Title :
Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors
Author :
Migliorini, Matteo ; Bianchi, Anna M. ; Nisticò, Domenico ; Kortelainen, Juha ; Arce-Santana, Edgar ; Cerutti, Sergio ; Mendez, Martin O.
Author_Institution :
Dept. of Biomed. Eng., Politec. di Milano, Milan, Italy
fDate :
Aug. 31 2010-Sept. 4 2010
Abstract :
This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant for staging sleep in REM, nonREM and WAKE periods. The performances of all the possible combinations of feature extractors and classifiers are compared in terms of accuracy and kappa index, using clinical polysomographyc evaluation as golden standard. 17 recordings from healthy subjects, including also polisomnography, were used to train and test the algorithms. When automatic classification is compared. QD-TVAM algorithm achieved a total accuracy of 76.81 ± 7.51% and kappa index of 0.55 ± 0.10, while LD-WDT achieved a total accuracy of 79 ± 10% and kappa index of 0.51 ± 0.17. The results suggest that a good sleep evaluation can be achieved through non-conventional recording systems that could be used outside sleep centers.
Keywords :
cardiology; discrete wavelet transforms; feature extraction; medical signal processing; pressure sensors; regression analysis; signal classification; sleep; HRV; REM sleep period; TVAM; WDT; automatic sleep classification; automatic sleep staging; ballistocardiographic signals; bed sensors; feature extraction; heart rate variability; kappa index; linear discriminant classifier; movement signals; nonREM sleep; polysomography; quadratic discriminant classifier; respiration signals; time variant autoregressive model; wake period; wavelet discrete transform; Accuracy; Classification algorithms; Computational modeling; Feature extraction; Heart rate variability; Indexes; Sensors; Adult; Algorithms; Ballistocardiography; Beds; Diagnosis, Computer-Assisted; Female; Humans; Pattern Recognition, Automated; Polysomnography; Reproducibility of Results; Sensitivity and Specificity; Sleep Stages; Transducers;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
Print_ISBN :
978-1-4244-4123-5
DOI :
10.1109/IEMBS.2010.5627217