An adaptive neuro-fuzzy inference system for sleep spindle detection

Liang, Sheng-Fu; Kuo, Chih‐En; Hu, Yuhan; Chen, Chun-Yu; Li, Yu-Hung

doi:10.1109/ifuzzy.2012.6409733

Cited by 5 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and the Combined form (children with both Inattentive and Hyperactive–Impulsive symptoms). While some relevant studies have been published in conferences ( Ghiassian et al, 2013 , Mahanand et al, 2013 , Liang et al, 2012 , Zhu et al, 2005 , Wang et al, 2011 ), Table 2 is provided to summarize the multivariate analyses applied to ADHD presented in the literature in journal form. Due to the inherent heterogeneity in these datasets it is challenging to compare the results between studies.…”

Section: Resultsmentioning

confidence: 99%

Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders

Levman

Takahashi

2015

NeuroImage: Clinical

View full text Add to dashboard Cite

Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us.

show abstract

Section: Resultsmentioning

confidence: 99%

Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders

Levman

Takahashi

2015

NeuroImage: Clinical

View full text Add to dashboard Cite

show abstract

“…In order to improve the efficiency of sleep spindle detection, several automatic methods had been developed including signal processing by time-frequency methods and decision making by thresholds or machine learning techniques. Time-frequency methods include Teager Energy Operator (TEO) [7], Continues Wavelet Transform (CWT) [8], and Matching Pursuit (MP) [9]. It is easy to obtain the energy of signal within a certain frequency band, but the result of TEO was sensitive to noise [10].…”

Section: Introductionmentioning

confidence: 99%

Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

Li¹,

Wang²,

Sugi³

et al. 2017

JBiSE

View full text Add to dashboard Cite

Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram (PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

show abstract

RETRACTED ARTICLE: Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods

Yücelbaş

Özşen

et al. 2016

Neural Comput & Applic

View full text Add to dashboard Cite

An adaptive neuro-fuzzy inference system for sleep spindle detection

Cited by 5 publications

References 11 publications

Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders

Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders

Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

RETRACTED ARTICLE: Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods

Contact Info

Product

Resources

About