A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model

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dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorTiwari, Shamik
dc.contributor.authorJain, Anurag
dc.contributor.authorSapra, Varun
dc.contributor.authorKoundal, Deepika
dc.contributor.authorAlenezi, Fayad
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorNour, Majid
dc.date.accessioned2023-08-11T08:47:28Z
dc.date.available2023-08-11T08:47:28Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis research study is funded by the UPES-SEED scheme, with project ID UPES/R&D/300119/13 titled 'Smart EKG monitoring system to diagnose cardiovascular disease' for 2019-20 from the University of Petroleum and Energy Studies, Dehradun, INDIA.en_US
dc.description.abstractAutomatic screening approaches can help diagnose Cardiovascular Disease (CVD) early, which is the leading source of mortality worldwide. Electrocardiogram (ECG/EKG)-based methods are frequently utilized to detect CVDs since they are a reliable and non-invasive tool. Due to this, Smart Cardiovascular Disease Detection System (SCDDS) has been offered in this manuscript to detect heart disease in advance. A wearable device embedded with electrodes and Internet of Things (IoT) sensors is utilized to record the EKG signals. Bluetooth is used to send EKG signals to the smartphone. The smartphone transfers the signals through an Android app to a pre-trained deep learning-based architecture deployed on the cloud. The architecture analyzes the EKG signal, and a heart report is communicated to the patient and advises further preventive action. We offered an ensembled Convolution Neural Network architecture (ConvNet) and Convolution Neural Network architecture - Long Short-Term Memory Networks (ConvNet-LSTM) architecture to detect atrial fibrillation heartbeats automatically. The architecture utilizes a convolutional neural network and long short-term memory network to extract local correlation features and capture the front-to-back dependencies of EKG sequence data. MIT-BIH atrial fibrillation database was utilized to design the architecture and achieved an overall categorization accuracy of 98% for the test set's heartbeat data. The findings of this work show that the suggested system has achieved significant accuracy with the ensembling of models. Such models can be deployed in wearable devices and smartphones for continuous monitoring and reporting of the heart.en_US
dc.description.sponsorshipUniversity of Petroleum and Energy Studies, Dehradun, INDIA [UPES/RD/300119/13]en_US
dc.identifier.citationTiwari, S., Jain, A., Sapra, V., Koundal, D., Alenezi, F., Polat, K., ... & Nour, M. (2023). A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model. Expert Systems with Applications, 213, 118933.en_US
dc.identifier.doi10.1016/j.eswa.2022.118933
dc.identifier.endpage13en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issuePart Aen_US
dc.identifier.scopus2-s2.0-85139049728en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2022.118933
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11493
dc.identifier.volume213en_US
dc.identifier.wosWOS:000867490400007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArrhythmiaen_US
dc.subjectCardiovascular Diseasesen_US
dc.subjectConvolution Neural Network Architectureen_US
dc.subjectElectrocardiogramen_US
dc.subjectInternet of Thingsen_US
dc.subjectLong Short-Term Memory Networksen_US
dc.titleA smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM modelen_US
dc.typeArticleen_US

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