Article
Biology
Karoliina T. Tapani, Paivi Nevalainen, Sampsa Vanhatalo, Nathan J. Stevenson
Summary: Neonatal seizure detection algorithms (SDA) are approaching human equivalence and provide a clinically relevant interpretation of the EEG.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Quanhong Wang, Weizhuang Kong, Jitao Zhong, Zhengyang Shan, Juan Wang, Xiaowei Li, Hong Peng, Bin Hu
Summary: In this paper, a novel classification algorithm called SVM-KSRC is proposed for automatic epilepsy detection based on electroencephalography (EEG). The algorithm combines support vector machine (SVM) and kernel sparse representation classification (KSRC) to classify EEG signals. Experimental results show that SVM-KSRC outperforms existing machine learning methods in epilepsy detection.
Article
Computer Science, Artificial Intelligence
M. A. Ganaie, Anuradha Kumari, A. K. Malik, M. Tanveer
Summary: This paper discusses the application of support vector machines (SVMs) in diagnosing neurological disorders and the challenges of dealing with noise and outliers in EEG signal classification. The authors propose an improved intuitionistic fuzzy twin support vector machine (IIFTWSVM) to address these challenges and evaluate its performance compared to baseline models.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Biology
Rishabh Bajpai, Rajamanickam Yuvaraj, A. Amalin Prince
Summary: The study introduces an automated system for detecting brain EEG pathology by converting EEG signals into image domain using time-frequency spectrum, utilizing Convolutional Neural Networks and Support Vector Machines for feature extraction and classification. It achieved high accuracy and specificity, providing clinicians with a diagnostic tool for early detection of EEG pathology.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Behavioral Sciences
Sheng Wong, Anj Simmons, Jessica Rivera-Villicana, Scott Barnett, Shobi Sivathamboo, Piero Perucca, Patrick Kwan, Levin Kuhlmann, Rajesh Vasa, Terence J. O'Brien
Summary: Diagnosing and managing seizures is challenging for clinicians, and the adoption of automated seizure detection using machine learning technology is limited. Our survey of medical professionals reveals that the main barriers for usage of seizure detection tools in clinical practice are availability, lack of training, and the blackbox nature of ML algorithms.
EPILEPSY & BEHAVIOR
(2023)
Article
Computer Science, Information Systems
Jian Liu, Yipeng Du, Xiang Wang, Wuguang Yue, Jim Feng
Summary: In this paper, the possibility of using Automated Machine Learning (AutoML) for epilepsy EEG detection is explored. The neural architecture search (NAS) algorithm is applied to design a model for epilepsy EEG analysis, and feature interpretability methods are used to ensure the reliability of the searched model. Experimental results show that the NAS-based model outperforms the baseline model, improving classification accuracy, F1-score, and Cohen's kappa coefficient. Furthermore, the NAS-based model is capable of extracting EEG features related to seizures for classification.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Operations Research & Management Science
M. A. Ganaie, M. Tanveer, Jatin Jangir
Summary: In this study, a novel universum twin support vector machine with pinball loss function (Pin-UTSVM) is proposed for the classification of EEG signals. The Pin-UTSVM model is more robust to noise compared to existing models and performs better in experimental results.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Liming Liu, Maoxiang Chu, Rongfen Gong, Li Zhang
Summary: The improved nonparallel support vector machine (INPSVM) proposed in this article inherits the advantages of nonparallel support vector machine (NPSVM) while also offering incomparable benefits over twin support vector machine (TSVM). INPSVM effectively eliminates noise effects and achieves higher classification accuracy for both linear and nonlinear datasets compared to other algorithms. Experimental results demonstrate the superior efficiency, accuracy, and robustness of INPSVM.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Chen Ding, Tian-Yi Bao, He-Liang Huang
Summary: The study proposes a quantum-inspired classical algorithm for LS-SVM, utilizing an improved sampling technique for classification. The theoretical analysis indicates that the algorithm can achieve classification with logarithmic runtime for low-rank, low-condition number, and high-dimensional data matrices.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
M. Tanveer, A. Tiwari, R. Choudhary, M. A. Ganaie
Summary: This study proposes a novel large scale pinball twin support vector machine (LPTWSVM) to address the limitations of the twin support vector machines (TWSVMs), using a unique pinball loss function and improving model performance by eliminating matrix inversion calculation and minimizing structural risk.
