A dissimilarity-based approach to automatic classification of biosignal modalities

Over the last years, pervasive wearable technology has spread to people's daily lives, unobtrusively acquiring large amounts of data. Such devices contain biomedical sensors, prone to contribute for the improvement of the user's quality of life through artificial intelligence algorithms (e.g. health monitoring and emotion recognition). Physiological signals are the basis of such applications, and critical problems are data (un)labeling and incorrect metadata about the source. We propose a framework for the automatic identification of the type of physiological data source, namely Respiration, Electrocardiography, Electrodermal Activity, and Blood Volume Pulse data through the application of Supervised Learning on different representation spaces (feature-based and dissimilarity-based), in both an Online and Offline setting. We build our model through a comprehensive study of (1) Supervised Learning classifiers; (2) Similarity metrics; (3) Data representation; and lastly, (4) Sample aggregation techniques for the creation of the prototypes that will translate the data into the dissimilarity-based space. We explore the aforementioned techniques on two unexplored databases. The experimental results led to accuracies superior to 92% for the online setting, and 96% for the offline setting, attaining competitive results with the current state of the art. Our work paves the way to the development of systems capable of automatically identifying sensor types and subsequently applying the most appropriate data processing, analysis and classification workflows. (c) 2021 Elsevier B.V. All rights reserved.