Cargando…

Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment

In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of h...

Descripción completa

Detalles Bibliográficos
Autores principales: Almasoud, Ahmed S., Mengash, Hanan Abdullah, Eltahir, Majdy M., Almalki, Nabil Sharaf, Alnfiai, Mrim M., Salama, Ahmed S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575382/
https://www.ncbi.nlm.nih.gov/pubmed/37837102
http://dx.doi.org/10.3390/s23198265
_version_ 1785120909158252544
author Almasoud, Ahmed S.
Mengash, Hanan Abdullah
Eltahir, Majdy M.
Almalki, Nabil Sharaf
Alnfiai, Mrim M.
Salama, Ahmed S.
author_facet Almasoud, Ahmed S.
Mengash, Hanan Abdullah
Eltahir, Majdy M.
Almalki, Nabil Sharaf
Alnfiai, Mrim M.
Salama, Ahmed S.
author_sort Almasoud, Ahmed S.
collection PubMed
description In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of heart conditions that lead to heart ailments and the identification of heart diseases. The deployment of IoT devices for arrhythmia classification offers many benefits such as remote patient care, continuous monitoring, and early recognition of abnormal heart rhythms. However, it is challenging to diagnose and manually classify arrhythmia as the manual diagnosis of ECG signals is a time-consuming process. Therefore, the current article presents the automated arrhythmia classification using the Farmland Fertility Algorithm with Hybrid Deep Learning (AAC-FFAHDL) approach in the IoT platform. The proposed AAC-FFAHDL system exploits the hyperparameter-tuned DL model for ECG signal analysis, thereby diagnosing arrhythmia. In order to accomplish this, the AAC-FFAHDL technique initially performs data pre-processing to scale the input signals into a uniform format. Further, the AAC-FFAHDL technique uses the HDL approach for detection and classification of arrhythmia. In order to improve the classification and detection performance of the HDL approach, the AAC-FFAHDL technique involves an FFA-based hyperparameter tuning process. The proposed AAC-FFAHDL approach was validated through simulation using the benchmark ECG database. The comparative experimental analysis outcomes confirmed that the AAC-FFAHDL system achieves promising performance compared with other models under different evaluation measures.
format Online
Article
Text
id pubmed-10575382
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-105753822023-10-14 Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment Almasoud, Ahmed S. Mengash, Hanan Abdullah Eltahir, Majdy M. Almalki, Nabil Sharaf Alnfiai, Mrim M. Salama, Ahmed S. Sensors (Basel) Article In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of heart conditions that lead to heart ailments and the identification of heart diseases. The deployment of IoT devices for arrhythmia classification offers many benefits such as remote patient care, continuous monitoring, and early recognition of abnormal heart rhythms. However, it is challenging to diagnose and manually classify arrhythmia as the manual diagnosis of ECG signals is a time-consuming process. Therefore, the current article presents the automated arrhythmia classification using the Farmland Fertility Algorithm with Hybrid Deep Learning (AAC-FFAHDL) approach in the IoT platform. The proposed AAC-FFAHDL system exploits the hyperparameter-tuned DL model for ECG signal analysis, thereby diagnosing arrhythmia. In order to accomplish this, the AAC-FFAHDL technique initially performs data pre-processing to scale the input signals into a uniform format. Further, the AAC-FFAHDL technique uses the HDL approach for detection and classification of arrhythmia. In order to improve the classification and detection performance of the HDL approach, the AAC-FFAHDL technique involves an FFA-based hyperparameter tuning process. The proposed AAC-FFAHDL approach was validated through simulation using the benchmark ECG database. The comparative experimental analysis outcomes confirmed that the AAC-FFAHDL system achieves promising performance compared with other models under different evaluation measures. MDPI 2023-10-06 /pmc/articles/PMC10575382/ /pubmed/37837102 http://dx.doi.org/10.3390/s23198265 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Almasoud, Ahmed S.
Mengash, Hanan Abdullah
Eltahir, Majdy M.
Almalki, Nabil Sharaf
Alnfiai, Mrim M.
Salama, Ahmed S.
Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment
title Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment
title_full Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment
title_fullStr Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment
title_full_unstemmed Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment
title_short Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment
title_sort automated arrhythmia classification using farmland fertility algorithm with hybrid deep learning model on internet of things environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575382/
https://www.ncbi.nlm.nih.gov/pubmed/37837102
http://dx.doi.org/10.3390/s23198265
work_keys_str_mv AT almasoudahmeds automatedarrhythmiaclassificationusingfarmlandfertilityalgorithmwithhybriddeeplearningmodeloninternetofthingsenvironment
AT mengashhananabdullah automatedarrhythmiaclassificationusingfarmlandfertilityalgorithmwithhybriddeeplearningmodeloninternetofthingsenvironment
AT eltahirmajdym automatedarrhythmiaclassificationusingfarmlandfertilityalgorithmwithhybriddeeplearningmodeloninternetofthingsenvironment
AT almalkinabilsharaf automatedarrhythmiaclassificationusingfarmlandfertilityalgorithmwithhybriddeeplearningmodeloninternetofthingsenvironment
AT alnfiaimrimm automatedarrhythmiaclassificationusingfarmlandfertilityalgorithmwithhybriddeeplearningmodeloninternetofthingsenvironment
AT salamaahmeds automatedarrhythmiaclassificationusingfarmlandfertilityalgorithmwithhybriddeeplearningmodeloninternetofthingsenvironment