{"id":{"repo_id":"cork","oai_identifier":"oai:cora.ucc.ie:10468/15015"},"canonical_url":"https://search.dev.ndltd.org/etd/cork/oai:cora.ucc.ie:10468/15015","repository":{"repo_id":"cork","name":"University College Cork","base_url":"https://cora.ucc.ie/server/oai/request"},"display":{"title":"Implementation of an AI-assisted sonification algorithm on an edge device","abstract":"Oxygen deprivation at birth leads to brain injury, which can have serious consequences. It is the dominant cause of seizures. Quickly and accurately detecting seizures is a challenging problem for neonates. A severe shortage of medical professionals with the necessary expertise for Electroencephalogram (EEG) analysis leads to significant delays in decision-making and hence treatment. These problems are made worse in disadvantaged communities. Artificial intelligence (AI) techniques have been proposed to automate the process and compensate for the lack of available expertise. However, these models are ’black boxes', and their lack of explainability dampens the wide adoption by medical professionals. AI-assisted sonification adds explainability to any such automated methodology, empowering medical professionals to make accurate decisions regardless of their level of expertise in EEG analysis. The feasibility of an implementation of an AI-assisted sonification algorithm on an edge device is presented and analyzed. A lightweight derived algorithm for resource-constrained implementation scenarios is also evaluated and presented, suggesting suitability for further ultra-low power, mobile and wearables implementations. Furthermore, a neural network is analysed for the potential of low-precision implementation, enabling inference on specialised hardware.","abstract_html":"Oxygen deprivation at birth leads to brain injury, which can have serious consequences. It is the dominant cause of seizures. Quickly and accurately detecting seizures is a challenging problem for neonates. A severe shortage of medical professionals with the necessary expertise for Electroencephalogram (EEG) analysis leads to significant delays in decision-making and hence treatment. These problems are made worse in disadvantaged communities. Artificial intelligence (AI) techniques have been proposed to automate the process and compensate for the lack of available expertise. However, these models are ’black boxes', and their lack of explainability dampens the wide adoption by medical professionals. AI-assisted sonification adds explainability to any such automated methodology, empowering medical professionals to make accurate decisions regardless of their level of expertise in EEG analysis. The feasibility of an implementation of an AI-assisted sonification algorithm on an edge device is presented and analyzed. A lightweight derived algorithm for resource-constrained implementation scenarios is also evaluated and presented, suggesting suitability for further ultra-low power, mobile and wearables implementations. Furthermore, a neural network is analysed for the potential of low-precision implementation, enabling inference on specialised hardware.","abstract_has_math":false,"creators":["O'Sullivan, Feargal"],"institution":"University College Cork","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Popovici, Emanuel","Temko, Andriy"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T01:46:20Z","subjects":["AI","Artificial Intelligence","Signal processing","EEG","Biomedical","Bio-signals","Sonification"],"languages":["en"],"rights":["© 2023, Feargal O'Sullivan."],"rights_urls":["https://creativecommons.org/publicdomain/zero/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10468/15015","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Popovici, Emanuel","Temko, Andriy"]},{"key":"dc:creator","label":"Author","values":["O'Sullivan, Feargal"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-09-20T14:12:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-09-20T14:12:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2023"]},{"key":"dc:publisher","label":"Institution","values":["University College Cork"]},{"key":"dc:type","label":"Dc Type","values":["Masters thesis (Research)"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MRes - Master of Research"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["AI","Artificial Intelligence","Signal processing","EEG","Biomedical","Bio-signals","Sonification"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2023, Feargal O'Sullivan."]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/publicdomain/zero/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10468/15015"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Oxygen deprivation at birth leads to brain injury, which can have serious consequences. It is the dominant cause of seizures. Quickly and accurately detecting seizures is a challenging problem for neonates. A severe shortage of medical professionals with the necessary expertise for Electroencephalogram (EEG) analysis leads to significant delays in decision-making and hence treatment. These problems are made worse in disadvantaged communities. Artificial intelligence (AI) techniques have been proposed to automate the process and compensate for the lack of available expertise. However, these models are ’black boxes', and their lack of explainability dampens the wide adoption by medical professionals. AI-assisted sonification adds explainability to any such automated methodology, empowering medical professionals to make accurate decisions regardless of their level of expertise in EEG analysis. The feasibility of an implementation of an AI-assisted sonification algorithm on an edge device is presented and analyzed. A lightweight derived algorithm for resource-constrained implementation scenarios is also evaluated and presented, suggesting suitability for further ultra-low power, mobile and wearables implementations. Furthermore, a neural network is analysed for the potential of low-precision implementation, enabling inference on specialised hardware."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Implementation of an AI-assisted sonification algorithm on an edge device"]}]}],"canonical_facts":{"dc:contributor.advisor":["Popovici, Emanuel","Temko, Andriy"],"dc:creator":["O'Sullivan, Feargal"],"dc:date.accessioned":["2023-09-20T14:12:14Z"],"dc:date.available":["2023-09-20T14:12:14Z"],"dc:date.issued":["2023"],"dc:description.abstract":["Oxygen deprivation at birth leads to brain injury, which can have serious consequences. It is the dominant cause of seizures. Quickly and accurately detecting seizures is a challenging problem for neonates. A severe shortage of medical professionals with the necessary expertise for Electroencephalogram (EEG) analysis leads to significant delays in decision-making and hence treatment. These problems are made worse in disadvantaged communities. Artificial intelligence (AI) techniques have been proposed to automate the process and compensate for the lack of available expertise. However, these models are ’black boxes', and their lack of explainability dampens the wide adoption by medical professionals. AI-assisted sonification adds explainability to any such automated methodology, empowering medical professionals to make accurate decisions regardless of their level of expertise in EEG analysis. The feasibility of an implementation of an AI-assisted sonification algorithm on an edge device is presented and analyzed. A lightweight derived algorithm for resource-constrained implementation scenarios is also evaluated and presented, suggesting suitability for further ultra-low power, mobile and wearables implementations. 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