Near real-time single-beat myocardial infarction detection from single-lead electrocardiogram using Long Short-Term Memory Neural Network

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

doi:10.1016/j.bspc.2021.102683

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…Table 12 shows a summary of the disease classification, which led to the determination of what type of machine learning techniques have been used to detect, predict, or monitor CVD. 10 (11.90%) [37,45,46, 25 (28.57%) [189][190][191][192][193][194] 6 (7.14%) [195][196][197][198] 4 (4.76%) [199][200][201][202][203][204][205] 7 (8.33%)…”

Section: Examples Of Cvd Detection Utilizing Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

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According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.

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Section: Examples Of Cvd Detection Utilizing Machine Learningmentioning

confidence: 99%

“…Physikalisch-Technische Bundesanstalt diagnostic ECG [194] Long Short-Term Memory (LSTM) Accuracy: 89.56% Recall: 91.88% Specificity: 80.81%…”

Section: Myocardial Infarctionmentioning

confidence: 99%

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

View full text Add to dashboard Cite

show abstract

“…Xia used a deep convolutional neural network (DCNN) (Xia et al, 2018) for atrial fibrillation detection from short ECG signals (<5s) without any designed feature extraction procedure. Martin used long a short-term memory network (LSTM) (H. Martin et al, 2021) to detect myocardial infarction from a single lead ECG signal. Onan, 2020 proposed a CNN-LSTM framework for sentiment analysis of product review on Twitter.…”

Section: Introductionmentioning

confidence: 99%

Abnormal ECG detection based on an adversarial autoencoder

et al. 2022

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Automatic detection and alarm of abnormal electrocardiogram (ECG) events play an important role in an ECG monitor system; however, popular classification models based on supervised learning fail to detect abnormal ECG effectively. Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, and outlier detector). The ECG-AAE framework is trained only with normal ECG data. Normal ECG signals could be mapped into latent feature space and then reconstructed as the original ECG signal back in our model, while abnormal ECG signals could not. Here, the TCN is employed to extract features of normal ECG data. Then, our model is evaluated on an MIT-BIH arrhythmia dataset and CMUH dataset, with an accuracy, precision, recall, F1-score, and AUC of 0.9673, 0.9854, 0.9486, 0.9666, and 0.9672 and of 0.9358, 0.9816, 0.8882, 0.9325, and 0.9358, respectively. The result indicates that the ECG-AAE can detect abnormal ECG efficiently, with its performance better than other popular outlier detection methods.

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“…This model was tested on two different ECG databases, and the accuracies were 77.12% and 84.17%, respectively. Similar LSTM models for MI diagnosis were also developed and evaluated in [ 20 ]. For MI diagnosis on wearable devices, a lightweight Binary CNN (BCNN) was designed in [ 21 ].…”

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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Near real-time single-beat myocardial infarction detection from single-lead electrocardiogram using Long Short-Term Memory Neural Network

Cited by 15 publications

References 36 publications

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Abnormal ECG detection based on an adversarial autoencoder

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

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