Publicação
Combining Synthesis of Cardiorespiratory Signals and Artifacts with Deep Learning for Robust Vital Sign Estimation
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Médica | pt_PT |
| dc.contributor.advisor | Antink, Christoph | |
| dc.contributor.advisor | Vieira, Pedro | |
| dc.contributor.author | Silva, Diogo Filipe Pereira Fontes Fernandes | |
| dc.date.accessioned | 2020-01-14T11:18:35Z | |
| dc.date.available | 2020-01-14T11:18:35Z | |
| dc.date.issued | 2019-12 | |
| dc.date.submitted | 2019 | |
| dc.description.abstract | Healthcare has been remarkably morphing on the account of Big Data. As Machine Learning (ML) consolidates its place in simpler clinical chores, more complex Deep Learning (DL) algorithms have struggled to keep up, despite their superior capabilities. This is mainly attributed to the need for large amounts of data for training, which the scientific community is unable to satisfy. The number of promising DL algorithms is considerable, although solutions directly targeting the shortage of data lack. Currently, dynamical generative models are the best bet, but focus on single, classical modalities and tend to complicate significantly with the amount of physiological effects they can simulate. This thesis aims at providing and validating a framework, specifically addressing the data deficit in the scope of cardiorespiratory signals. Firstly, a multimodal statistical synthesizer was designed to generate large, annotated artificial signals. By expressing data through coefficients of pre-defined, fitted functions and describing their dependence with Gaussian copulas, inter- and intra-modality associations were learned. Thereafter, new coefficients are sampled to generate artificial, multimodal signals with the original physiological dynamics. Moreover, normal and pathological beats along with artifacts were included by employing Markov models. Secondly, a convolutional neural network (CNN) was conceived with a novel sensor-fusion architecture and trained with synthesized data under real-world experimental conditions to evaluate how its performance is affected. Both the synthesizer and the CNN not only performed at state of the art level but also innovated with multiple types of generated data and detection error improvements, respectively. Cardiorespiratory data augmentation corrected performance drops when not enough data is available, enhanced the CNN’s ability to perform on noisy signals and to carry out new tasks when introduced to, otherwise unavailable, types of data. Ultimately, the framework was successfully validated showing potential to leverage future DL research on Cardiology into clinical standards. | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/91157 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Multimodal Cardiorespiratory Signals | pt_PT |
| dc.subject | Sensor Fusion | pt_PT |
| dc.subject | Gaussian Copula | pt_PT |
| dc.subject | Deep Learning | pt_PT |
| dc.subject | Convolutional Neural Network | pt_PT |
| dc.subject | Data Augmentation | pt_PT |
| dc.title | Combining Synthesis of Cardiorespiratory Signals and Artifacts with Deep Learning for Robust Vital Sign Estimation | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Master of Science in Biomedical Engineering | pt_PT |