Restriction lift date: 2023-10-31
AI-assisted analysis of heart sounds and interpretation of acoustic representation of brainwaves in neonates
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Date
2022-08-18
Authors
Gomez Quintana, Sergi
Journal Title
Journal ISSN
Volume Title
Publisher
University College Cork
Published Version
Abstract
Numerous reports from World Health Organisation (WHO) consistently list the diseases of the heart and the brain among the top three causes of death across the globe. In low and low-to-middle-income countries, the neonatal stage is the most dangerous of the whole life and is a time of particular concern for medical professionals and parents. Timely detection of abnormalities during the first days of life allows medical staff to make informed decisions which have life-saving consequences. For this, continuous monitoring is required and it has several challenges in a clinical setting. First, acquiring physiological data from neonates is not trivial, often involving time-consuming processes that require specialised training. Second, specific monitoring equipment is often expensive and not affordable in low-income communities. More importantly, the complexity of the data may be difficult to interpret even for trained professionals and the required expertise might not be available 24/7. Alternative methods and tools that are low cost and require minimum training while providing the accuracy level of a specialist medical professional are required. This work deals with the development of such methods for the analysis of neonatal heart and brain signals by means of artificial intelligence (AI) and AI-guided sonification.
Sound analysis can play an important role as a non-invasive, intuitive, and cost-effective tool to facilitate the interpretation of physiological signals. Heart auscultation is already part of the clinical examination routine. It uses a stethoscope, which is a low cost and reliable tool to screen for neonatal heart defects. However, heart sound interpretation is subjective, dependent on the assessor’s hearing acuity and the acquired level of expertise. Assistance from AI can provide an objective interpretation of heart sounds to complement the traditional auscultation method. A novel, accurate method for detecting congenital heart disease in phonocardiogram (PCG) signals using AI is presented.
When dealing with the brain abnormalities in newborns, neonatal seizures are one of the most common neurological conditions, and they need to be treated as a medical emergency with prompt detection and intervention. Electroencephalography (EEG), the gold standard for monitoring electrical brain activity, is often difficult to interpret visually and requires a highly specialised medical professional. These professionals might not be readily available in low or medium-income settings, and even in high-income countries, they might be available only in tertiary care centres and not present 24/7. AI-driven sonification of EEG for detection of neonatal seizures, which is developed in this work, helps to improve the detection of these threatening seizure events by decreasing the level of expertise required from healthcare professionals while maintaining the same accuracy. It is shown that AI-assisted sonification can augment the medical professional to make decisions which are better than AI alone while improving the interpretability of the made decisions, which is a key requirement in the medical domain.
The proposed algorithms and methodologies are validated on numerous datasets. The developed prototypes are implemented using cloud and Internet of Things technologies. It is shown that these technologies allow for an affordable, real-time analysis of heart and brain physiological signals with minimum training.
Description
Keywords
Biomedical , EEG , PCG , Neonatal , Brainwave sonification , AI-assisted brainwave sonificaiton , Edge implementation , Signal processing , Machine learning , Artificial intelligence
Citation
Gomez Quintana, S. 2022. AI-assisted analysis of heart sounds and interpretation of acoustic representation of brainwaves in neonates. PhD Thesis, University College Cork.