Indexing ECG for Integrated Health Social Networks Predicting Keywords from ECG to Access Online Information

Abstract

Health Social Networks (HSN) provide rich medical knowledge bases that are scalable and sustainable, while IoT provides non-invasive, pervasive, and low-cost methods to collect patient data. However, receiving relevant information from HSN is time consuming and challenging for users, such as searching for the right relevant information using keywords and filtering. On the other hand, healthcare IoT has limited access to the vast medical knowledge bases, such as HSN, to interpret the collected data. To address these challenges, we propose Keyword-based Integrated HSN of Things (KIHoT), an approach that combines the strengths of both HSNs and IoT to overcome their limitations. In this method, data (biosignals) collected via IoT devices are converted to human readable keywords using word embedding vector features and CNN (Convolutional Neural Network) predictors. The CNN predictors are trained to predict keywords that individuals search within an HSN to extract relevant information of the given biosignals. Those keywords are encoded as word embedding for searching relevant information. KIHoT utilizes contrast learning techniques to extract latent feature representations of electrocardiogram (ECG) signals, which are then used to predict disease-related keywords. The proposed method was evaluated using 11,936 ECG signals from patients with heart disease and achieved an accuracy of 98% for disease prediction. Our results suggest that KIHoT can effectively extract relevant information from HSN portals, making it easier for researchers and clinicians to access valuable medical knowledge.