Article
Computer Science, Artificial Intelligence
Matteo Avolio, Antonio Fuduli
Summary: This paper introduces a novel approach for binary multiple instance learning classification, combining the strengths of SVM and PSVM, aiming to discriminate between positive and negative instances by generating a hyperplane placed in the middle between two parallel hyperplanes.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Essam Abdellatef, Heba M. Emara, Mohamed R. Shoaib, Fatma E. Ibrahim, Mohamed Elwekeil, Walid El-Shafai, Taha E. Taha, Adel S. El-Fishawy, El-Sayed M. El-Rabaie, Ibrahim M. Eldokany, Fathi E. Abd El-Samie
Summary: This paper presents two approaches for EEG signal classification: one using machine learning tools and the other using a CNN-based residual learning model. Both approaches are evaluated on the CHB-MIT dataset and show promising results for seizure detection and prediction.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2023)
Article
Computer Science, Information Systems
Mohammad Aslani, Stefan Seipel
Summary: A novel instance selection method called BPLSH is designed to address the high computational complexity of SVMs in the training phase on large datasets. Experimental results show that BPLSH outperforms other methods in terms of classification accuracy, preservation rate, and execution time.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Naihua Xiu, Nai-Yang Deng
Summary: This paper investigates the general forms and characteristics of nonparallel support vector machines (NSVMs) and categorizes them into two types. It reveals the advantages and defects of different types and points out the inconsistency problems. Based on this observation, a novel max-min distance-based NSVM is proposed with desired consistency. The proposed NSVM has the consistency of training and test and the consistency of metric, and it assigns each sample an ascertained loss.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoming Wang, Shitong Wang, Zengxi Huang, Yajun Du
Summary: This paper introduces a novel method called sparse support vector machine guided by radius-margin bound (RMB-SSVM) to efficiently condense the basis vectors in support vector machines. By selecting basis vectors and learning corresponding coefficients with a criterion related to SVM's generalization ability, the RMB-SSVM model can yield better performance.
APPLIED SOFT COMPUTING
(2021)
Article
Clinical Neurology
Soraia Ventura, Sean R. Mathieson, John M. O'Toole, Vicki Livingstone, Mary-Anne Ryan, Geraldine B. Boylan
Summary: The study aimed to characterize sleep features in 4-5 month old infants, providing normative values for sleep macrostructure and sleep spindles. Sex differences were observed in spindle spectral power and brain symmetry index, with males showing lower power and higher symmetry index compared to females. These findings contribute to a better understanding of infant sleep patterns and potential biomarkers for brain maturation.
Editorial Material
Clinical Neurology
Ronit M. Pressler, Geraldine B. Boylan
Editorial Material
Pediatrics
Sampsa Vanhatalo, Nathan J. Stevenson, Ronit M. Pressler, Nicholas S. Abend, Stephane Auvin, Francesco Brigo, M. Roberta Cilio, Cecil D. Hahn, Hans Hartmann, Lena Hellstrom-Westas, Terrie E. Inder, Solomon L. Moshe, Magda L. Nunes, Renee A. Shellhaas, Kollencheri P. Vinayan, Linda S. de Vries, Jo M. Wilmshurst, Elissa Yozawitz, Geraldine B. Boylan
Summary: Brain monitoring plays a crucial role in neonatal neurocritical care, but recent research failed to prove its effectiveness in seizure treatment.
PEDIATRIC RESEARCH
(2023)
Review
Pediatrics
Antoine Giraud, Carol M. Stephens, Geraldine B. Boylan, Brian H. Walsh
Summary: This study aimed to summarise the association between perinatal inflammation exposure and electroencephalography (EEG) features in preterm infants. The results showed that perinatal inflammation exposure was associated with a decrease in amplitude and a reduced incidence of sleep-wake cycling patterns.
