A New Fully Automated Random-Forest Algorithm for Sleep Staging

Klok, Aske B.; Edin, Joakim; Cesari, Matteo; Olesen, Alexander Neergaard; Jennum, Poul; Sørensen, Helge Bjarup Dissing

doi:10.1109/embc.2018.8513413

Cited by 16 publications

(10 citation statements)

References 20 publications

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“…The shorter time span is a facilitation for the patient as well as for the attending physician. In the context of this research about the RBD disorder, early detection could have an impact on avoiding secondary diseases like Parkinson or Dementia, which strongly affect the lives of the patients [13].…”

Section: Discussionmentioning

confidence: 99%

“…To suffer under a secondary disease means a high risk for people with the RBD disorder. About 50 % of all patients will develop another neurodegenerative disease like Dementia or Parkinson within 12 years [13]. To diagnose RBD, polysomnographic recordings (PG) are used.…”

Section: Introductionmentioning

confidence: 99%

“…The analysis of overnight EEG recordings is complex and time-consuming. In addition, there is the fact that different physicians make different diagnoses using the same EEG recording [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

Buettner¹,

Grimmeisen²,

Gotschlich³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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While diagnosing sleep disorders by physicians using electroencephalographic data is protracted and inaccurate, we report promising results from a novel, fast and reliable machine learning approach. Our approach only needs an electroencephalographic recording snippet of 10 minutes instead of eight hours to correctly classify the disorder with an accuracy of over 90 percent. The Rapid Eye Movement sleep behavior disorder can lead to secondary diseases like Parkinson or Dementia. Therefore, it is important to classify the disorder fast and with a high level of accuracy -which is now possible with our approach.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

Buettner¹,

Grimmeisen²,

Gotschlich³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…We demonstrated that the accuracy of several classifiers was heavily influenced by the pre-processing of the signals, but the chosen method was not affected by this process. While other groups have reported using the random forest classifier to score sleep [43][44][45] fewer have used the bagging 46 or gradient boosting ensemble classification methods. 47 To our knowledge, even fewer approaches have used the bagging classifier with random forest as the base classifier.…”

Section: Discussionmentioning

confidence: 99%

Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An open-source platform for automatic sleep staging of rodent polysomnographic data

Caldart

Sánchez

Ben-Hamo

et al. 2020

Preprint

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AbstractThe temporal distribution of sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. This temporal distribution is typically determined polysomnographically. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyography (EMG) recordings is usually performed manually, where 5-10 second epochs are categorized as one of three specific stages: wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep. This process is laborious, time-consuming, and particularly impractical for large experimental cohorts with recordings lasting longer than 24 hours.To circumvent this problem, we developed an open-source Python toolkit, Sleep Identification Enabled by Supervised Training Algorithms (SIESTA), that automates the detection of these three main behavioral stages in mice. Our supervised machine learning algorithm extracts features from the ECoG and EMG signals, then automatically scores recordings with a hierarchical classifier based on Bagging Random Forest approaches. We evaluated this approach on data collected from wild-type mice housed under both normal and different lighting conditions, as well as from a mutant mouse line with abnormal sleep phenotypes. To validate its performance on test data, we compared SIESTA with manually scored data and obtained F1 scores of 0.92 for wakefulness, 0.81 for REM, and 0.93 for NREM.SIESTA has a user-friendly interface that can be used without coding expertise. To our knowledge, this is the first time that such a strategy has been developed using all open-source and freely available resources, and our aim is that SIESTA becomes a useful tool that facilitates further research of sleep in rodent models.

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“…In [12], The authors took extracted features from multiple aspects, including time domain features (mean, 75th percentile), frequency domain feature (band powers, spectral variance, band power ratio), and non-linear features (approximate entropy, Lempel-Ziv complexity). After the features were extracted, they were then selectively fed into a classifier, such as a random forest [12,13] or a support vector machine [14], to determine the sleep stage. In deep learning, instead of defining features manually, a neural network is used to automatically obtain features from the EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Sleep Stage Classification for Clinical Practices across Different Polysomnography Systems Using Frontal EEG

Juang

et al. 2021

Processes

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Automatic bio-signal processing and scoring have been a popular topic in recent years. This includes sleep stage classification, which is time-consuming when carried out by hand. Multiple sleep stage classification has been proposed in recent years. While effective, most of these processes are trained and validated against a singular set of data in uniformed pre-processing, whilst in a clinical environment, polysomnography (PSG) may come from different PSG systems that use different signal processing methods. In this study, we present a generalized sleep stage classification method that uses power spectra and entropy. To test its generality, we first trained our system using a uniform dataset and then validated it against another dataset with PSGs from different PSG systems. We found that the system achieved an accuracy of 0.80 and that it is highly consistent across most PSG records. A few samples of NREM3 sleep were classified poorly, and further inspection showed that these samples lost crucial NREM3 features due to aggressive filtering. This implies that the system’s effectiveness can be evaluated by human knowledge. Overall, our classification system shows consistent performance against PSG records that have been collected from different PSG systems, which gives it high potential in a clinical environment.

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A New Fully Automated Random-Forest Algorithm for Sleep Staging

Cited by 16 publications

References 20 publications

High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An open-source platform for automatic sleep staging of rodent polysomnographic data

Hybrid Sleep Stage Classification for Clinical Practices across Different Polysomnography Systems Using Frontal EEG

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