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Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix
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Orientador(es)
Resumo(s)
Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals’ feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.
Descrição
The APC was funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen.
Hanse Wissenschaftskolleg - Institute for Advanced Study: BRAIN Program.
Publisher Copyright:
© 2022 by the authors.
Palavras-chave
automatic segmentation biosignal processing clustering data mining human activity recognition information retrieval novelty function self-similarity matrix unsupervised segmentation Analytical Chemistry Biotechnology Biomedical Engineering Instrumentation Engineering (miscellaneous) Clinical Biochemistry