PEDIATRIC RESEARCH
(2022)
Editorial Material
Pediatrics
Antoine Giraud, Carol M. Stephens, Geraldine B. Boylan, Brian H. Walsh
PEDIATRIC RESEARCH
(2023)
Article
Clinical Neurology
Andreea M. Pavel, John M. O'Toole, Jacopo Proietti, Vicki Livingstone, Subhabrata Mitra, William P. Marnane, Mikael Finder, Eugene M. Dempsey, Deirdre M. Murray, Geraldine B. Boylan, ANSeR Consortium
Summary: The study aimed to assess whether early clinical and electroencephalography (EEG) features can predict seizures in infants with hypoxic-ischemic encephalopathy (HIE). Machine learning models were developed using clinical and EEG parameters to predict infants at high risk of seizures. The results showed that the combination of clinical and EEG analysis can accurately predict seizure development in HIE infants.
Article
Pediatrics
Mary Anne J. Ryan, Sean R. Mathieson, Vicki Livingstone, Marc Paul O'Sullivan, Eugene M. Dempsey, Geraldine B. Boylan
Summary: This prospective observational study examines the nocturnal sleep architecture of healthy moderate to late preterm infants at 36 weeks post menstrual age. The study finds that sleep state architecture is dependent on birth GA, with infants born at lower GA having less active sleep and more quiet sleep. These findings may have implications for the neurodevelopment of the infants.
PEDIATRIC RESEARCH
(2023)
Review
Pediatrics
Mohamed El-Dib, Nicholas S. J. Abend, Topun Austin, Geraldine Boylan, Valerie Chock, M. Roberta Cilio, Gorm Greisen, Lena Hellstroem-Westas, Petra Lemmers, Adelina Pellicer, Ronit Pressler, Arnold Sansevere, Tammy Tsuchida, Sampsa Vanhatalo, Courtney J. Wusthoff
Summary: The development of neonatal neurocritical care in the past decade has shown significant progress in neuromonitoring and neuroprotection. Commonly used brain monitoring tools in the neonatal intensive care unit (NICU) include amplitude integrated EEG (aEEG), full multichannel continuous EEG (cEEG), and near-infrared spectroscopy (NIRS). However, there is no consensus on the consistent and efficient use of these modalities in common NICU scenarios. This review summarizes current evidence for the best utilization of neuromonitoring modalities in neonates with encephalopathy or possible seizures.
PEDIATRIC RESEARCH
(2023)
Review
Pediatrics
Mohamed El-Dib, Nicholas S. Abend, Topun Austin, Geraldine Boylan, Valerie Chock, M. Roberta Cilio, Gorm Greisen, Lena Hellstrom-Westas, Petra Lemmers, Adelina Pellicer, Ronit M. Pressler, Arnold Sansevere, Eniko Szakmar, Tammy Tsuchida, Sampsa Vanhatalo, Courtney J. Wusthoff
Summary: Neonatal intensive care has shifted focus from cardiorespiratory care to a holistic approach that emphasizes brain health. The most commonly used tools in the neonatal intensive care unit (NICU) to monitor brain function and physiology are amplitude-integrated EEG, full multichannel continuous EEG, and near-infrared spectroscopy. Each of these tools has unique characteristics and functions, but there is no consensus on the optimal approach to neuromonitoring in the NICU. This review examines current evidence to guide the use of these neuromonitoring tools for neuroprotective care in extremely premature infants and critically ill neonates.
PEDIATRIC RESEARCH
(2023)
Review
Public, Environmental & Occupational Health
Sonia Marie Lenehan, Leanna Fogarty, Cathal O'Connor, Sean Mathieson, Geraldine B. Boylan
Summary: This review investigates the development of early childhood sleep and its association with neurodevelopment. Results show changes in sleep architecture during the first two years, with sleep playing a critical role in memory, learning, and socio-emotional development. Future studies should focus on sleep architecture at each month of life, especially during periods of rapid neurodevelopment between 7-24 months.
