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Detection of abnormalities in ECG using Deep Learning
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Resumo(s)
A significant part of healthcare is focused on the information that the physiological signals
offer about the health state of an individual. The Electrocardiogram (ECG) cyclic
behaviour gives insight on a subject’s emotional, behavioral and cardiovascular state.
These signals often present abnormal events that affects their analysis. Two examples
are the noise, that occurs during the acquisition, and symptomatic patterns, that are
produced by pathologies.
This thesis proposes a Deep Neural Networks framework that learns the normal behaviour
of an ECG while detecting abnormal events, tested in two different settings:
detection of different types of noise, and; symptomatic events caused by different pathologies.
Two algorithms were developed for noise detection, using an autoencoder and
Convolutional Neural Networks (CNN), reaching accuracies of 98,18% for the binary
class model and 70,74% for the multi-class model, which is able to discern between base
wandering, muscle artifact and electrode motion noise. As for the arrhythmia detection
algorithm was developed using an autoencoder and Recurrent Neural Networks with
Gated Recurrent Units (GRU) architecture. With an accuracy of 56,85% and an average
sensitivity of 61.13%, compared to an average sensitivity of 75.22% for a 12 class
model developed by Hannun et al. The model detects 7 classes: normal sinus rhythm,
paced rhythm, ventricular bigeminy, sinus bradycardia, atrial fibrillation, atrial flutter
and pre-excitation.
It was concluded that the process of learning the machine learned features of the
normal ECG signal, currently sacrifices the accuracy for higher generalization. It performs
better at discriminating the presence of abnormal events in ECG than classifying different
types of events. In the future, these algorithms could represent a huge contribution in
signal acquisition for wearables and the study of pathologies visible in not only in ECG,
but also EMG and respiratory signals, especially applied to active learning.
Descrição
Palavras-chave
Electrocardiogram Signal Processing Deep Learning Artificial Intelligence Arrhythmia Detection Noise Detection