Data reduction algorithms to enable long-term monitoring from low-power miniaturised wireless EEG systems

Abstract

Objectives: The weight and volume of battery-powered wireless electroencephalography (EEG) systems are dominated by the batteries. Battery dimensions are in turn determined by the required energy capacity, which is derived from the system power consumption and required monitoring time. Data reduction may be carried out to reduce the amount of data transmitted and thus proportionally reduce the power consumption of the wireless transmitter, which dominates system power consumption. This thesis presents two new data selection algorithms that, in addition to achieving data reduction, also select EEG containing epileptic seizures and spikes that are important in diagnosis. Methods: The algorithms analyse short EEG sections, during monitoring, to determine the presence of candidate seizures or spikes. Phase information from different frequency components of the signal are used to detect spikes. For seizure detection, frequencies below 10 Hz are investigated for a relative increase in frequency and/or amplitude. Significant attention has also been given to metrics in order to accurately evaluate the performance of these algorithms for practical use in the proposed system. Additionally, signal processing techniques to emphasize seizures within the EEG and techniques to correct for broad-level amplitude variation in the EEG have been investigated. Results: The spike detection algorithm detected 80% of spikes whilst achieving 50% data reduction, when tested on 992 spikes from 105 hours of 10-channel scalp EEG data obtained from 25 adults. The seizure detection algorithm identified 94% of seizures selecting 80% of their duration for transmission and achieving 79% data reduction. It was tested on 34 seizures with a total duration of 4158 s in a database of over 168 hours of 16-channel scalp EEG obtained from 21 adults. These algorithms show great potential for longer monitoring times from miniaturised wireless EEG systems that would improve electroclinical diagnosis of patients.