ECG Arrhythmia Classification Using Convolutional Neural Network

Thapliyal, Amitabh; Verma, Om Prakash; Kumar, Amioy

doi:10.46338/ijetae0722_19

Cited by 24 publications

(2 citation statements)

References 16 publications

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“…By placing a series of electrodes on body surfaces, such as the chest, arms, and neck, ECGs can be used to track the electrical activity of heart rate rhythms over time. Changes in the rhythm of beats in the heart can be detected with these electrodes (Abdelhafid et al, 2022). ECG signals can be used to consistently diagnose and monitor individuals suffering from a variety of cardiac illnesses and severe cardiovascular syndromes, including arrhythmias.…”

Section: Study Of Arrhythmia Classification Algorithms On Electrocard...mentioning

confidence: 99%

“…Currently, there have been many studies on methods of detecting arrhythmia (Abdelhafid et al, 2022;Li et al, 2022;Madan et al, 2022;Montenegro et al, 2022) but there are many studies using machine learning algorithms for classification (Chickaramanna et al, 2022;Jahan et al, 2022;Mohanty et al, 2021;Ozpolat & Karabatak, 2023;Xie et al, 2019). Research from (Ozpolat & Karabatak, 2023) conducted classification experiments using the Support Vector Machine algorithm is abbreviated as SVM and the Quantum Support Vector Machine algorithm is abbreviated as QSVM.…”

Section: Literature Reviewmentioning

confidence: 99%

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Study of Arrhythmia Classification Algorithms on Electrocardiogram Using Deep Learning

Arifin

Mandala

2023

SinkrOn

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Arrhythmia is a heart disease that occurs due to a disturbance in the heartbeat that causes the heart rhythm to become irregular. In some cases, arrhythmias can be life-threatening if not detected immediately. The method used to detect is electrocardiogram (ECG) signal analysis. To avoid misdiagnosis by cardiologists and to ease the workload, methods are proposed to detect and classify arrhythmias by utilizing Artificial Intelligence (AI). In recent years, there has been a lot of research on the detection of this disease. However, many of such studies are more likely to use machine learning algorithms in the classification process, and most of the accuracy results still do not reach optimal levels in general. Therefore, this study aims to classify arrhythmias using deep learning algorithms. There are several stages of performing arrhythmia detection, namely, preprocessing, feature extraction, and classification. The focus of this research is only on the classification stage, where the Long Short-Term Memory (LSTM) algorithm is proposed. After going through a series of experiments, the performance of the proposed algorithm is further analyzed to compare accuracy, specificity, and sensitivity with other machine learning algorithms based on previous research, with the aim of obtaining an optimal algorithm for arrhythmia detection. Based on the results of the study, the Long Short-Term Memory (LSTM) algorithm managed to outperform the performance of other machine learning algorithms with accuracy, specificity, and sensitivity results of 98.47%, 99.24%, and 97.67%, respectively.

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Section: Study Of Arrhythmia Classification Algorithms On Electrocard...mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Study of Arrhythmia Classification Algorithms on Electrocardiogram Using Deep Learning

Arifin

Mandala

2023

SinkrOn

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An evolutionary algorithmic framework cloud based evidence collection architecture

Rathore

Khan²,

Kumar

et al. 2023

Multimed Tools Appl

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Forensic in cloud computing is an advancement of evolutionary modern forensic science that protects against cyber criminals. Single centralize point compilation and storage of data, however, overcome the authenticity of digital evidence. In order to address this serious issue, this article suggests a evolutionary modern algorithm automated forensic platform leveraging infrastructure as a cloud service (IaaS) based on Blockchain concept. This proposed forensic structural design, evidence collection of evidence and stored on a blockchain which is circulated around several peer blocks. Secure Block Verification Mechanism (SBVM) is proposed to Safeguarding the device from unauthorised users. Using the cuckoo search optimization algorithm for strengthening of the cloud environment, secret keys are optimally generated. On the bases of level of confidentiality, all data is stored and encrypted at cloud authentication server. Confidentiality-based Algebraically Homomorphic Cryptosystems learning is presented with a fast-forwarding algorithm for encryption. A block in the SDN controller is created for every data and information is stored in the cloud service provider and the history is recorded as metadata data about data. A hash based tree is constructed in each block by Secure Hash Algorithm version -3 of 512 bits. By implementing graph theory-based graph neural networks in Smart Contracts, our framework enables users to track their data (GNNSC). Finally, the construction of a evidence graph using blockchain data enables evidence analysis. Experiments was carried out in a Python programming and blockchain integrated cloud environment with network simulator-3.30 (for Software Defined Network). As part of result our newly designed forensic architecture using blochchain (FAuB) good results in terms of evidence response time, insertion times of cloud evidence, verification time of evidence, computational overhead of evidence, hashes calculation time, keys generations times of evidence, evidence encryption time, evidence decryptions time, and total overall change rate of evidence, according to a comprehensive comparative study.

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