One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

Cheikhrouhou, Omar; Mahmud, Redowan; Zouari, Ramzi; Ibrahim, Muhammad; Zaguia, Atef; Gia, Tuan Nguyen

doi:10.1109/access.2021.3097751

Cited by 59 publications

(31 citation statements)

References 41 publications

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“…The MIT-BIH ECG data collected under different devices and different environmental conditions can interfere with the real signal to different degrees due to various interference factors, and we call the data in the MIT-BIH database heterogeneous data. To reduce or even avoid the harm caused by noise, the main work in this section is the study of MIT-BIH ECG databases, by perfecting the preprocessing methods so that MIT-BIH data can be cross-used [ 16 ], each MIT-BIH ECG database has its characteristics, such as the number of leads, storage format, and sampling frequency, but the common purpose is to conduct research or perform disease diagnosis. The principle of the formula lies in

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Section: Mit-bih Cardiac Database For Online Automatic Diagnosis Of Cardiac Arrhythmiasmentioning

confidence: 99%

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Yan

Zhang

2021

Journal of Healthcare Engineering

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Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.

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Section: Mit-bih Cardiac Database For Online Automatic Diagnosis Of Cardiac Arrhythmiasmentioning

confidence: 99%

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Yan

Zhang

2021

Journal of Healthcare Engineering

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“…Most IoT applications [ 58 , 59 ] require real-time responses for accurate decision-making. As a future task it is intended to optimize the response time and employ scheduling to provide real-time or near to real-time response for delay-sensitive applications.…”

Section: Discussionmentioning

confidence: 99%

AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing

Nabi

Ahmad

Ibrahim

et al. 2022

Sensors

Self Cite

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Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also an important factor. This paper contributed an adaptive Particle Swarm Optimisation (PSO) based task scheduling approach that reduces the task execution time, and increases throughput and Average Resource Utilization Ratio (ARUR). Moreover, an adaptive inertia weight strategy namely Linearly Descending and Adaptive Inertia Weight (LDAIW) is introduced. The proposed scheduling approach provides a better balance between local and global search leading to an optimized task scheduling. The performance of the proposed approach has been evaluated and compared against five renown PSO based inertia weight strategies concerning makespan and throughput. The experiments are then extended and compared the proposed approach against the other four renowned meta-heuristic scheduling approaches. Analysis of the simulated experimentation reveals that the proposed approach attained up to 10%, 12% and 60% improvement for makespan, throughput and ARUR respectively.

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“…We have selected Fully Convolutional Networks (FCN), LeCun Network (LeNet), Inception Network (IncepNet), Multi Channel Deep Convolutional Neural Network (MCDCNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and our proposed Residual Neural Network (P-ResNet). We have also considered DL algorithms because some of their variants have been successfully applied to solve classification tasks related to intrusion detection [12], [32]. Therefore, we have considered Long Short-Term Memory (LSTM), Neural Network (NN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) algorithms because of their optimal performance.…”

Section: A T E S P H O N E _ S I G N a L T E Mp _ C O N D I T I O Nmentioning

confidence: 99%

Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

Mehedi,

Anwar,

Rahman

et al. 2022

Preprint

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Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning-based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning, and deep transfer learning have progressed and experienced great success in different fields and have emerged as a potential solution for dependable network intrusion detection. However, new and emerging challenges have arisen related to the accuracy, efficiency, scalability, and dependability of the traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep transfer learning-based dependable IDS model that outperforms several existing approaches. The unique contributions include effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data, designing a dependable deep transfer learning-based ResNet model, and evaluating considering real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Extensive analysis and performance evaluation show that the proposed model is robust, more efficient, and has demonstrated better performance, ensuring dependability.

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One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

Cited by 59 publications

References 41 publications

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing

Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

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