ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features

Robustness of Deep Learning models in electrocardiogram noise detection and classification

BACKGROUND AND OBJECTIVE

Automatic electrocardiogram (ECG) signal analysis for heart disease detection has gained significant attention due to busy lifestyles. However, ECG signals are susceptible to noise, which adversely affects the performance of ECG signal analysers. Traditional blind filtering methods use predefined noise frequency and filter order, but they alter ECG biomarkers. Several Deep Learning-based ECG noise detection and classification methods exist, but no study compares recurrent neural network (RNN) and convolutional neural network (CNN) architectures and their complexity.

METHODS

This paper introduces a knowledge-based ECG filtering system using Deep Learning to classify ECG noise types and compare popular computer vision model architectures in a practical Internet of Medical Things (IoMT) framework. Experimental results demonstrate that the CNN-based ECG noise classifier outperforms the RNN-based model in terms of performance and training time.

RESULTS

The study shows that AlexNet, visual geometry group (VGG), and residual network (ResNet) achieved over 70% accuracy, specificity, sensitivity, and F1 score across six datasets. VGG and ResNet performances were comparable, but VGG was more complex than ResNet, with only a 4.57% less F1 score.

CONCLUSIONS

This paper introduces a Deep Learning (DL) based ECG noise classifier for a knowledge-driven ECG filtering system, offering selective filtering to reduce signal distortion. Evaluation of various CNN and RNN-based models reveals VGG and Resnet outperform. Further, the VGG model is superior in terms of performance. But Resnet performs comparably to VGG with less model complexity.

Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer

RATIONALE AND OBJECTIVES

An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contrast-enhanced computed tomography (CT) images for survival prediction in patients with GBC after surgical resection.

METHODS

A total of 167 patients with GBC who underwent surgical resection at two medical institutions were retrospectively enrolled. After obtaining the pre-treatment CT images, the tumor lesions were manually segmented, and handcrafted radiomics features were extracted. A clinical prognostic signature and radiomics signature were built using machine learning algorithms based on the optimal clinical features or handcrafted radiomics features, respectively. Subsequently, a DenseNet121 model was employed for transfer learning on the radiomics image data and as the basis for the deep learning signature. Finally, we used logistic regression on the three signatures to obtain the unified multimodal model for comprehensive interpretation and analysis.

RESULTS

The integrated model performed better than the other models, exhibiting the highest area under the curve (AUC) of 0.870 in the test set, and the highest concordance index (C-index) of 0.736 in predicting patient survival rates. A Kaplan-Meier analysis demonstrated that patients in high-risk group had a lower survival probability compared to those in low-risk group (log-rank p < 0.05).

CONCLUSION

The nomogram is useful for predicting the survival of patients with GBC after surgical resection, helping in the identification of high-risk patients with poor prognosis and ultimately facilitating individualized management of patients with GBC.

ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.

Pseudo anomalies enhanced deep support vector data description for electrocardiogram quality assessment

Electrocardiogram (ECG) recordings obtained from wearable devices are susceptible to noise interference that degrades the signal quality. Traditional methods for assessing the quality of electrocardiogram signals (SQA) are mostly supervised and typically rely on limited types of noise in the training data, which imposes limitations in detecting unknown anomalies. The high variability of both ECG signals and noise presents a greater challenge to the generalization of traditional methods. In this paper, we propose a simple and effective unsupervised SQA method by modeling the SQA of ECG as a problem of anomaly detection, in which, a model of pseudo anomalies enhanced deep support vector data description is introduced to learn a more discriminative and generalized hypersphere of the high-quality ECG in a self-supervised manner. Specifically, we propose a series of ECG noise-generation methods to simulate the noise of real scenarios and use the generated noise samples as the pseudo anomalies to correct the hypersphere learned solely by the high-quality ECG samples. Finally, the quality of ECG can be measured based on the distance to the center of the hypersphere. Extensive experimental results on multiple public datasets and our constructed real-world 12-lead dataset demonstrate the effectiveness of the proposed method.

Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review

Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.

