EEG sleep stages identification based on weighted undirected complex networks

Diykh, Mohammed; Li, Yan; Abdulla, Shahab

doi:10.1016/j.cmpb.2019.105116

Cited by 52 publications

(32 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ISRUC3 database was scored by two experts and the label made by the second expert was used in this paper. The Pz-Oz channel of the S-EDF database is used according to the recommendations of various studies [ 3 , 4 , 5 , 6 , 7 ]. At the same time, for the DRMS database, as the researches [ 9 , 10 , 11 , 12 ] recommended, the Cz-A1 channel was adopted in this work.…”

Section: Methodsmentioning

confidence: 99%

“…With the random forest classifier, they achieved accuracies of 90.38%, 91.50%, 92.11%, 94.80%, 97.50% for 6-stage to 2-stage classification of sleep states on the Sleep-EDF database. Diykh et al adopted different structural and spectral attributes extracted from weighted undirected networks to automatically classify the sleep stages [ 4 ]. Kang et al present a statistical framework to estimate whole-night sleep states in patients with obstructive sleep apnea (OSA)—the most common sleep disorder [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

Shen

Ran

et al. 2020

Sensors

View full text Add to dashboard Cite

The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen’s and Kale’s (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

Shen

Ran

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…For modeling nonlinear data, complex networks are effective method [27]. Complex network is a weighted undirected graph G= (V, E, W), where V is the set of nodes, E denotes the set of edges e (vi, vj) between the pairs of the nodes vi and vj and W is the weights w (vi, vj) assigned to their 13 corresponding edges e (vi, vj) of E. Three complex networks are constructed from the training datasets and one data record which should be classified independent from it belongs to training or test dataset.…”

Section: Constructing Complex Network Of Patientsmentioning

confidence: 99%

CNFE-SE: A novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features

Ranjbari

Khatibi

Dizaj

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features. Methods: For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples' demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcome Results: Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions: The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).

show abstract

“…For modeling nonlinear data, complex networks are effective method [25]. Complex network is a weighted undirected graph G= (V, E, W), where V is the set of nodes, E denotes the set of edges e (vi, vj) between the pairs of the nodes vi and vj and W is the weights w (vi, vj) assigned to the edges e (vi, vj) of E. Three complex networks are constructed from the training datasets.…”

Section: Constructing Complex Network Of Patientsmentioning

confidence: 99%

CNFE-SE: A novel hybrid approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features

Ranjbari

Khatibi

Dizaj

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Intrauterine Insemination (IUI) outcome prediction is a challenging issue with which the assisted reproductive technology (ART) practitioners are dealing. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. The large number of studies that have been focused on predicting the IVF and ICSI outcome by machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to develop an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking of the most significant features, based on patients' cycle’s characteristics under IUI treatment.Methods: For this purpose, a novel hybrid approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute for 8,360 couples who underwent 11,255 IUI cycles were included. Results: Experimental results show that the proposed method outperforms the compared methods with AUC of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions: The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female BMI.

show abstract

EEG sleep stages identification based on weighted undirected complex networks

Cited by 52 publications

References 61 publications

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

CNFE-SE: A novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features

CNFE-SE: A novel hybrid approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features

Contact Info

Product

Resources

About