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BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Published on: December 9, 2015

Modelling stabilograms with hidden Markov models.

J Rasku1, M Juhola, T Tossavainen

  • 1Department of Computer Sciences, 33014 University of Tampere, Tampere, Finland.

Journal of Medical Engineering & Technology
|July 31, 2008
PubMed
Summary
This summary is machine-generated.

Hidden Markov models effectively analyze complex human balance signals. These computational models can distinguish between healthy individuals and Meniere's disease patients, even with signal noise.

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Last Updated: Jul 3, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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Published on: December 9, 2015

Area of Science:

  • Computational biology
  • Biomedical engineering
  • Signal processing

Background:

  • Hidden Markov models (HMMs) are powerful tools for analyzing complex digital signals.
  • Human balance signals are intricate and can be challenging to interpret visually.
  • Distinguishing between healthy individuals and those with neurological conditions is crucial in clinical settings.

Purpose of the Study:

  • To investigate the utility of Hidden Markov Models (HMMs) for controlling and representing human balance signals.
  • To determine if HMMs can accurately classify healthy subjects and patients with Meniere's disease.
  • To assess the robustness of HMMs in handling noise and perturbations within balance data.

Main Methods:

  • Utilized Hidden Markov Models (HMMs) for signal analysis.
  • Recorded human balance data from subjects standing on a force platform.
  • Applied HMMs to classify individuals into predefined groups (healthy controls vs. Meniere's disease patients).

Main Results:

  • Hidden Markov Models (HMMs) demonstrated capability in controlling and representing human balance signals.
  • Accurate classification of healthy controls and Meniere's disease patients was achieved using HMMs.
  • HMMs proved effective in overcoming signal disturbances, including noise and unforeseen perturbations.

Conclusions:

  • Hidden Markov Models (HMMs) are a viable computational method for analyzing complex human balance signals.
  • HMMs offer a robust approach for clinical classification tasks, such as differentiating between healthy individuals and Meniere's disease patients.
  • The application of HMMs enhances the interpretation of biological signals, even in the presence of inherent complexities and noise.