A scalogram tensor decomposition based ECG quality assessment

ECG quality assessment is crucial for reducing false alarms and physician strain in automated diagnosis of cardiovascular diseases. Recent researches have focused on constructing an automatic noisy ECG record rejection mechanism. This work develops a noisy ECG record rejection system using scalogram and Tucker tensor decomposition. The system can reject ECG records, which cannot be analyzed or diagnosed. Scalogram of all 12‑lead ECG signals per subject are stacked to form a 3-way tensor. Tucker tensor decomposition is applied with empirical settings to obtain the core tensor. The core tensor is reshaped to form the latent features set. When tested using the PhysioNet challenge 2011 dataset in five-fold cross validation settings, the RusBoost ensemble classifier proved to be a very reliable option, producing an accuracy of 92.4% along with sensitivity of 87.1% and specificity of 93.5%. According to the experimental findings, combining the scalogram with Tucker tensor decomposition yields competitive performance and has the potential to be used in actual evaluation of ECG quality.

Self-attention-based convolutional neural network and time-frequency common spatial pattern for enhanced motor imagery classification

BACKGROUND

Motor imagery (MI) based brain-computer interfaces (BCIs) have promising potentials in the field of neuro-rehabilitation. However, due to individual variations in active brain regions during MI tasks, the challenge of decoding MI EEG signals necessitates improved classification performance for practical application.

NEW METHOD

This study proposes a self-attention-based Convolutional Neural Network (CNN) in conjunction with a time-frequency common spatial pattern (TFCSP) for enhanced MI classification. Due to the limited availability of training data, a data augmentation strategy is employed to expand the scale of MI EEG datasets. The self-attention-based CNN is trained to automatically extract the temporal and spatial information from EEG signals, allowing the self-attention module to select active channels by calculating EEG channel weights. TFCSP is further implemented to extract multiscale time-frequency-space features from EEG data. Finally, the EEG features derived from TFCSP are concatenated with those from the self-attention-based CNN for MI classification.

RESULTS

The proposed method is evaluated on two publicly accessible datasets, BCI Competition IV IIa and BCI Competition III IIIa, yielding mean accuracies of 79.28 % and 86.39 %, respectively.

CONCLUSIONS

Compared with state-of-the-art methods, our approach achieves superior classification results in accuracy. Self-attention-based CNN combining with TFCSP can make full use of the time-frequency-space information of EEG, and enhance the classification performance.

2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low- and Middle-Income Countries

Tetanus is a life-threatening bacterial infection that is often prevalent in low- and middle-income countries (LMIC), Vietnam included. Tetanus affects the nervous system, leading to muscle stiffness and spasms. Moreover, severe tetanus is associated with autonomic nervous system (ANS) dysfunction. To ensure early detection and effective management of ANS dysfunction, patients require continuous monitoring of vital signs using bedside monitors. Wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative to bedside monitors. Machine learning-based ECG analysis can be a valuable resource for classifying tetanus severity; however, using existing ECG signal analysis is excessively time-consuming. Due to the fixed-sized kernel filters used in traditional convolutional neural networks (CNNs), they are limited in their ability to capture global context information. In this work, we propose a 2D-WinSpatt-Net, which is a novel Vision Transformer that contains both local spatial window self-attention and global spatial self-attention mechanisms. The 2D-WinSpatt-Net boosts the classification of tetanus severity in intensive-care settings for LMIC using wearable ECG sensors. The time series imaging-continuous wavelet transforms-is transformed from a one-dimensional ECG signal and input to the proposed 2D-WinSpatt-Net. In the classification of tetanus severity levels, 2D-WinSpatt-Net surpasses state-of-the-art methods in terms of performance and accuracy. It achieves remarkable results with an F1 score of 0.88 ± 0.00, precision of 0.92 ± 0.02, recall of 0.85 ± 0.01, specificity of 0.96 ± 0.01, accuracy of 0.93 ± 0.02 and AUC of 0.90 ± 0.00.

FGSQA-Net: A Weakly Supervised Approach to Fine-Grained Electrocardiogram Signal Quality Assessment

OBJECTIVE

Due to the lack of fine-grained labels, current research can only evaluate the signal quality at a coarse scale. This article proposes a weakly supervised fine-grained electrocardiogram (ECG) signal quality assessment method, which can produce continuous segment-level quality scores with only coarse labels.

