Machine learning techniques for detecting hypoglycemic  events using electrocardiograms

Carmo, Natasha Rusty Silva

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Campo DC	Valor	Idioma
dc.contributor.author	Carmo, Natasha Rusty Silva	-
dc.date.accessioned	2022-02-07T19:17:03Z	-
dc.date.available	2022-02-07T19:17:03Z	-
dc.date.issued	2021-08-20	-
dc.identifier.citation	CARMO, Natasha Rusty Silva. Machine learning techniques for detecting hypoglycemic events using electrocardiograms. 2021. 86 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Sergipe, São Cristóvão, 2021.	pt_BR
dc.identifier.uri	https://ri.ufs.br/jspui/handle/riufs/15019	-
dc.language	eng	pt_BR
dc.subject	Machine learning	eng
dc.subject	Biosignal processing	eng
dc.subject	Hypoglycemia	eng
dc.subject	D1namo dataset	eng
dc.subject	Neurokit1	eng
dc.subject	1dcnn	eng
dc.title	Machine learning techniques for detecting hypoglycemic events using electrocardiograms	pt_BR
dc.type	Dissertação	pt_BR
dc.contributor.advisor1	Moreno, Edward David	-
dc.description.resumo	Background Machine learning methods have long been employed to automatically analyze electrocardiogram signals. In the past ten years, most studies have used a limited number of open databases to test their results, most of which were collected in clinical settings. The growth in the number of fitness trackers and other wearable devices that collect large amounts of data every day offer a new potential to use data analysis to derive information that can improve the quality of life for many people. Recently, an open database was released with data (electrocardiogram, respiratory rate, motion data, food intake annotations and blood glucose) from patients with type 1 diabetes. It gives the opportunity to explore the potential of this data to predict hypoglycemic events through a noninvasive method. Methods The study uses pre-processing techniques to clean the data and extract features from physiological signals in the dataset and verify how they correlate with blood glucose. Time and frequency domain features are derived from the signal for the analysis. Automatic machine learning is employed to determine the best classification model. The results are compared against a 1D Convolutional Neural Network approach that automatically extracts features from individual heart beats. The final models are evaluated in regards to performance metrics (accuracy, precision and sensitivity) with respect to their ability to predict hypoglycemic events. Results A 10-fold cross-validation provided the following percentage values for accuracy, precision and sensitivity, respectively: 86.89 ± 2.8, 87.03 ± 2.7 and 86.90 ± 2.8 for the Random Forest model and 93.00 ± 2.3, 93.08 ± 2.2 and 93.00 ± 2.3 for 1D CNN. The statistical evaluation of the mean accuracy for both models from an unpaired T test returned a p-value lower than 0.0001, meaning that the distributions are significantly different and 1D CNN model outperforms the decision tree model. Discussion and Conclusion The small number of positive samples for hypoglycemia and high data imbalance pose a challenge to classification. It is necessary to have reasonable number of samples from both classes to achieve classification metrics that are suitable for medical applications. When this condition is satisfied, data acquired from a wearable device under normal living conditions has shown to be suitable for the task of classifying hypoglycemic events.	pt_BR
dc.publisher.program	Pós-Graduação em Ciência da Computação	pt_BR
dc.subject.cnpq	CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO	pt_BR
dc.publisher.initials	Universidade Federal de Sergipe	pt_BR
dc.description.local	São Cristóvão	pt_BR
Aparece nas coleções:	Mestrado em Ciência da Computação

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