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Electroencephalography (EEG) is an important clinical tool for reviewing sleep-wake cycling in neonates in intensive care. Trace alternant (TA)-a characteristic pattern of EEG activity during quiet sleep in term neonates-is defined by…

Signal Processing · Electrical Eng. & Systems 2020-05-13 Sumit A. Raurale , Geraldine B. Boylan , Gordon Lightbody , John M. O'Toole

Objective Accurate identification of hypoxic-ischemic brain injury in the early neonatal period is essential for initiating therapeutic hypothermia (TH) within 6 hours of birth to optimize neurodevelopmental outcomes. We aimed to develop a…

Neurons and Cognition · Quantitative Biology 2025-11-19 Marc Fiammante , Anne-Isabelle Vermersch , Marie Vidailhet , Mario Chavez

Electroencephalography (EEG) is a valuable clinical tool for grading injury caused by lack of blood and oxygen to the brain during birth. This study presents a novel end-to-end architecture, using a deep convolutional neural network, that…

Signal Processing · Electrical Eng. & Systems 2020-05-13 Sumit A. Raurale , Geraldine B. Boylan , Gordon Lightbody , John M. O'Toole

This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a…

Electroencephalography (EEG) is an important clinical tool for grading injury caused by lack of oxygen or blood to the brain during birth. Characteristics of low-voltage waveforms, known as inter-bursts, are related to different grades of…

Signal Processing · Electrical Eng. & Systems 2019-07-08 Sumit A. Raurale , Saif Nalband , Geraldine B. Boylan , Gordon Lightbody , John M. O'Toole

Over the past few decades, electroencephalography (EEG) monitoring has become a pivotal tool for diagnosing neurological disorders, particularly for detecting seizures. Epilepsy, one of the most prevalent neurological diseases worldwide,…

Machine Learning · Computer Science 2025-08-08 Andrea Pollastro , Francesco Isgrò , Roberto Prevete

This paper presents the HRVConformer, a novel deep learning architecture for the classification of hypoxic-ischemic encephalopathy (HIE) using the instantaneous heart rate (HR) signal. Unlike conventional approaches that rely on handcrafted…

Machine Learning · Computer Science 2026-05-27 Shuwen Yu , William P Marnane , Geraldine B. Boylan , Gordon Lightbody

Learning transferable representations for electroencephalography (EEG) remains challenging because EEG signals are inherently multi-channel and non-stationary. Channels observed at the same time provide coupled measurements of neural…

Machine Learning · Computer Science 2026-05-13 Fan Ma , Qier An , Peng Chen , Lingfei Qian , Xiang Lan , Mingyang Jiang , Zhiling Gu , Xenophon Papademetris , Hua Xu

Continuous electroencephalography (EEG) is routinely used in neurocritical care to monitor seizures and other harmful brain activity, including rhythmic and periodic patterns that are clinically significant. Although deep learning methods…

Human-Computer Interaction · Computer Science 2026-01-05 Argha Kamal Samanta , Deepak Mewada , Monalisa Sarma , Debasis Samanta

Electrical Impedance Tomography (EIT) is an emerging non-invasive medical imaging modality. It is based on feeding electrical currents into the patient, measuring the resulting voltages at the skin, and recovering the internal conductivity…

Image and Video Processing · Electrical Eng. & Systems 2020-11-06 Juan Pablo Agnelli , Aynur Çöl , Matti Lassas , Rashmi Murthy , Matteo Santacesaria , Samuli Siltanen

The amount of power in different frequency bands of the electroencephalogram (EEG) carries information about the behavioral state of a subject. Hence, neurologists treating epileptic patients monitor the temporal evolution of the different…

Neurons and Cognition · Quantitative Biology 2020-06-17 Melisa Maidana Capitán , Nuria Cámpora , Claudio Sebastián , Sigvard Silvia Kochen , Inés Samengo

An Electroencephalogram (EEG) is a non-invasive exam that records the brain's electrical activity. This is used to help diagnose conditions such as different brain problems. EEG signals are taken for epilepsy detection, and with Discrete…

Machine Learning · Computer Science 2024-05-28 Rabel Guharoy , Nanda Dulal Jana , Suparna Biswas , Lalit Garg

The classification of harmful brain activities, such as seizures and periodic discharges, play a vital role in neurocritical care, enabling timely diagnosis and intervention. Electroencephalography (EEG) provides a non-invasive method for…

Machine Learning · Computer Science 2025-10-21 Shivraj Singh Bhatti , Aryan Yadav , Mitali Monga , Neeraj Kumar

Electroencephalography (EEG) is a method to record the electrical signals in the brain. Recognizing the EEG patterns in the sleeping brain gives insights into the understanding of sleeping disorders. The dataset under consideration contains…

Machine Learning · Statistics 2018-04-25 Aditya Chindhade , Abhijeet Alshi , Aakash Bhatia , Kedar Dabhadkar , Pranav Sivadas Menon

Electroencephalography (EEG) signals have been promising for long-term braking intensity prediction but are prone to various artifacts that limit their reliability. Here, we propose a novel framework that models EEG signals as mixtures of…

Human-Computer Interaction · Computer Science 2026-04-21 Zikun Zhou , Wenshuo Wang , Wenzhuo Liu , Hui Yao , Chaopeng Zhang , Yichen Liu , Xiaonan Yang , Junqiang Xi

Pain remains one of the most pressing health challenges, yet its measurement still relies heavily on self-report, limiting monitoring in non-communicative patients and hindering translational research. Neural oscillations recorded with…

Neurons and Cognition · Quantitative Biology 2025-09-16 D. A. Blanco-Mora , A. Dierolf , J. Gonçalves , M. van Der Meulen

Epilepsy is one of the most common neurological diseases, characterized by transient and unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary method used to perform both the diagnosis and the monitoring of…

Electroencephalography (EEG) plays a crucial role in the diagnosis of various neurological disorders. However, small hospitals and clinics often lack advanced EEG signal analysis systems and are prone to misinterpretation in manual EEG…

Artificial Intelligence · Computer Science 2024-11-18 Chin-Sung Tung , Sheng-Fu Liang , Shu-Feng Chang , Chung-Ping Young

Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited application due to inter-rater variability, resource constraints, and poor…

Objective: Young children and infants, especially newborns, are highly susceptible to seizures, which, if undetected and untreated, can lead to severe long-term neurological consequences. Early detection typically requires continuous…

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