MATERNAL AND CHILD HEALTH JOURNAL
(2023)
Article
Computer Science, Information Systems
Duc-Minh Ngo, Dominic Lightbody, Andriy Temko, Cuong Pham-Quoc, Ngoc-Thinh Tran, Colin C. C. Murphy, Emanuel Popovici
Summary: This study proposes a hardware-based framework for network intrusion detection using lightweight artificial neural network models. Anomaly-based intrusion detection systems using machine learning have gained popularity due to their ability to detect unseen attacks, but deploying them on IoT devices is computationally expensive. This paper presents a high-performance and ultra-low power consumption framework that achieves high accuracy and faster inference compared to traditional hardware.
Article
Pediatrics
Marc Paul O. Sullivan, Vicki Livingstone, Irina Korotchikova, Eugene M. Dempsey, Deirdre M. Murray, Geraldine B. Boylan
Summary: Unconditional reference centiles for sleep parameters were established in infants 4-16 weeks of age based on data from a randomized controlled trial. The results showed that total sleep duration in a 24-hour period, night-time sleep duration in a 12-hour period, and longest sleep episode duration increased from 4 to 16 weeks, while daytime sleep duration in a 12-hour period decreased.
ARCHIVES OF DISEASE IN CHILDHOOD
(2023)
Article
Clinical Neurology
Soraia Ventura, Sean R. Mathieson, Marc P. O'Sullivan, John M. O'Toole, Vicki Livingstone, Ronit M. Pressler, Eugene M. Dempsey, Deirdre M. Murray, Geraldine B. Boylan
Summary: The study aims to examine the impact of parent-led massage on the sleep electroencephalogram (EEG) features of typically developing term-born infants at 4 months. The results show that parent-led massage is associated with distinct functional brain changes in the sleep EEG at 4 months.
DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY
(2023)
Article
Multidisciplinary Sciences
John M. M. O'Toole, Sean R. R. Mathieson, Sumit A. A. Raurale, Fabio Magarelli, William P. P. Marnane, Gordon Lightbody, Geraldine B. B. Boylan
Summary: This report presents a dataset of neonatal electroencephalogram (EEG) recordings graded based on the severity of abnormalities in the background pattern. The dataset includes 169 hours of multi-channel EEG from 53 neonates diagnosed with hypoxic-ischaemic encephalopathy (HIE). The grading system assesses attributes such as amplitude, continuity, sleep-wake cycling, symmetry and synchrony, and abnormal waveforms to categorize the background severity into 4 grades. The dataset can be used for reference, training, and algorithm development for neonatal EEG with HIE.
Article
Allergy
Cathal O'Connor, Vicki Livingstone, Jonathan O'B Hourihane, Alan D. Irvine, Geraldine Boylan, Deirdre Murray
Summary: This large birth cohort study examined the relationship between emollient bathing at 2 months and the trajectory of atopic dermatitis (AD) in the first 2 years of life. The results showed that infants who had emollient baths at 2 months had a higher prevalence of AD, suggesting that early use of emollients may increase the risk of AD.
PEDIATRIC ALLERGY AND IMMUNOLOGY
(2023)
Article
Clinical Neurology
Jaakko Vallinoja, Timo Nurmi, Julia Jaatela, Vincent Wens, Mathieu Bourguignon, Helena Maenpaa, Harri Piitulainen
Summary: The study aimed to assess the effects of lesions related to spastic diplegic cerebral palsy on functional connectivity. Using multiple imaging modalities, the researchers found enhanced functional connectivity in the sensorimotor network of individuals with spastic diplegic cerebral palsy, which was not correlated with hand coordination performance.
CLINICAL NEUROPHYSIOLOGY
(2024)
Article
Clinical Neurology
Francesca Ginatempo, Nicola Loi, John C. Rothwell, Franca Deriu
Summary: This study comprehensively investigated sensorimotor integration in the cranial-cervical muscles of healthy adults and found that the integration of sensory inputs with motor output is profoundly influenced by the type of sensory afferent involved and the functional role played by the target muscle.
CLINICAL NEUROPHYSIOLOGY
(2024)