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Survey on Feature Extraction and Applications of Biosignals

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Machine Learning for Health Informatics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9605))

Abstract

Biosignals have become an important indicator not only for medical diagnosis and subsequent therapy, but also passive health monitoring. Extracting meaningful features from biosignals can help people understand the human functional state, so that upcoming harmful symptoms or diseases can be alleviated or avoided. There are two main approaches commonly used to derive useful features from biosignals, which are hand-engineering and deep learning. The majority of the research in this field focuses on hand-engineering features, which require domain-specific experts to design algorithms to extract meaningful features. In the last years, several studies have employed deep learning to automatically learn features from raw biosignals to make feature extraction algorithms less dependent on humans. These studies have also demonstrated promising results in a variety of biosignal applications. In this survey, we review different types of biosignals and the main approaches to extract features from the signal in the context of biomedical applications. We also discuss challenges and limitations of the existing approaches, and possible future research.

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  1. William Penney Laboratory, Data Science Institute, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK

    Akara Supratak, Chao Wu, Hao Dong, Kai Sun & Yike Guo

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    Andreas Holzinger

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Supratak, A., Wu, C., Dong, H., Sun, K., Guo, Y. (2016). Survey on Feature Extraction and Applications of Biosignals. In: Holzinger, A. (eds) Machine Learning for Health Informatics. Lecture Notes in Computer Science(), vol 9605. Springer, Cham. https://doi.org/10.1007/978-3-319-50478-0_8

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