Development of machine learning schemes for use in non-invasive and continuous patient health monitoring
Baker, Stephanie (2021) Development of machine learning schemes for use in non-invasive and continuous patient health monitoring. PhD thesis, James Cook University.
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Abstract
Stephanie Baker developed machine learning schemes for the non-invasive and continuous measurement of blood pressure and respiratory rate from heart activity waveforms. She also constructed machine learning models for mortality risk assessment from vital sign variations. This research contributes several tools that offer significant advancements in patient monitoring and wearable healthcare.
| Item ID: | 69281 |
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| Item Type: | Thesis (PhD) |
| Keywords: | artificial intelligence, biomedical engineering, body sensor networks, communications standards, cuffless blood pressure, electrocardiogram, intelligent systems, intensive care, Internet of Things (IoT), machine learning, mortality risk prediction, neonatal mortality, neural networks, photoplethysmogram, prognostics, respiratory rate, security, wearable healthcare, wearable sensors |
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| Copyright Information: | Copyright © 2021 Stephanie Baker. |
| Additional Information: | For this thesis, Stephanie Baker received the Dean's Award for Excellence 2022. Five publications arising from this thesis are stored in ResearchOnline@JCU, at the time of processing. Please see the Related URLs. The publications are: Chapter 2: Baker, Stephanie, Xiang, Wei, and Atkinson, Ian (2017) Internet of Things for smart healthcare: technologies, challenges, and opportunities. IEEE Access, 5. pp. 26521-26544. Chapter 3: Baker, Stephanie, Xiang, Wei, and Atkinson, Ian (2021) A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms. Computer Methods and Programs in Biomedicine, 207. 106191. Chapter 4: Baker, Stephanie, Xiang, Wei, and Atkinson, Ian (2021) Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks. PLoS ONE, 16 (4). e0249843. Chapter 5: Baker, Stephanie, Xiang, Wei, and Atkinson, Ian (2020) Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach. Scientific Reports, 10. 21282. Chapter 6: Baker, Stephanie, Xiang, Wei, and Atkinson, Ian (2021) Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates. Computers in Biology and Medicine, 134. 104521. |
| Date Deposited: | 08 Sep 2021 01:13 |
| FoR Codes: | 40 ENGINEERING > 4003 Biomedical engineering > 400308 Medical devices @ 33% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 34% 42 HEALTH SCIENCES > 4203 Health services and systems > 420308 Health informatics and information systems @ 33% |
| SEO Codes: | 20 HEALTH > 2004 Public health (excl. specific population health) > 200407 Health status (incl. wellbeing) @ 50% 22 INFORMATION AND COMMUNICATION SERVICES > 2201 Communication technologies, systems and services > 220105 Network systems and services @ 50% |
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