for the CNN against the consensus committee. A total of 992 748 ECGs from 365 009 adult patients (mean [SD] age, 56.2 [17.6] years; 183 600 women [50.3%]; and 175 277 White patients [48.0%]) were included in the analysis. In 91 440 test data set ECGs, the CNN demonstrated an AUC of at least 0.960 for 32 of 38 classes (84.2%). Against the consensus committee diagnoses, the CNN had higher frequency-weighted mean F1 scores than both cardiologists and MUSE in all 5 categories (CNN frequency-weighted F1 score for rhythm, 0.812; conduction, 0.729; chamber diagnosis, 0.598; infarct, 0.674; and other diagnosis, 0.875). For 32 of 38 classes (84.2%), the CNN had AUCs of at least 0.910 and demonstrated comparable F1 scores and higher sensitivity than cardiologists, except for atrial fibrillation (CNN F1 score, 0.847 vs cardiologist F1 score, 0.881), junctional rhythm (0.526 vs 0.727), premature ventricular complex (0.786 vs 0.800), and Wolff-Parkinson-White (0.800 vs 0.842). Compared with MUSE, the CNN had higher F1 scores for all classes except supraventricular tachycardia (CNN F1 score, 0.696 vs MUSE F1 score, 0.714). The LIME technique highlighted physiologically relevant ECG segments. The results of this cross-sectional study suggest that readily available ECG data can be used to train a CNN algorithm to achieve comparable performance to clinical cardiologists and exceed the performance of MUSE automated analysis for most diagnoses, with some exceptions. The LIME explainability technique applied to CNNs highlights physiologically relevant ECG segments that contribute to the CNN's diagnoses.

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