WavelNet: A novel convolutional neural network architecture for arrhythmia classification from electrocardiograms

An improved multi-scale feature extraction method for nonlinear signals

This paper proposes an innovative multi-scale feature extraction method for analyzing electroencephalogram (EEG) and electrocardiogram (ECG) signals. The method utilizes an energy functional derived from the Cahn-Hilliard (CH) phase field equation to extract features, aiming to improve classification accuracy. To validate its effectiveness, we integrate the extracted features with a Support Vector Machine (SVM) classifier, forming the CH-SVM model for both EEG and ECG classification. The proposed method achieves an accuracy of 97.14% for EEG and 92.65% for ECG. Compared to conventional convolutional neural network (CNN) models, it demonstrates a significant reduction in computational cost. Furthermore, in comparison to the traditional multi-scale feature extraction method-Multifractal Detrended Fluctuation Analysis (MF-DFA)-the proposed method improves EEG classification accuracy by 5.84% and ECG classification accuracy by 5.15%. These results highlight the superior performance of the CH-SVM method in biomedical signal classification, offering both enhanced accuracy and computational efficiency.

A neural network model based on attention pooling and adaptive multi-level feature fusion for arrhythmia automatic detection

With the rising incidence of cardiovascular disease, timely detection and treatment are critical for patients with arrhythmias, and the electrocardiogram (ECG) remains a vital tool for diagnosing and monitoring heart health. In automated arrhythmia detection, researchers have made significant progress in intra-patient paradigms. However, challenges persist in the inter-patient paradigm, where existing methods often rely on manually extracted features or exhibit inadequate performance in detecting anomalous categories. Against the above challenges, this paper proposes a neural network model based on Attention Pooling (AP) and Adaptive Multilevel Feature Fusion (AMFF) to enhance the performance for automatic detection of abnormal categories in the inter-patient paradigm. Among them, the attentional pooling mechanism enables the model to focus on the features of key channels and spatial locations, effectively reducing the influence of redundant information; to address the problem of ECG signal scale differences, we designed adaptive multilevel feature fusion (AMFF), which uses weighted multilevel features to achieve adaptive feature fusion and can utilize multilevel features at the same time, thus enhancing the feature expression capability of the model. Based on following the AAMI criteria, we evaluated the proposed model using the MIT-BIH arrhythmia database. The results showed that the model achieved an overall accuracy of 99.32% in the intra-patient paradigm and 93.35% in the inter-patient paradigm. For the inter-patient paradigm, the model not only performs well in N-category classification but also achieves good results in the anomaly categories of S, V, and F. This demonstrates a relatively balanced performance.

MDMNet: Multi-dimensional multi-modal network to identify organ system limitation in cardiopulmonary exercise testing

BACKGROUND AND OBJECTIVE

Cardiopulmonary exercise testing (CPET) serves as an integrative and comprehensive assessment tool for cardiorespiratory fitness. In this paper, we present a novel multi-dimensional multi-modal network (MDMNet) to identify functional limitation of organ systems via CPET, which is of great importance in clinical practice and yet a challenging task due to (1) the intricate intra-variable associations, and (2) the significant inter-individual variability.

METHODS

The proposed model has three compelling characteristics. First, we employ a dedicated embedding strategy for CPET data to map raw inputs into the learned embedding space, facilitating the detection of latent features of physiological variables. Second, we devise a novel multi-dimensional feature extraction module to capture rich features of physiological inputs at different dimensions, which consists of a one-dimensional feature extraction branch unfolding both temporal and spatial patterns of the entire data, and a two-dimensional feature extraction branch based on Gramian Angular Field (GAF) encoding to reveal the complicated temporal correlation relationships between time points within a variable. Third, we integrate these techniques with clinically significant demographic information to establish our MDMNet incorporating multi-dimensional with multi-modal learning, thereby further addressing the issues of complex intra-variable associations and inter-individual variability simultaneously.

