M. Migliorini

Publications (3)0 Total impact

• Conference Proceeding: Evaluation of the sleep quality based on bed sensor signals: Time-variant analysis

  M.O. Mendez, M. Migliorini, J.M. Kortelainen, D. Nistico, E. Arce-Santana, S. Cerutti, A.M. Bianchi
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  ABSTRACT: Automatic detection of the sleep macrostructure (Wake, NREM -non Rapid Eye Movement- and REM-Rapid Eye Movement-) based on bed sensor signals is presented. This study assesses the feasibility of different methodologies to evaluate the sleep quality out of sleep centers. The study compares a) the features extracted from time-variant autoregressive modeling (TVAM) and Wavelet Decomposition (WD) and b) the performance of K-Nearest Neighbor (KNN) and Feed Forward Neural Networks (FFNN) classifiers. In the current analysis, 17 full polysomnography recordings from healthy subjects were used. The best agreement for Wake-NREM-REM with respect to the gold standard was 71.95 ± 7.47% of accuracy and 0.42 ± 0.10 of kappa index for TVAM-LD while WD-FFNN shows 67.17 ± 11.88% of accuracy and 0.39 ± 0.13 of kappa index. The results suggest that the sleep quality assessment out of sleep centers could be possible and as consequence more people could be beneficiated.
  Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
• Conference Proceeding: Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors

  M. Migliorini, A.M. Bianchi, D. Nisticò, J. Kortelainen, E. Arce-Santana, S. Cerutti, M.O. Mendez
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  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.
  Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
• Article: Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors

  M. Migliorini, A M Bianchi, D. Nisticograve, Juha M. Kortelainen, E. Arce-Santana, S Cerutti, M O Mendez
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  ABSTRACT: 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). Buenos Aires, Argentina, 31 Aug. - 4 Sept. 2010, 3273-3276