Portable acquisition and interpretation of EEG for neonatal healthcare applications

Neonatal encephalopathy is a significant concern for both parents and medical staff. It results in the death or disability of over 2 million infants globally each year and accounts for 23% of all infant deaths. Early identification and treatment of brain injury is vital. Electroencephalography (EEG) is the gold standard for monitoring brain function. However, conventional EEG monitors are complex systems, which require specialised medical staff to configure and interpret the data. The equipment and expertise are limited to tertiary-care hospitals with neurology/neurophysiology facilities. Even in such hospitals, the process of diagnosing neonatal brain injuries suffers from long delays, making it difficult to intervene within the effective treatment window. In this thesis, a portable EEG acquisition and interpretation system for clinical use in the neonatal population is investigated. The acquisition system includes the design of a low-power and wireless electronic circuit for the acquisition, processing, and transmission of EEG signals. Existing state-of-the-art devices are reviewed and analysed. A custom solution, which offers eight channels of low-noise EEG acquisition and integration with a low-power microcontroller unit for on-board data processing and machine learning inference, is proposed. Novel signal processing and machine learning algorithms to support EEG data interpretation are optimised for use in resource-constrained applications and platforms. To date, minimal consideration is given to the regulatory and commercial requirements when developing medical devices in academia. This introduces a barrier to bringing academic innovation through to clinical adoption. A regulatory and commercial route-to-market is proposed herein for a cost-effective and time-efficient translation to clinical use.

Grantor dc:publisher

University College Cork

Year dc:date.issued

2020

Author dc:creator

• O'Sullivan, Mark

Advisors dc:contributor.advisor

• Popovici, Emanuel
• Temko, Andriy

• EEG
• Electroencephalography
• Electroencephalogram
• Neonatal
• Embedded systems

Statement dc:rights

• © 2020, Mark O'Sullivan.

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10468/11970

OAI identifier oai:identifier

oai:cora.ucc.ie:10468/11970