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A Parametric Lossy Compression Techniques for Biosignals: A Review

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Abstract

Rapid growth in wireless communication and sensor based technology has contributed to the integration of wearable device with internet technology which supports wireless body area network (WBAN) enabled tele-health monitoring applications. Wearable technology promotes long-term monitoring of biosignals for individuals suffering from chronic conditions like cardiovascular, sleep disorder, mood disorder, epileptic seizure etc. The hardware used in these wearables are miniaturized in nature and resource constrained. This resource constrained wearable devices have to collect, analyze and transmit large amount of data with limited power consumption. Hence, the wearable device must have faster computational speed and least communication cost. In order to address these issues, various light weight lossy compression schemes based on parametric method are advocated so far to reduce the size of the data. The acquired data has been compressed at once where they are acquired (on node processing) to support the battery life for long term monitoring. This article reviews parametric method based two major paradigms Compressive Sensing and Autoencoder techniques for biosignal compression. The biosignals that are acquired from surface-mounted on body communicating wearable IoT devices are considered for this review. And this study presents a complete investigation of compression techniques of these Biosignals.

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We thank Almighty God and greatly appreciate the commentary assistance of the editor & the anonymous reviewers that has helped us to improve the overall quality of our manuscript.

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  1. Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, India

    Evangelin Dasan & Rajakumar Gnanaraj

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Dasan, E., Gnanaraj, R. A Parametric Lossy Compression Techniques for Biosignals: A Review. Wireless Pers Commun 128, 507–536 (2023). https://doi.org/10.1007/s11277-022-09965-8

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