CNEL Alumnus: Helene Chini


Former student webpage:
About Me

I am a master's student under Dr Principe at the CNEL.
 
My Research Interests

I am interested in unsupervised segmentation algorithms applied to data from patients under assisted ventilation.
 
Research Goals

To apply different non-linear prediction models and segmentation algorithms to biomedical data.

Outline of my thesis:

INTRODUCTION
Goal(apply segmentation techniques to non-stationary data with nonlinear models)

CHAPTER 1: MODELING AND MONITORING OF THE DATA
*Description of data (assisted ventilation; flow, pressure: shape and parameters)
*Different models for the data:
1. linear vs. non linear :why a linear model is no good
2. RBF network: several clusterings for 1st layer, adaptation of linear layer
3. Feedforward network: less versatile
4. Supervised vs. unsupervised training, and the problem of generalization
*Methods of segmentation
1. The segmentation criterion: MSE estimate
2. Hard competition on MSE
3. Adaptation of the models: initial & tracking

CHAPTER 2: SIMULATIONS WITH SYNTHETIC DATA TO BENCHMARK METHODS:
*Synthetic data
*The linear model
*Supervised training
1. Feedforward networks
2. RBF
*Unsupervised training
1. Feedforward networks
2. RBF

CHAPTER 3: SIMULATION WITH SEVERAL SAMPLES OF REAL DATA
*Data: patient doesn’t fight ventilator
*Data: relatively clean
*Data: patient agitated and fights ventilator

CHAPTER 4: CONCLUSION




Links

 
CNEL Papers
Thesis
1Helene Chili, "Nonlinear Neural Networks for Modeling and Online Segmentation of Nonstationary Ventilatory Signals", Dec. 2002.(PDF)
 

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