EEG cap with TEGs will record an electrical activity of a brain using multiple electrodes placed on the scalp. Instead of raw data storage, sensor will do feature extraction for automatic seizure detection. Several medical algorithms are considered to trade off between diagnostic power and power consumption.
Spikes stand out from a normal brain activity on the EEG. Spike density bio-markers are of the great importance for the diagnosis of epilepsy in children. Spike detection algorithms are being implemented within clinical application of real-time EEG signal analysis. They are based on template matching, an approach for signal pattern recognition often used for bio-medical signals. Real life data obtained from Kinderspital is being used to test the algorithm implementation.
The trigger for abnormal behaviour can be found in the environment. Patient environment monitor (PEM) is used to keep a track of the environment and includes accelerometer and gyroscope to identify the amount of movement of the patient. Using very low power microphone and low resolution camera, there is the ability to determine characteristics of the environment of the patient such as level of noise and level of light.
Wearable ICT only allows low-density EEG (LDEEG) for monitoring of patients. However, it suffers from some limitations such as functional connectivity estimation and source estimation. The functional connectivity (FC) is an appealing feature of the brain electromagnetic activity that has been shown to deteriorate in a number of psychiatric and neurodegenerative diseases including Alzheimer’s disease. Long-term FC provides important data for diagnosis. The project seeks to find an approach to assess FC from LDEEG and make measurements with wearable ICT useful.
Analyzing relevant information extracted from multiple bio-signals such as ECG, respiration rate and skin conductance, there is also a possibility of assessing emotions.