Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks

Yeh, Li-Ren; Chen, Wei-Chin; Chan, Hua-Yan; Lu, Nan‐Han; Wang, Chi-Yuan; Twan, Wen‐Hung; Du, Weichang; Huang, Yung-Hui; Hsu, Shih-Yen; Chen, Tai‐Been

doi:10.3390/bios11060188

Cited by 23 publications

(29 citation statements)

References 31 publications

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“…The convolutional neural network is used to classify the images to aid in anesthesia. During the research, three models, ResNet, SqueezeNet, and AlexNet, used and showed the accuracy and waveform as 0.97, 0.75, and 0.96, respectively [ 7 ].…”

Section: Literature Reviewmentioning

confidence: 99%

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches

Rahman

Asif

Sultan

et al. 2022

Computational Intelligence and Neuroscience

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According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect and diagnose heart abnormalities. Data scientists are working on finding the different methods with the required accuracy (Strodthoff et al., 2021). Electrocardiogram (ECG) is the procedure to find the heart condition in the waveform. For ages, the machine learning techniques, which are feature based, played a vital role in the medical sciences and centralized the data in cloud computing and having access throughout the world. Furthermore, deep learning or transfer learning widens the vision and introduces different transfer learning methods to ensure accuracy and time management to detect the ECG in a better way in comparison to the previous and machine learning methods. Hence, it is said that transfer learning has turned world research into more appropriate and innovative research. Here, the proposed comparison and accuracy analysis of different transfer learning methods by using ECG classification for detecting ECG Arrhythmia (CAA-TL). The CAA-TL model has the multiclassification of the ECG dataset, which has been taken from Kaggle. Some of the healthy and unhealthy datasets have been taken in real-time, augmented, and fused with the Kaggle dataset, i.e., Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH dataset). The CAA-TL worked on the accuracy of heart problem detection by using different methods like ResNet50, AlexNet, and SqueezeNet. All three deep learning methods showed remarkable accuracy, which is improved from the previous research. The comparison of different deep learning approaches with respect to layers widens the research and gives the more clarity and accuracy and at the same time finds it time-consuming while working with multiclassification with massive dataset of ECG. The implementation of the proposed method showed an accuracy of 98.8%, 90.08%, and 91% for AlexNet, SqueezeNet, and ResNet50, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches

Rahman

Asif

Sultan

et al. 2022

Computational Intelligence and Neuroscience

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“…A health monitoring system based on deep learning in the IoT context[ 23 ] is also developed to predict CVD. Similarly, Yeh et al [ 24 ] have used deep neural networks to analyze ECG signals to assess the patient's condition and give appropriate drugs. The deep learning approach is also used in the IoT[ 25 ] for valvular heart disease screening.…”

Section: System Development Preliminariesmentioning

confidence: 99%

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

Dami¹

2022

WJCC

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The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).

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“…Based on the World Health Organization (WHO) report, 30–40% of deaths in the entire world are due to cardiovascular diseases which is an alarming situation, and the ratio is increasing with the passage of time. This irregular functioning and abnormalities can be found by cardiologists [ 1 ]. Literature indicates that it is difficult to identify the accurate position and transition of ECG signals with one or a simple algorithm.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning

Asif

Abbas

Khan

et al. 2022

Computational Intelligence and Neuroscience

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With the emergence of the Internet of Things (IoT), investigation of different diseases in healthcare improved, and cloud computing helped to centralize the data and to access patient records throughout the world. In this way, the electrocardiogram (ECG) is used to diagnose heart diseases or abnormalities. The machine learning techniques have been used previously but are feature-based and not as accurate as transfer learning; the proposed development and validation of embedded device prove ECG arrhythmia by using the transfer learning (DVEEA-TL) model. This model is the combination of hardware, software, and two datasets that are augmented and fused and further finds the accuracy results in high proportion as compared to the previous work and research. In the proposed model, a new dataset is made by the combination of the Kaggle dataset and the other, which is made by taking the real-time healthy and unhealthy datasets, and later, the AlexNet transfer learning approach is applied to get a more accurate reading in terms of ECG signals. In this proposed research, the DVEEA-TL model diagnoses the heart abnormality in respect of accuracy during the training and validation stages as 99.9% and 99.8%, respectively, which is the best and more reliable approach as compared to the previous research in this field.

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Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks

Cited by 23 publications

References 31 publications

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning

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