A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

doi:10.1016/j.jneumeth.2016.07.012

Cited by 218 publications

(116 citation statements)

References 42 publications

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“…The analysis of the studies on automatic sleep scoring reveals that the number of these studies is increasing in recent years (Hassan & Bhuiyan, 2015, 2016a, 2016b, 2016c, 2017; Hassan, Bashar & Bhuiyan, 2015a, 2015b; Hassan & Subasi, 2017). Moreover, the comparison of previous methods of sleep scoring with the introduced method in the present study showed some interesting points.…”

Section: Discussionmentioning

confidence: 99%

“…Many examples can be mentioned here regarding the application of intelligent techniques in medical diagnostic automation (Hassan & Haque, 2015a, 2016a, 2016b; Bashar, Hassan & Bhuiyan, 2015a; Hassan, 2015a, 2015b, 2016) and EEG analysis (Hassan & Haque, 2015b, 2015c, 2016a, 2017; Bashar, Hassan & Bhuiyan, 2015b; Hassan, Siuly & Zhang, 2016; Hassan & Subasi, 2016, 2017; Hassan & Bhuiyan, 2015, 2016a, 2016b, 2016c, 2017; Hassan, Bashar & Bhuiyan, 2015a, 2015b). In 2011, Kravoska et al achieved 81% accuracy in sleep scoring using various features derived from PSG signals.…”

Section: Introductionmentioning

confidence: 99%

“…In 2016, Hassan et al worked on a single EEG for sleep scoring using normal inverse Gaussian parameters and achieved 90.01% accuracy (Hassan & Bhuiyan, 2017). Their other remarkable accomplishment was the achievement of 93.69% accuracy, which was obtained by using a tunable Q-wavelet transform (Hassan & Bhuiyan, 2016a). …”

Section: Introductionmentioning

confidence: 99%

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Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Savareh

Bashiri

Behmanesh

et al. 2018

PeerJ

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IntroductionSleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.MethodsSleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis.ResultsNeighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively.Discussion and ConclusionSimilar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Savareh

Bashiri

Behmanesh

et al. 2018

PeerJ

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show abstract

“…(2) is regarded as a linear independent vector to calculate the orthogonal set of vectors. The equations for the Gram-Schmidt orthogonalization process are as follows [9] : 错误!未找到引用源。 (4) 错误!未找到引用源。 (5) 错误!未找到引用源。 (6) Where 错误!未找到引用源。is kth group of the standard vector according to the Eq. (2), and 错误!未找到引用源。 is kth group of perpendicular to each other with the same linear span.…”

Section: Mahalanobis-taguchi-gram-schmidt Methodsmentioning

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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“…In case of helical gear, EMD is applied to vibration and acoustic signals and based on the kurtosis value, for both the input signals and empirical mode decomposition obtained the upper hand of EMD is exhibited.Kedadouche et al [14] applied combination of EMD and Lempel-Ziv complexity method the detect damage in an early stage of gear. Hassan and Bhuiyan [15] demonstrated the effectiveness of sleep scoring algorithm combine with TQWT features. Mcfadden and Smith [16] explained how the defect influencing the dynamic characteristic of the rolling bear with radial load.…”

Section: Introductionmentioning

confidence: 99%

Research on intelligent fault diagnosis of gears using EMD, spectral features and data mining techniques

Sagar

Vivekkumar

Reddy

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features

Cited by 218 publications

References 42 publications

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

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

Research on intelligent fault diagnosis of gears using EMD, spectral features and data mining techniques

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