Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients

Goto, Shinichi; Kimura, Mai; Katsumata, Yoshinori; Kamatani, Takashi; Ichihara, Genki; Ko, Seien; Sasaki, Junichi; Fukuda, Keiichi; Sano, Motoaki

doi:10.1371/journal.pone.0210103

Cited by 74 publications

(52 citation statements)

References 17 publications

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“…Our network structure combining one-dimensional convolution (1D-conv) and long–short term memory (LSTM) efficiently learned the complex time-series pattern of voltage in an ECG record and effectively estimated the time point of VT without the need for a respiratory gas analyzer. This combination of 1D-conv and LSTM has been shown to be effective in learning 12-lead ECG in our previous investigation 18 . The two-dimensional convolution network is useful in extracting information from a still image 19 .…”

Section: Discussionmentioning

confidence: 90%

Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data

et al. 2020

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Regular aerobic physical activity is of utmost importance in maintaining a good health status and preventing cardiovascular diseases (CVDs). Although cardiopulmonary exercise testing (CPX) is an essential examination for noninvasive estimation of ventilatory threshold (VT), defined as the clinically equivalent to aerobic exercise, its evaluation requires an expensive respiratory gas analyzer and expertize. To address these inconveniences, this study investigated the feasibility of a deep learning (DL) algorithm with single-lead electrocardiography (ECG) for estimating the aerobic exercise threshold. Two hundred sixty consecutive patients with CVDs who underwent CPX were analyzed. Single-lead ECG data were stored as time-series voltage data with a sampling rate of 1000 Hz. The data of preprocessed ECG and time point at VT calculated by respiratory gas analyzer were used to train a neural network. The trained model was applied on an independent test cohort, and the DL threshold (DLT; a time of VT estimated through the DL algorithm) was calculated. We compared the correlation between oxygen uptake of the VT (VT–VO2) and the DLT (DLT–VO2). Our DL model showed that the DLT–VO2 was confirmed to be significantly correlated with the VT–VO2 (r = 0.875; P < 0.001), and the mean difference was nonsignificant (−0.05 ml/kg/min, P > 0.05), which displayed strong agreements between the VT and the DLT. The DL algorithm using single-lead ECG data enabled accurate estimation of VT in patients with CVDs. The DL algorithm may be a novel way for estimating aerobic exercise threshold.

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Section: Discussionmentioning

confidence: 90%

Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data

et al. 2020

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“…MI is the most dangerous form of IHD with the highest mortality rate [10].MI is usually diagnosed by changes in the ECG due to the increase of serum enzymes, such as creatine phosphokinase and troponin T or I [10]. ECG is the most reliable tool for interpreting MI [12][13][14], apart from the emergence of expensive and sophisticated alternatives [7]. However, interpreting MI via morphological ECG is a challenging task due to its significant variation in different patients under different physical conditions [15,16].…”

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confidence: 99%

“…The algorithm that is usually used for sequential models is a deep learning technique [17]. Some deep learning algorithms that used the sequential model to interpret MI from ECG signals have been presented in References [12,14]. These studies combine Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to interpret MI only in one (Lead I) or several leads (I, II, V1, V2, V3, V4, V5, V6).…”

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confidence: 99%

Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier

et al. 2019

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The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals' morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal.Algorithms 2019, 12, 118 2 of 12 records electrical signals related to heart activity and producing a voltage-chart cardiac rate and being a cardiological test that has been used in the past 100 years [7]. ECG signals have three different waveforms for each cardiac cycle: P wave, QRS complex, and T wave in normal rate [8]. In other cases, ECG form changes in the T waveform, the ST interval length, and ST elevation. Its morphology causes a cardiac abnormality, i.e., Ischemic Heart Disease (IHD) [9]. The IHD is the single largest cause of the main contributors to the disease burden in developing countries [10]. The two leading manifestations of IHD are angina and Acute Myocardial Infarction (MI) [10]. Angina is the characteristic caused by atherosclerosis leading to stenosis of one or more coronary arteries. Then, MI occurs due to a lack of oxygen demand in the cardiac muscle tissue. If cardiac muscle activity increases, oxygen demand also increases [11]. MI is the most dangerous form of IHD with the highest mortality rate [10].MI is usually diagnosed by changes in the ECG due to the increase of serum enzymes, such as creatine phosphokinase and troponin T or I [10]. ECG is the most reliable tool for interpreting ...

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“…20 Early revascularization in patients with acute coronary syndrome can also be predicted using such AI-based modelling. 21 One research group developed the structure of neural networks in the AI model such that it could train from the data of electrocardiogram (ECG) of patients and controls fed to the model, and could finally deliver information that selected patients who required urgent revascularization as output. 21 On validation, their AI model had a predictive value (c-statistics) of 0.89.…”

Section: Neural Network-based Artificial Intelligence Models For Predmentioning

confidence: 99%

“…21 One research group developed the structure of neural networks in the AI model such that it could train from the data of electrocardiogram (ECG) of patients and controls fed to the model, and could finally deliver information that selected patients who required urgent revascularization as output. 21 On validation, their AI model had a predictive value (c-statistics) of 0.89. The best accuracy of classification was 0.83, with sensitivity and specificity of 0.79 and 0.87, respectively.…”

Section: Neural Network-based Artificial Intelligence Models For Predmentioning

confidence: 99%

Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management

Mishra

Ashraf

2019

Semin Thromb Hemost

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Thrombosis development in either arterial or venous system remains a major cause of death and disability worldwide. This poorly controlled in vivo clotting could result in many severe complications including myocardial infarction, venous thromboembolism, stroke, and cerebral venous thrombosis, to name a few. These conditions are collectively known as thromboembolic disorders (TEDs). Appropriate understanding of TEDs is challenging, as they are multifactorial and involve several and often different risk factors. Hence, it requires a collective effort and data from numerous research studies to fully comprehend molecular mechanisms for prediction, prevention, treatment, and overall management of these conditions. To accomplish this arduous feat, a comprehensive approach is required that can compile thousands of available experimental data and transform these into more applicable and purposeful findings. Thus, large datasets could be utilized to generate models that could be predictive of how an individual would respond when subjected to any kind of additional risk factors or surgery, hospitalization, etc., or in the presence of some susceptible genetic variations. Artificial intelligence-based methods harness the capabilities of computer software to imitate human behaviors such as language translation, visual perception, and, most importantly, decision making. These emerging tools, if appropriately explored, might assist in processing of large data and tackle the complexities of identifying novel or interesting pathways that could otherwise be hidden due to their enormity. This narrative review attempts to compile the applications of various subfields of artificial intelligence and machine learning in the context of thrombosis research to date. It further reflects on the potential of artificial intelligence in transforming enormous research data into translational application in the form of predictive computational models.

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Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients

Cited by 74 publications

References 17 publications

Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data

Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data

Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier

Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management

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