Deep Learning Approach based on 1D Convolutional Neural Networks for
predicting Sudden Cardiac Arrest
Abstract
Sudden cardiac arrest (SCA) is a serious heart problem which occur
without any apparent symptoms and warning. SCA leads to a large amount
of fatality. Hence, predicting the occurrence of SCA is very important.
Current traditional methods for predicting a ventricular fibrillation
(VF) onset requires a considerably longer-monitoring time of patients,
resulting in unprecedented complications. The emerging technology, in
particular machine learning techniques have gained popularity due to the
effectiveness. However, most of existing techniques heavily rely on a
set of hand-engineered features which may results in poor generalization
when presented out of distribution patient data. On the other hand,
existing deep learning approaches rely heavily on large training data
which is not available publicly. In this study, we proposed a deep
learning approach based on 1D convolutional neural network trained with
discrete Fourier transform (DFT) features from raw electrocardiogram
(ECG) signals. As demonstrated in the results, our method can predict
accurately around 90 minutes before the onset of SCA with an accuracy of
96%. The advance prediction might save many lives. That is, optimized
deep learning models offer better generalization than hand-crafted
features while detecting long-term signal relations.