Digital Medicine in Neuroscience

Project 1: Close loop ventilator

Design, develop, and simulate a closed-loop mechanical ventilator that can accurately control respiratory rate, tidal volume, and inspiratory pressure to provide life-supporting therapy for patients with respiratory distress.

1. Literature Review: Research existing closed-loop mechanical ventilators and their limitations. Identify the importance of closed-loop control in mechanical ventilation.

2. Design Requirements: Specify the components and subsystems necessary for the design (e.g., sensors, actuators, control algorithms). Determine the power supply requirements.

3. System Design: Create a block diagram or flowchart illustrating the system's architecture. Identify potential sources of error or noise in the system.

4. Control Algorithm Development: Choose an appropriate control algorithm (e.g., PID, fuzzy logic) and explain its selection. Implement the control algorithm using programming languages like MATLAB/Simulink, Python, or C++.

5. Simulation and Testing: Use simulation tools to test and validate the system's performance under different scenarios (e.g., varying respiratory rates, tidal volumes). Identify potential issues and areas for improvement.

Project 2: Machine Learning in Medical Bio-signal processing

This project aims to leverage machine learning techniques to analyze and interpret medical bio-signals, such as electrocardiograms (ECG), electroencephalograms (EEG), and electromyograms (EMG). The goal is to develop models that can accurately detect and classify various physiological conditions, enhancing diagnostic capabilities and patient care.

Objectives:

1. To collect and preprocess medical bio-signal data.

2. To apply machine learning algorithms for feature extraction and selection.

3. To develop and train machine learning models for classification and prediction.

4. To evaluate the performance of the models using appropriate metrics.

5. To implement a user-friendly interface for real-time bio-signal analysis.