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Difference between revisions of "Exploring feature selection and classification algorithms for ultra-low-power closed-loop systems for epilepsy control"

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===Status: Available===
 
===Status: Available===
 +
:Looking for Semester and Master Project Students
 
:Supervision: [[:User:Liaoj | Jiawei Liao]], [[:User:Herschmi | Michael Hersche]], Reza Ranjandish  
 
:Supervision: [[:User:Liaoj | Jiawei Liao]], [[:User:Herschmi | Michael Hersche]], Reza Ranjandish  
 
===Prerequisites===
 
===Prerequisites===

Revision as of 22:16, 18 January 2021

Ieeg seizure.png
EpilepsyStim.jpg

Introduction

The usage of Implantable Medical Devices (IMD) as therapeutic systems is gaining acceptance in the medical community and from patients. Following this trend and taking benefit of improvements in microelectronic fabrication technologies, new systems are proposed. The complexity of the microelectronic implantable systems has significantly increased over the years, constituting true systems-on-a-chip, that gather functionalities including electrical recording and stimulation, data pre-processing, wireless power and data transfer and overall system control. Data is generally processed inside the implant in the digital domain. The major goal consists of reliably detecting some markers of an abnormal signal that allows tailoring a therapy that is to be delivered in a closed-loop manner. The most widely used method and data processing chain involves recording of electrical signals, converting them into the digital domain, detecting some features and their relative intensities, and subsequently controlling electrical stimulation. The effectiveness of the delivered therapy is verified in real time. This technique is used in the control of epilepsy in closed-loop fashion, for example. Still, improving the efficiency of the detection is a key factor to the success of the therapy.

Project Description

The goal of this project is to optimize the feature selection algorithm and develop a more accurate and light-weight seizure detection algorithm. To this end, students will:

  • Study literature about seizure detection and feature selection
  • Optimize feature selection algorithms
  • Implement machine learning algorithms for seizure detection based on the selected features. Both traditional machine learning and deep learning algorithms can be explored.
  • Test on iEEG dataset


Status: Available

Looking for Semester and Master Project Students
Supervision: Jiawei Liao, Michael Hersche, Reza Ranjandish

Prerequisites

  • Machine Learning
  • Python Programming

Character

  • 20% literature review
  • 40% Feature selection algorithm optimization
  • 40% Seizure detection algorithm development

Professor

Prof. Taekwang Jang <tjang@ethz.ch>

Reference

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