METHODS

A novel network architecture, i.e. FGSQA-Net, is developed for signal quality assessment, which consists of a feature shrinking module and a feature aggregation module. Multiple feature shrinking blocks, which combine residual CNN block and max pooling layer, are stacked to produce a feature map corresponding to continuous segments along the spatial dimension. Segment-level quality scores are obtained by feature aggregation along the channel dimension.

RESULTS

The proposed method was evaluated on two real-world ECG databases and one synthetic dataset. Our method produced an average AUC value of 0.975, which outperforms the state-of-the-art beat-by-beat quality assessment method. The results are visualized for 12-lead and single-lead signals over a granularity from 0.64 to 1.7 seconds, demonstrating that high-quality and low-quality segments can be effectively distinguished at a fine scale.

CONCLUSION

FGSQA-Net is flexible and effective for fine-grained quality assessment for various ECG recordings and is suitable for ECG monitoring using wearable devices.

SIGNIFICANCE

This is the first study on fine-grained ECG quality assessment using weak labels and can be generalized to similar tasks for other physiological signals.

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study's objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.

A novel algorithm to assess the quality of 12-lead ECG recordings: validation in a real telecardiology application

Objective. Automatic detection of Electrocardiograms (ECG) quality is fundamental to minimize costs and risks related to delayed diagnosis due to low ECG quality. Most algorithms to assess ECG quality include non-intuitive parameters. Also, they were developed using data non-representative of a real-world scenario, in terms of pathological ECGs and overrepresentation of low-quality ECG. Therefore, we introduce an algorithm to assess 12-lead ECG quality, Noise Automatic Classification Algorithm (NACA) developed in Telehealth Network of Minas Gerais (TNMG).Approach. NACA estimates a signal-to-noise ratio (SNR) for each ECG lead, where 'signal' is an estimated heartbeat template, and 'noise' is the discrepancy between the template and the ECG heartbeat. Then, clinically-inspired rules based on SNR are used to classify the ECG as acceptable or unacceptable. NACA was compared with Quality Measurement Algorithm (QMA), the winner of Computing in Cardiology Challenge 2011 (ChallengeCinC) by using five metrics: sensitivity (Se), specificity (Sp), positive predictive value (PPV),F₂, and cost reduction resulting from adoption of the algorithm. Two datasets were used for validation: TestTNMG, consisting of 34 310 ECGs received by TNMG (1% unacceptable and 50% pathological); ChallengeCinC, consisting of 1000 ECGs (23% unacceptable, higher than real-world scenario).Main results. Both algorithms reached a similar performance on ChallengeCinC, although NACA performed considerably better than QMA in TestTNMG (Se = 0.89 versus 0.21; Sp = 0.99 versus 0.98; PPV = 0.59 versus 0.08;F₂= 0.76 versus 0.16 and cost reduction 2.3 ± 1.8% versus 0.3 ± 0.3%, respectively).Significance. Implementing of NACA in a telecardiology service results in evident health and financial benefits for the patients and the healthcare system.

A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network

The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are found helpful in diagnosing cardiac diseases. However, traditional ways, i.e., a medical consultation required effort, knowledge, and time to interpret the ECG signals due to the large amount of data and complexity. Neural networks have been shown to be efficient recently in interpreting the biomedical signals including ECG and EEG. The novelty of the proposed work is using spectrograms instead of raw signals. Spectrograms could be easily reduced by eliminating frequencies with no ECG information. Moreover, spectrogram calculation is time-efficient through short-time Fourier transformation (STFT) which allowed to present reduced data with well-distinguishable form to convolutional neural network (CNN). The data reduction was performed through frequency filtration by taking a specific cutoff value. These steps makes architecture of the CNN model simple which showed high accuracy. The proposed approach reduced memory usage and computational power through not using complex CNN models. A large publicly available PTB-XL dataset was utilized, and two datasets were prepared, i.e., spectrograms and raw signals for binary classification. The highest accuracy of 99.06% was achieved by the proposed approach, which reflects spectrograms are better than the raw signals for ECG classification. Further, up- and down-sampling of the signals were also performed at various sampling rates and accuracies were attained.

