Cargando…
A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates
Electroencephalography (EEG) is increasingly being used in pediatric neurology and provides opportunities to diagnose various brain illnesses more accurately and precisely. It is thought to be one of the most effective tools for identifying newborn seizures, especially in Neonatal Intensive Care Uni...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458382/ https://www.ncbi.nlm.nih.gov/pubmed/37631573 http://dx.doi.org/10.3390/s23167037 |
_version_ | 1785097152276463616 |
---|---|
author | Mumenin, Khondoker Mirazul Biswas, Prapti Khan, Md. Al-Masrur Alammary, Ali Saleh Nahid, Abdullah-Al |
author_facet | Mumenin, Khondoker Mirazul Biswas, Prapti Khan, Md. Al-Masrur Alammary, Ali Saleh Nahid, Abdullah-Al |
author_sort | Mumenin, Khondoker Mirazul |
collection | PubMed |
description | Electroencephalography (EEG) is increasingly being used in pediatric neurology and provides opportunities to diagnose various brain illnesses more accurately and precisely. It is thought to be one of the most effective tools for identifying newborn seizures, especially in Neonatal Intensive Care Units (NICUs). However, EEG interpretation is time-consuming and requires specialists with extensive training. It can be challenging and time-consuming to distinguish between seizures since they might have a wide range of clinical characteristics and etiologies. Technological advancements such as the Machine Learning (ML) approach for the rapid and automated diagnosis of newborn seizures have increased in recent years. This work proposes a novel optimized ML framework to eradicate the constraints of conventional seizure detection techniques. Moreover, we modified a novel meta-heuristic optimization algorithm (MHOA), named Aquila Optimization (AO), to develop an optimized model to make our proposed framework more efficient and robust. To conduct a comparison-based study, we also examined the performance of our optimized model with that of other classifiers, including the Decision Tree (DT), Random Forest (RF), and Gradient Boosting Classifier (GBC). This framework was validated on a public dataset of Helsinki University Hospital, where EEG signals were collected from 79 neonates. Our proposed model acquired encouraging results showing a 93.38% Accuracy Score, 93.9% Area Under the Curve (AUC), 92.72% F1 score, 65.17% Kappa, 93.38% sensitivity, and 77.52% specificity. Thus, it outperforms most of the present shallow ML architectures by showing improvements in accuracy and AUC scores. We believe that these results indicate a major advance in the detection of newborn seizures, which will benefit the medical community by increasing the reliability of the detection process. |
format | Online Article Text |
id | pubmed-10458382 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104583822023-08-27 A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates Mumenin, Khondoker Mirazul Biswas, Prapti Khan, Md. Al-Masrur Alammary, Ali Saleh Nahid, Abdullah-Al Sensors (Basel) Article Electroencephalography (EEG) is increasingly being used in pediatric neurology and provides opportunities to diagnose various brain illnesses more accurately and precisely. It is thought to be one of the most effective tools for identifying newborn seizures, especially in Neonatal Intensive Care Units (NICUs). However, EEG interpretation is time-consuming and requires specialists with extensive training. It can be challenging and time-consuming to distinguish between seizures since they might have a wide range of clinical characteristics and etiologies. Technological advancements such as the Machine Learning (ML) approach for the rapid and automated diagnosis of newborn seizures have increased in recent years. This work proposes a novel optimized ML framework to eradicate the constraints of conventional seizure detection techniques. Moreover, we modified a novel meta-heuristic optimization algorithm (MHOA), named Aquila Optimization (AO), to develop an optimized model to make our proposed framework more efficient and robust. To conduct a comparison-based study, we also examined the performance of our optimized model with that of other classifiers, including the Decision Tree (DT), Random Forest (RF), and Gradient Boosting Classifier (GBC). This framework was validated on a public dataset of Helsinki University Hospital, where EEG signals were collected from 79 neonates. Our proposed model acquired encouraging results showing a 93.38% Accuracy Score, 93.9% Area Under the Curve (AUC), 92.72% F1 score, 65.17% Kappa, 93.38% sensitivity, and 77.52% specificity. Thus, it outperforms most of the present shallow ML architectures by showing improvements in accuracy and AUC scores. We believe that these results indicate a major advance in the detection of newborn seizures, which will benefit the medical community by increasing the reliability of the detection process. MDPI 2023-08-09 /pmc/articles/PMC10458382/ /pubmed/37631573 http://dx.doi.org/10.3390/s23167037 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 Mumenin, Khondoker Mirazul Biswas, Prapti Khan, Md. Al-Masrur Alammary, Ali Saleh Nahid, Abdullah-Al A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates |
title | A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates |
title_full | A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates |
title_fullStr | A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates |
title_full_unstemmed | A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates |
title_short | A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates |
title_sort | modified aquila-based optimized xgboost framework for detecting probable seizure status in neonates |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458382/ https://www.ncbi.nlm.nih.gov/pubmed/37631573 http://dx.doi.org/10.3390/s23167037 |
work_keys_str_mv | AT mumeninkhondokermirazul amodifiedaquilabasedoptimizedxgboostframeworkfordetectingprobableseizurestatusinneonates AT biswasprapti amodifiedaquilabasedoptimizedxgboostframeworkfordetectingprobableseizurestatusinneonates AT khanmdalmasrur amodifiedaquilabasedoptimizedxgboostframeworkfordetectingprobableseizurestatusinneonates AT alammaryalisaleh amodifiedaquilabasedoptimizedxgboostframeworkfordetectingprobableseizurestatusinneonates AT nahidabdullahal amodifiedaquilabasedoptimizedxgboostframeworkfordetectingprobableseizurestatusinneonates AT mumeninkhondokermirazul modifiedaquilabasedoptimizedxgboostframeworkfordetectingprobableseizurestatusinneonates AT biswasprapti modifiedaquilabasedoptimizedxgboostframeworkfordetectingprobableseizurestatusinneonates AT khanmdalmasrur modifiedaquilabasedoptimizedxgboostframeworkfordetectingprobableseizurestatusinneonates AT alammaryalisaleh modifiedaquilabasedoptimizedxgboostframeworkfordetectingprobableseizurestatusinneonates AT nahidabdullahal modifiedaquilabasedoptimizedxgboostframeworkfordetectingprobableseizurestatusinneonates |