Yandy M Castillo-Aleman, May A Martinez, Yendry Ventura-Carmenate, Carlos A Villegas-Valverde, Antonio A Bencomo-Hernandez, Rene A Rivero Jimenez, Fatema M Al-Kaabi
Effects of Ramadan Fasting on Extracorporeal Photopheresis Outcomes
Cureus
linear parametric statespace models ubiquitous tool analyzing neural time series data providing way characterize underlying brain dynamics much greater statistical efficiency nonparametric data analysis approaches however neural time series data frequently timevarying exhibiting rapid changes dynamics transient activity often key feature interest data stationary methods adapted timevarying scenarios employing fixedduration windows assumption quasistationarity timevarying dynamics explicitly modeled switching statespace models ie using pool statespace models different dynamics selected probabilistic switching process unfortunately exact solutions state inference parameter learning switching statespace models intractable revisit switching statespace model inference approach first proposed ghahramani hinton provide explicit derivations solving inference problem iteratively applying variational approximation joint posterior hidden states switching process introduce novel initialization procedure using efficient leaveoneout strategy compare among candidate models significantly improves performance compared existing method relies deterministic annealing utilize state inference solution within generalized expectationmaximization algorithm estimate model parameters switching process linear statespace models dynamics potentially shared among candidate models perform extensive simulations different settings benchmark performance existing switching inference methods validate robustness switching inference solution outside generative switching model class finally demonstrate utility method sleep spindle detection real recordings showing switching statespace models used detect extract transient spindles human sleep electroencephalograms unsupervised manner