Generalizable Semi-Supervised Learning Strategies for Multiple Learning Tasks Using 1-D Biomedical Signals

Abstract

Progress in the sensors field has enabled collection of biomedical signal data, such as photoplethysmography (PPG), electrocardiogram (ECG), and electroencephalogram (EEG), allowing for application of supervised machine learning techniques such as convolutional neural networks (CNN). However, the cost associated with annotating these biomedical signals is high and prevents the widespread use of such techniques. To address the challenges of generating a large labeled dataset, we adapt and apply semi-supervised learning (SSL) frameworks to a new problem setting, i.e., artifact detection in PPG signal and verified its generalizability in ECG and EEG as well. Our proposed framework is able to leverage unlabeled data to achieve similar PPG artifact detection performance obtained by fully supervised learning approach using only 75 labeled samples, or 0.5\% of the available labeled data.