Automated detection scheme for acute myocardial infarction using convolutional neural network and long short-term memory

Muraki, Ryosuke; Teramoto, Akira; Sugimoto, Keiko; Sugimoto, Kunihiko; Yamada, Akira; Watanabe, Eiichi

doi:10.1371/journal.pone.0264002

Cited by 15 publications

(3 citation statements)

References 28 publications

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“…LSTM is a structure with an artificial recurrent neural network that is used in deep learning algorithms. Compared with standard neural networks, LSTM is applied during unsegmented data handling owing to the property that processes entire sequences of data using feedback connections [38]. The technical structure of the model is shown in Figure 1 and Supplementary Table S1.…”

Section: Development Of the Prediction Modelmentioning

confidence: 99%

Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis

Choi

Chung

Lee³

et al. 2022

Sensors

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Intermittent manual measurement of vital signs may not rapidly predict sepsis development in febrile patients admitted to the emergency department (ED). We aimed to evaluate the predictive performance of a wireless monitoring device that continuously measures heart rate (HR) and respiratory rate (RR) and a machine learning analysis in febrile but stable patients in the ED. We analysed 468 patients (age, ≥18 years; training set, n = 277; validation set, n = 93; test set, n = 98) having fever (temperature >38 °C) and admitted to the isolation care unit of the ED. The AUROC of the fragmented model with device data was 0.858 (95% confidence interval [CI], 0.809–0.908), and that with manual data was 0.841 (95% CI, 0.789–0.893). The AUROC of the accumulated model with device data was 0.861 (95% CI, 0.811–0.910), and that with manual data was 0.853 (95% CI, 0.803–0.903). Fragmented and accumulated models with device data detected clinical deterioration in febrile patients at risk of septic shock 9 h and 5 h 30 min earlier, respectively, than those with manual data. Continuous vital sign monitoring using a wearable device could accurately predict clinical deterioration and reduce the time to recognise potential clinical deterioration in stable ED patients with fever.

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Section: Development Of the Prediction Modelmentioning

confidence: 99%

Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis

Choi

Chung

Lee³

et al. 2022

Sensors

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show abstract

“…RNN was applied to provide detailed information for the initial image, while LSTM to generate the segmentation result: this approach increases accuracy. Another RRN application in the field of cardiology was presented by Muraki et al [90]. Here, simple RNNs, LSTM, and other RNN variations (such as Gated Recurrent Units (GRU)) were successfully used to detect acute myocardial infarction (AMI) in echocardiography.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Rudnicka,

Proniewska,

Perkins

et al. 2024

Electronics

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Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.

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“…In recent years, convolutional neural networks (CNN), a type of deep learning, have been applied in various fields including medical image analysis [7] , [8] , [9] , [10] , [11] , [12] , [13] . Many studies using CNN have been conducted ECG analysis [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms

Kondo

Teramoto

Watanabe

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Objective: The early detection of cardiac disease is important because the disease can lead to sudden death and poor prognosis. Electrocardiograms (ECG) are used to screen for cardiac diseases and are useful for the early detection and determination of treatment strategies. However, the ECG waveforms of cardiac care unit (CCU) patients with severe cardiac disease are often complicated by comorbidities and patient conditions, making it difficult to predict the severity of further cardiac disease. Therefore, this study predicts the short-term prognosis of CCU patients to detect further deterioration in CCU patients at an early stage. Methods: The ECG data (II, V3, V5, aVR induction) of CCU patients were converted to image data. The transformed ECG images were used to predict short-term prognosis with a two-dimensional convolutional neural network (CNN). Results: The prediction accuracy was 77.3%. Visualization by GradCAM showed that the CNN tended to focus on the shape and regularity of waveforms, such as heart failure and myocardial infarction. Conclusion: These results suggest that the proposed method may be useful for short-term prognosis prediction using the ECG waveforms of CCU patients.

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Automated detection scheme for acute myocardial infarction using convolutional neural network and long short-term memory

Cited by 15 publications

References 28 publications

Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis

Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms

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