DeepMI: Deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time

Tadesse, Girmaw Abebe; Javed, Hamza; Weldemariam, Komminist; Liu, Yong; Liu, Jin; Chen, Jiyan; Zhu, Tingting

doi:10.1016/j.artmed.2021.102192

Cited by 33 publications

(16 citation statements)

References 35 publications

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“…Tadesse et al [ 24 ] introduced a deep multilead electrocardiogram (ECG) fusion method (DeepMI) for myocardial infarction (MI) detection in healthcare systems. ECG first collects data that are related to MI and produces a feasible set of data for the detection process.…”

Section: Related Workmentioning

confidence: 99%

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

Zhou

2022

BioMed Research International

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The blockage of blood in the vessels results in heart attacks and cardiac arrests which are referred to as myocardial infarction. Early detection of such infarction is feasible through percutaneous coronary intervention (PCI) based on electrocardiogram (ECG) monitoring. The variations in blood flow and clot are precisely observed through periodic ECG monitoring and previous correlations. This article introduces a concentrated value assessment model (CVAM) for determining PCI levels in treating myocardial infarction. The ECG observations from the previous observation sessions are accumulated and organized for validating the infarction rate. This requires the accompanying concentrated data like a heartbeat, blood pressure, and flow rate observed in different sessions. Based on the session observation and normal data correlation, the PCI level is recommended for the patient. In this analysis process, the value shift due to blocks and high and low blood pressure is accounted for through the deep learning paradigm. This paradigm correlates the above factors with the ECG values for precisely determining PCI from the last known concentration. The learning paradigm is trained based on session and normal observation data through different intervals. This model is validated using the metrics precision, analysis rate, diagnosis recommendation, and complexity.

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Section: Related Workmentioning

confidence: 99%

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

Zhou

2022

BioMed Research International

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“…A neural network (NN) is one way to train a computer to mimic the human brain, which enables the computer system to achieve AI through deep learning (DL). In recent years, there has been a surge in research publications where AI has been used in medicine and healthcare [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ,…”

Section: Resultsmentioning

confidence: 99%

“…Due to the high discriminatory power of CNNs, more tasks employ CNNs as their dominant choice. The experiments reported in [ 63 , 93 , 95 ] have had successful ECG signal classification using CNNs. However, controlled hospital settings are required in the existing approaches.…”

Section: Resultsmentioning

confidence: 99%

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Huang

Wang

Ramsey

et al. 2022

Sensors

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Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.

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“…Various automated algorithms for identifying IHD and MI have been advocated because of the in-depth integration of AI in medicine. Tadesse et al (2021 ) proposed an end-to-end algorithm for identifying the time occurrence of MI using a 10 s 12-lead ECG. Their model could classify normal, acute, recent, and old onset cases of MI, with AUROCs of 96.7, 82.9, 68.6, and 73.8%, respectively.…”

Section: Introductionmentioning

confidence: 99%

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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DeepMI: Deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time

Cited by 33 publications

References 35 publications

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

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

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