RESULTS

We evaluated the proposed method on the publicly available CPET dataset, achieving AUC scores of 0.948, 0.949 and 0.931 for three tasks respectively.

CONCLUSIONS

The superiority of our method in discerning inter-individual differences was further demonstrated through partial least squares discriminant analysis, which holds significant potential for automated clinical application of CPET.

Shuffled ECA-Net for stress detection from multimodal wearable sensor data

BACKGROUND

Recently, stress has been recognized as a key factor in the emergence of individual and social issues. Numerous attempts have been made to develop sensor-augmented psychological stress detection techniques, although existing methods are often impractical or overly subjective. To overcome these limitations, we acquired a dataset utilizing both wireless wearable multimodal sensors and salivary cortisol tests for supervised learning. We also developed a novel deep neural network (DNN) model that maximizes the benefits of sensor fusion.

METHOD

We devised a DNN involving a shuffled efficient channel attention (ECA) module called a shuffled ECA-Net, which achieves advanced feature-level sensor fusion by considering inter-modality relationships. Through an experiment involving salivary cortisol tests on 26 participants, we acquired multiple bio-signals including electrocardiograms, respiratory waveforms, and electrogastrograms in both relaxed and stressed mental states. A training dataset was generated from the obtained data. Using the dataset, our proposed model was optimized and evaluated ten times through five-fold cross-validation, while varying a random seed.

RESULTS

Our proposed model achieved acceptable performance in stress detection, showing 0.916 accuracy, 0.917 sensitivity, 0.916 specificity, 0.914 F1-score, and 0.964 area under the receiver operating characteristic curve (AUROC). Furthermore, we demonstrated that combining multiple bio-signals with a shuffled ECA module can more accurately detect psychological stress.

CONCLUSIONS

We believe that our proposed model, coupled with the evidence for the viability of multimodal sensor fusion and a shuffled ECA-Net, would significantly contribute to the resolution of stress-related issues.

A graph-based cardiac arrhythmia classification methodology using one-lead ECG recordings

In this study, we present a novel graph-based methodology for an accurate classification of cardiac arrhythmia diseases using a single-lead electrocardiogram (ECG). The proposed approach employs the visibility graph technique to generate graphs from time signals. Subsequently, informative features are extracted from each graph and then fed into classifiers to match the input ECG signal with the appropriate target arrhythmia class. The six target classes in this study are normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), atrial premature contraction (A), and fusion (F) beats. Three classification models were explored, including graph convolutional neural network (GCN), multi-layer perceptron (MLP), and random forest (RF). ECG recordings from the MIT-BIH arrhythmia database were utilized to train and evaluate these classifiers. The results indicate that the multi-layer perceptron model attains the highest performance, showcasing an average accuracy of 99.02%. Following closely, the random forest achieves a strong performance as well, with an accuracy of 98.94% while providing critical intuitions.

MA-MIL: Sampling point-level abnormal ECG location method via weakly supervised learning

BACKGROUND AND OBJECTIVE

Current automatic electrocardiogram (ECG) diagnostic systems could provide classification outcomes but often lack explanations for these results. This limitation hampers their application in clinical diagnoses. Previous supervised learning could not highlight abnormal segmentation output accurately enough for clinical application without manual labeling of large ECG datasets.

METHOD

In this study, we present a multi-instance learning framework called MA-MIL, which has designed a multi-layer and multi-instance structure that is aggregated step by step at different scales. We evaluated our method using the public MIT-BIH dataset and our private dataset.

RESULTS

The results show that our model performed well in both ECG classification output and heartbeat level, sub-heartbeat level abnormal segment detection, with accuracy and F1 scores of 0.987 and 0.986 for ECG classification and 0.968 and 0.949 for heartbeat level abnormal detection, respectively. Compared to visualization methods, the IoU values of MA-MIL improved by at least 17 % and at most 31 % across all categories.

CONCLUSIONS

MA-MIL could accurately locate the abnormal ECG segment, offering more trustworthy results for clinical application.

Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination

Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement. Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method's performance is further evaluated. Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm. Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples.

A Residual-Dense-Based Convolutional Neural Network Architecture for Recognition of Cardiac Health Based on ECG Signals

Cardiovascular disorders are often diagnosed using an electrocardiogram (ECG). It is a painless method that mimics the cyclical contraction and relaxation of the heart's muscles. By monitoring the heart's electrical activity, an ECG can be used to identify irregular heartbeats, heart attacks, cardiac illnesses, or enlarged hearts. Numerous studies and analyses of ECG signals to identify cardiac problems have been conducted during the past few years. Although ECG heartbeat classification methods have been presented in the literature, especially for unbalanced datasets, they have not proven to be successful in recognizing some heartbeat categories with high performance. This study uses a convolutional neural network (CNN) model to combine the benefits of dense and residual blocks. The objective is to leverage the benefits of residual and dense connections to enhance information flow, gradient propagation, and feature reuse, ultimately improving the model's performance. This proposed model consists of a series of residual-dense blocks interleaved with optional pooling layers for downsampling. A linear support vector machine (LSVM) classified heartbeats into five classes. This makes it easier to learn and represent features from ECG signals. We first denoised the gathered ECG data to correct issues such as baseline drift, power line interference, and motion noise. The impacts of the class imbalance are then offset by resampling techniques that denoise ECG signals. An RD-CNN algorithm is then used to categorize the ECG data for the various cardiac illnesses using the retrieved characteristics. On two benchmarked datasets, we conducted extensive simulations and assessed several performance measures. On average, we have achieved an accuracy of 98.5%, a sensitivity of 97.6%, a specificity of 96.8%, and an area under the receiver operating curve (AUC) of 0.99. The effectiveness of our suggested method for detecting heart disease from ECG data was compared with several recently presented algorithms. The results demonstrate that our method is lightweight and practical, qualifying it for continuous monitoring applications in clinical settings for automated ECG interpretation to support cardiologists.

Automated inter-patient arrhythmia classification with dual attention neural network

BACKGROUND AND OBJECTIVES

Arrhythmia classification based on electrocardiograms (ECG) can enhance clinical diagnostic efficiency. However, due to the significant differences in the number of different categories of heartbeats, the performance of classes with fewer samples in arrhythmia classification have not met expectations under the inter-patient paradigm. This paper aims to mitigate the adverse effects of category imbalance and improve arrhythmia classification performance.

METHODS

We constructed a novel dual attention hybrid network (DA-Net) for arrhythmia classification under sample imbalance, based on modified convolutional networks with channel attention (MCC-Net) and sequence-to-sequence network with global attention (Seq2Seq). The refined local features of the input heartbeat are first extracted by MCC-Net and then sent to Seq2Seq for further feature fusion. By applying local and global attention in the feature extraction and fusion parts, respectively, the method fully fuses low-level feature details and high-level context information and enhances the ability to extract discriminative features.

RESULTS

Based on the MIT-BIH arrhythmia database, under the inter-patient paradigm without any data augmentation methods, the proposed method achieved 99.98% accuracy (ACC) for five categories. The various performance indicators are as follows: Class N: sensitivity (SEN) = 99.96%, specificity (SPEC) = 99.93%, positive predictive value (PPV) = 99.99%; Class S: SEN = 99.67%, SPEC = 99.98%, PPV = 99.56%; Class V: SEN = 100%, SPEC = 99.99%, PPV = 99.91%; Class F: SEN = 100%, PPV = 99.98%, SPEC = 97.17%. In further experiments simulating extreme cases, the model still achieved ACC of 99.54% and 98.91% in the three-category and five-category categories when the training sample size was much smaller than the test sample.

CONCLUSIONS

Without any data augmentation methods, the proposed model not only alleviates the negative impact of class imbalance and achieves excellent performance in all categories but also provides a new approach for dealing with class imbalance in arrhythmia classification. Additionally, our method demonstrates potential in conditions with fewer samples.