Real-world example: Comparisons of switching inference algorithms for sleep spindle detection.

(a) In the top three panels, the spindle activity is visualized using a spectrogram, the original time trace, and after being bandpass filtered within 10 Hz–16 Hz. The fourth panel shows the posterior model probabilities of st = 1...

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Bibliographic Details
Main Author:	Mingjian He (10158389) (author)
Other Authors:	Proloy Das (16892326) (author), Gladia Hotan (16892329) (author), Patrick L. Purdon (10158392) (author)
Published:	2023
Subjects:	Biophysics Medicine Genetics Biotechnology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified sleep spindle detection provide explicit derivations perform extensive simulations human sleep electroencephalograms exhibiting rapid changes estimate model parameters inference problem iteratively extract transient spindles underlying brain dynamics probabilistic switching process different dynamics selected switching process transient activity different settings state inference switching state variational approximation ubiquitous tool stationary methods space models space modeling real recordings parameter learning maximization algorithm key feature joint posterior hidden states generalized expectation explicitly modeled exact solutions employing fixed efficient leave e ., duration windows deterministic annealing benchmark performance
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Real-world example: Comparisons of switching inference algorithms for sleep spindle detection.

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