Deep Transfer Learning Applied to Time-series Classification for Predicting Heart Failure Worsening Using Electrocardiography Public
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Computational ECG (electrocardiogram) analysis enables accurate and faster diagnosis and early prediction of heart failure related symptoms (heart failure worsening). Machine learning, particularly deep learning, has been applied for ECG data successfully. The previous applications, however, either mainly focused on classifying occurrent, known patterns of on-going heart failure or heart failure related diseases such arrhythmia, which have undesirable predictability beforehand, or emphasizing on data from pre-processed public database data. In this dissertation, we developed an approach, however, does not fully capitalize on the potential of deep learning, which directly learns important features from raw input data without relying on a priori knowledge. Here, we present a deep transfer learning pipeline which combines an image-based pretrained deep neural network model with manifold learning to predict the precursors of heart failure (heart failure-worsening and recurrent heart failure related re-hospitalization) using raw ECG time series from wearable devices. In this dissertation, we used the unprocessed real-life ECG data from the SENTINEL-HF study by Dovancescu, et al. to predict the precursors of heart failure worsening. To extract rich features from ECG time series, we took a deep transfer learning approach where 1D time-series of five heartbeats were transformed to 2D images by Gramian Angular Summation Field (GASF) and then the pretrained models, VGG19 were used for feature extraction. Then, we applied UMAP (Uniform Manifold Approximation and Projection) to capture the manifold of the standardized feature space and reduce the dimension, followed by SVM (Support Vector Machine) training. Using our pipeline, we demonstrated that our classifier was able to predict heart failure worsening with 92.1% accuracy, 92.9% precision, 92.6% recall and F1 score of 0.93 bypassing the detection of known abnormal ECG patterns. In conclusion, we demonstrate the feasibility of early alerts of heart failure by predicting the precursor of heart failure worsening based on raw ECG signals. We expected that our approached provided an innovative method to assess the recovery and successfulness for the treatment patient received during the first hospitalization, to predict whether recurrent heart failure is likely to occur, and to evaluate whether the patient should be discharged.
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