Real-time frequency-independent single-Lead and single-beat myocardial infarction detection

Martin, Harold L.; Morar, Ulyana; Izquierdo, Walter; Cabrerizo, Mercedes; Cabrera, Anastasio; Adjouadi, Malek

doi:10.1016/j.artmed.2021.102179

Cited by 19 publications

(14 citation statements)

References 45 publications

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“…It can also prove that AI can discover more information hidden in subtle ECG waveform changes, or that AI is a microscope in the world of data. Some studies divided their datasets based on samples (Zhao et al, 2020), while others based on subjects (Xiao et al, 2018;Cho et al, 2020;Makimoto et al, 2020;Martin et al, 2021). In our study, we compared inter-and intra-analyses.…”

Section: Discussionmentioning

confidence: 99%

“…Some studies divided their datasets based on samples ( Zhao et al, 2020 ), while others based on subjects ( Xiao et al, 2018 ; Cho et al, 2020 ; Makimoto et al, 2020 ; Martin et al, 2021 ). In our study, we compared inter- and intra-analyses.…”

Section: Discussionmentioning

confidence: 99%

“…In the comparison test, their model outperformed cardiologists. Martin et al (2021 ) used lead II ECG data from the PTB-XL database to develop a Deep-LSTM network for detecting real-time MI. The proposed model achieved an accuracy, recall, and specificity of 77.12, 75.85, and 83.02%, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter

Cao

Chen

et al. 2022

Front. Physiol.

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Artificial intelligence is increasingly being used on the clinical electrocardiogram workflows. Few electrocardiograms based on artificial intelligence algorithms have focused on detecting myocardial ischemia using long-term electrocardiogram data. A main reason for this is that interference signals generated from daily activities while wearing the Holter monitor lowered the ability of artificial intelligence to detect myocardial ischemia. In this study, an automatic system combining denoising and segmentation modules was developed to detect the deviation of the ST-segment and J point. We proposed a ECG Bidirectional Transformer network that applied in both denoising and segmentation tasks. The denoising model achieved RMSEde, SNRimp, and PRD values of 0.074, 10.006, and 16.327, respectively. The segmentation model achieved precision, sensitivity (recall), and F1-score of 96.00, 93.06, and 94.51%, respectively. The system’s ability to distinguish the depression and elevation of the ST-segment and J point was also verified by cardiologists as well. From our ECG dataset, 103 patients with ST-segment depression and 10 patients with ST-segment elevation were detected with positive predictive values of 80.6 and 60% respectively. Using Holter ECG and transformer-based deep neural networks, we can detect subtle ST-segment changes in noisy ECG signals. This system has the potential to improve the efficacy of daily medicine and to provide a broader population-level screening for asymptomatic myocardial ischemia.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter

Cao

Chen

et al. 2022

Front. Physiol.

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“…Data augmentation for ECG The data augmentation task has also been explored for ECG applications in the previous studies. Martin et al (2021) tried to use oversampling method to augment the imbalanced data. ClementVirgeniya and Ramaraj (2021) tried to feed the data into the adaptive synthetic (ADASYN) (He et al, 2008) based sampling model, which utilized a weighted distribution for different minority class samples depending upon the learning stages of difficulty, instead of using synthetic models such as synthetic minority oversampling technique (SMOTE).…”

Section: Related Workmentioning

confidence: 99%

GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

Zhu¹,

Jielin²,

Yang³

et al. 2022

Preprint

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There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with machine learning methods are often limited by the amount of labeled data. This phenomenon is particularly problematic for clinically-relevant data, where labeling at scale can be time-consuming and costly in terms of the specialized expertise and human effort required. Moreover, deep learning classifiers may be vulnerable to adversarial examples and perturbations, which could have catastrophic consequences, for example, when applied in the context of medical treatment, clinical trials, or insurance claims. In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals. We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space. To better utilize domain-specific knowledge, we design a

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“…It achieved an accuracy of 95.22% without feature extraction and selection. In [ 19 ], a multi-layer Long Short-Term Memory (LSTM) network (a typical variant of RNN) was employed to analyze single-lead ECGs and identify MI patients. This model was tested on two different ECG databases, and the accuracies were 77.12% and 84.17%, respectively.…”

Section: Introductionmentioning

confidence: 99%

EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms

Liu

Chang

et al. 2021

Biosensors

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Multi-branch Networks (MBNs) have been successfully applied to myocardial infarction (MI) diagnosis using 12-lead electrocardiograms. However, most existing MBNs share a fixed architecture. The absence of architecture optimization has become a significant obstacle to a more accurate diagnosis for these MBNs. In this paper, an evolving neural network named EvoMBN is proposed for MI diagnosis. It utilizes a genetic algorithm (GA) to automatically learn the optimal MBN architectures. A novel fixed-length encoding method is proposed to represent each architecture. In addition, the crossover, mutation, selection, and fitness evaluation of the GA are defined to ensure the architecture can be optimized through evolutional iterations. A novel Lead Squeeze and Excitation (LSE) block is designed to summarize features from all the branch networks. It consists of a fully-connected layer and an LSE mechanism that assigns weights to different leads. Five-fold inter-patient cross validation experiments on MI detection and localization are performed using the PTB diagnostic database. Moreover, the model architecture learned from the PTB database is transferred to the PTB-XL database without any changes. Compared with existing studies, our EvoMBN shows superior generalization and the efficiency of its flexible architecture is suitable for auxiliary MI diagnosis in real-world.

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Real-time frequency-independent single-Lead and single-beat myocardial infarction detection

Cited by 19 publications

References 45 publications

Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter

Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter

GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms

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