Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study

Boashash, Boualem; Ouelha, Samir

doi:10.1016/j.knosys.2016.05.027

Cited by 113 publications

(81 citation statements)

References 40 publications

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“…The traditional classification of sleep stages is the classification criteria developed by experts according to Rechtschaffen and Kales (R & K) [2] . This study used the six-state sleep stages of the R & K standard: Awake (Awa), Stage 1 (S1), Stage 2 (S2), Stage 3 (S3), Stage 4 (S4) and REM [3] . The five-state staging phase combines S3 and S4 into one state.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection for sleep EEG signal with Mahalanobis-Taguchi-Gram-Schmidt method

Peng¹,

Zheng²,

Liu³

et al. 2018

Proceedings of the 2018 7th International Conference on Energy, Environment and Sustainable Development (ICEESD 2018)

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Abstract：Considering the tedious steps, the poor accuracy and over-subjectivity of human sleep quality judgment artificially, this paper presents an automatic detection algorithm of sleep quality based on Mahalanobis-Taguchi system method. Based on the modeling and analysis of the human brain dual channel EEG signals, the normalized vector of each channel is obtained under different sleep stages. At the same time, the linear independent vector group is subjected to Gram-Schmidt orthogonalization, and the mean value of the signal-to-noise ratio of each sleep stage is calculated by using the Mahalanobis-Taguchi-Gram-Schmidt method. Through analyzing the waveform of the mean signal-to-noise ratio on different sleep stages, the normal and the abnormal sleep quality can be identified.

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Section: Introductionmentioning

confidence: 99%

Anomaly detection for sleep EEG signal with Mahalanobis-Taguchi-Gram-Schmidt method

Peng¹,

Zheng²,

Liu³

et al. 2018

Proceedings of the 2018 7th International Conference on Energy, Environment and Sustainable Development (ICEESD 2018)

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show abstract

“…Artifacts are unwanted signals produced by the random movement of the subjects, electrode contact, electrocardiogram, respiration, eye blinking and electrical interference in the NICUs [29]. These different types of artifacts are generalised and defined as an 'artifact' class in this project (see Figure 1.…”

Section: Artifactsmentioning

confidence: 99%

“…Much research focuses on specific patterns: for example, neonatal seizure detection and localisation [29,[35][36][37], and detection of the sleep-wake cycle [38]. Classification of a broader variety of EEG background patterns will increase the system complexity and the need for specific EEG data, especially in long-term multichannel EEG recordings.…”

Section: Significance and Motivationmentioning

confidence: 99%

“…The benefits of EEG are that it can easily be implemented at the cotside soon after birth and it provides a realtime measure of cerebral function [12]. Detecting seizures [29,45,46] and assessing response to anticonvulsants [47] …”

Section: Thesis Organisationmentioning

confidence: 99%

“…This is time consuming and not feasible in certain applications -for example, in automatic pattern recognition and classification [29].…”

Section: What Is Already Known On This Topic?mentioning

confidence: 99%

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Design and optimisation of time-frequency analysis for multichannel neonatal EEG background features in term neonates with hypoxic ischaemic encephalopathy: characterisation, classification and neurodevelopmental outcome prediction

Awal¹

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Neonatal Hypoxic Ischaemic Encephalopathy (HIE) is a major cause of morbidity and mortality in newborns throughout the world with an incidence of 1-8/1000 newborns in developed countries and up to 26/1000 newborns in developing countries. It causes one million neonatal deaths globally per year. Electroencephalography (EEG) is a useful method for assessment of brain activity in newborns with HIE. EEG performs well in the early diagnosis and classification of HIE severity and in predicting neurodevelopmental outcome. A normal EEG is highly predictive of a normal outcome and various abnormal EEG features are associated with neurological abnormalities or death.A systematic review and meta-analysis of the literature was undertaken to determine which specific background features of the EEG best predict outcome (Aim 1). Automatic detection and classification

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Detection of Epileptic Seizures in EEG—Inspired by Machine Learning Techniques

Mahantesh

2020

Intelligent Computing and Communication

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Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study

Cited by 113 publications

References 40 publications

Anomaly detection for sleep EEG signal with Mahalanobis-Taguchi-Gram-Schmidt method

Anomaly detection for sleep EEG signal with Mahalanobis-Taguchi-Gram-Schmidt method

Design and optimisation of time-frequency analysis for multichannel neonatal EEG background features in term neonates with hypoxic ischaemic encephalopathy: characterisation, classification and neurodevelopmental outcome prediction

Detection of Epileptic Seizures in EEG—Inspired by Machine Learning Techniques

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