Automatic ECG Quality Assessment Techniques: A Systematic Review

Cardiovascular diseases are the leading cause of death, globally. Stroke and heart attacks account for more than 80% of cardiovascular disease-related deaths. To prevent patient mismanagement and potentially save lives, effective screening at an early stage is needed. Diagnosis is typically made using an electrocardiogram (ECG) analysis. However, ECG recordings are often corrupted by different types of noise, degrading the quality of the recording and making diagnosis more difficult. This paper reviews research on automatic ECG quality assessment techniques used in studies published from 2012-2022. The CinC11 Dataset is most often used for training and testing algorithms. Only one study tested its algorithm on people in real-time, but it did not specify the demographic data of the subjects. Most of the reviewed papers evaluated the quality of the ECG recordings per single lead. The accuracy of the algorithms reviewed in this paper range from 85.75% to 97.15%. More clarity on the research methods used is needed to improve the quality of automatic ECG quality assessment techniques and implement them in a clinical setting. This paper discusses the possible shortcomings in current research and provides recommendations on how to advance the field of automatic ECG quality assessment.

Fusing depth local dual-view features and dual-input transformer framework for improving the recognition ability of motion artifact-contaminated electrocardiogram

Heart health monitoring based on wearable devices is often contaminated by various noises to varying degrees. Using signal quality indicators (SQIs) to achieve signal quality assessment (SQA) is among the most promising ways to solve this problem, but the performance of SQIs in expressing ECG quality features contaminated by motion artifact (MA) noise remains disappointing. Here, we present a novel SQA method that fuses the proposed depth local dual-view (DLDV) features and the dual-input transformer (DI-Transformer) framework to improve the recognition ability of MA-contaminated ECGs. The proposed DLDV features are to identify subtle differences between MA and ECG through depth local amplitude and phase angle features. When it fuses with the temporal relationship features extracted by DI-Transformer, its accuracy is significantly improved compared to the SQIs-based methods. In addition, we also verify the robustness and the accuracy of DLDV features on four traditional classifiers. Finally, we conduct our experiments on the two datasets. On the PhysioNet/Computing in Cardiology Challenge dataset, the DLDV features (Acc = 95.49%) outperform the combination of six SQIs features (Acc = 91.26%). When combined with our DI-Transformer, it delivered an accuracy of 99.62%, outperforming the state-of-the-art SQA methods. On the artificial testset constructed by MA noise, our DI-Transformer outperforms four traditional methods and also delivered an accuracy of 97.69%.

Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing

Infectious diseases remain a common problem in low- and middle-income countries, including in Vietnam. Tetanus is a severe infectious disease characterized by muscle spasms and complicated by autonomic nervous system dysfunction in severe cases. Patients require careful monitoring using electrocardiograms (ECGs) to detect deterioration and the onset of autonomic nervous system dysfunction as early as possible. Machine learning analysis of ECG has been shown of extra value in predicting tetanus severity, however any additional ECG signal analysis places a high demand on time-limited hospital staff and requires specialist equipment. Therefore, we present a novel approach to tetanus monitoring from low-cost wearable sensors combined with a deep-learning-based automatic severity detection. This approach can automatically triage tetanus patients and reduce the burden on hospital staff. In this study, we propose a two-dimensional (2D) convolutional neural network with a channel-wise attention mechanism for the binary classification of ECG signals. According to the Ablett classification of tetanus severity, we define grades 1 and 2 as mild tetanus and grades 3 and 4 as severe tetanus. The one-dimensional ECG time series signals are transformed into 2D spectrograms. The 2D attention-based network is designed to extract the features from the input spectrograms. Experiments demonstrate a promising performance for the proposed method in tetanus classification with an F1 score of 0.79 ± 0.03, precision of 0.78 ± 0.08, recall of 0.82 ± 0.05, specificity of 0.85 ± 0.08, accuracy of 0.84 ± 0.04 and AUC of 0.84 ± 0.03.

State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint)

Background

Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals.

Objective

This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications.

Methods

The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches.

Results

We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models.

Conclusions

We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.