A Dynamic Systems Approach for Detecting and Localizing of Infarct-Related Artery in Acute Myocardial Infarction Using Compressed Paper-Based Electrocardiogram (ECG)

Le, Trung Q.; Chandra, Vibhuthi; Afrin, Kahkashan; Srivatsa, Sanjay S.; Bukkapatnam, Satish

doi:10.3390/s20143975

Cited by 9 publications

(7 citation statements)

References 32 publications

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“…Furthermore, the fibers have been utilized to fabricate the smart wearable multisensing textile-based system for real-time prediction of disease onsets. The system is an extension of our previous reported projects [44][45][46]. In this paper, we demonstrate that the polyester ECFs may be used for electrical stimulation of neural tissue in an in vivo rodent model.…”

Section: Introductionmentioning

confidence: 76%

Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation

Richter

Mace

Hays

et al. 2021

Sensors

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Advancements in electrode technologies to both stimulate and record the central nervous system’s electrical activities are enabling significant improvements in both the understanding and treatment of different neurological diseases. However, the current neural recording and stimulating electrodes are metallic, requiring invasive and damaging methods to interface with neural tissue. These electrodes may also degrade, resulting in additional invasive procedures. Furthermore, metal electrodes may cause nerve damage due to their inherent rigidity. This paper demonstrates that novel electrically conductive organic fibers (ECFs) can be used for direct nerve stimulation. The ECFs were prepared using a standard polyester material as the structural base, with a carbon nanotube ink applied to the surface as the electrical conductor. We report on three experiments: the first one to characterize the conductive properties of the ECFs; the second one to investigate the fiber cytotoxic properties in vitro; and the third one to demonstrate the utility of the ECF for direct nerve stimulation in an in vivo rodent model.

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Section: Introductionmentioning

confidence: 76%

Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation

Richter

Mace

Hays

et al. 2021

Sensors

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“…Furthermore, many studies are presented to classify cardiac arrhythmias using multi-lead ECG [42,81]. Arrhythmias may not be observed in all ECG channels and may be dominant only in some ECG channels.…”

Section: Resultsmentioning

confidence: 99%

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning

Özdemir

Güren

2021

Preprint

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Background: Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis.Methods: A novel method is proposed to automatically diagnose COVID-19 by using Electrocardiogram (ECG) data with deep learning for the first time. Moreover, a new and effective method called hexaxial feature mapping is proposed to represent 12-lead ECG to 2D colorful images. Gray-Level Co-Occurrence Matrix (GLCM) method is used to extract features and generate hexaxial mapping images. These generated images are then fed into a new Convolutional Neural Network (CNN) architecture to diagnose COVID-19.Results: Two different classification scenarios are conducted on a publicly available paper-based ECG image dataset to reveal the diagnostic capability and performance of the proposed approach. In the first scenario, ECG data labeled as COVID-19 and No-Findings (normal) are classified to evaluate COVID-19 classification ability. According to results, the proposed approach provides encouraging COVID-19 detection performance with an accuracy of 96.20% and F1-Score of 96.30%. In the second scenario, ECG data labeled as Negative (normal, abnormal, and myocardial infarction) and Positive (COVID-19) are classified to evaluate COVID-19 diagnostic ability. The experimental results demonstrated that the proposed approach provides satisfactory COVID-19 prediction performance with an accuracy of 93.00% and F1-Score of 93.20%. Furthermore, different experimental studies are conducted to evaluate the robustness of the proposed approach.Conclusion: Automatic detection of cardiovascular changes caused by COVID-19 can be possible with a deep learning framework through ECG data. This not only proves the presence of cardiovascular changes caused by COVID-19 but also reveals that ECG can potentially be used in the diagnosis of COVID-19. We believe the proposed study may provide a crucial decision-making system for healthcare professionals.Source Code: All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-

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“…Furthermore, several research studies using multi-lead ECG to classify cardiac arrhythmias are described [ 56 , 57 ]. Arrhythmias may not be present in all ECG channels, or they may be prevalent in only a few.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, numerous deep learning-based studies have employed radiographic images to detect COVID-19, with many of them achieving excellent classification results. As an example, consider the following studies: Al-Waisy et al [46] achieved 99.99% accuracy, Dhiman Furthermore, several research studies using multi-lead ECG to classify cardiac arrhythmias are described [56,57]. Arrhythmias may not be present in all ECG channels, or they may be prevalent in only a few.…”

Section: Discussionmentioning

confidence: 99%

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

Rahman

Akinbi

Chowdhury

et al. 2022

Health Inf Sci Syst

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The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities.

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A Dynamic Systems Approach for Detecting and Localizing of Infarct-Related Artery in Acute Myocardial Infarction Using Compressed Paper-Based Electrocardiogram (ECG)

Cited by 9 publications

References 32 publications

Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation

Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

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