0000000000999366

AUTHOR

Karen Spruyt

showing 2 related works from this author

Multi-modality of polysomnography signals’ fusion for automatic sleep scoring

2019

Abstract Objective The study aims to develop an automatic sleep scoring method by fusing different polysomnography (PSG) signals and further to investigate PSG signals’ contribution to the scoring result. Methods Eight combinations of four modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) were considered to find the optimal fusion of PSG signals. A total of 232 features, covering statistical characters, frequency characters, time-frequency characters, fractal characters, entropy characters and nonlinear characters, were derived from these PSG signals. To select the optimal features for each signal fusion, …

Computer science0206 medical engineeringHealth InformaticsFeature selection02 engineering and technologyPolysomnographyElectroencephalographyta3112Approximate entropy03 medical and health sciences0302 clinical medicinepolysomnographymedicineEntropy (information theory)aivotutkimusta217ta113Sleep Stagesmedicine.diagnostic_testsignaalinkäsittelybusiness.industryPattern recognitionautomatic sleep scoringMutual informationuni (biologiset ilmiöt)020601 biomedical engineeringmulti-modality analysisRandom forestSignal ProcessingArtificial intelligencebusiness030217 neurology & neurosurgeryBiomedical Signal Processing and Control
researchProduct

Multi-modality of polysomnography signals’ fusion for automatic sleep scoring

2019

Objective: The study aims to develop an automatic sleep scoring method by fusing different polysomnography (PSG) signals and further to investigate PSG signals’ contribution to the scoring result. Methods: Eight combinations of four modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) were considered to find the optimal fusion of PSG signals. A total of 232 features, covering statistical characters, frequency characters, time-frequency characters, fractal characters, entropy characters and nonlinear characters, were derived from these PSG signals. To select the optimal features for each signal fusion, four wi…

polysomnographysignaalinkäsittelyautomatic sleep scoringaivotutkimusuni (biologiset ilmiöt)multi-modality analysis
researchProduct