Paper: Estimating Heart Rate and Detecting Feeding Events of Fish Using an Implantable Biologger

February 18th, 2020

Monitoring of physiology and behavior of marine animals living undisturbed in their natural habitats can provide valuable data on their well-being and response to environmental stressors. We focus on detection of feeding of predatory fish using implantable biologgers that record electrocardiogram (ECG) signals.

We propose a novel processing pipeline for resource-constrained embedded systems that can infer higher-level information, such as heart-rate and feeding events, from the ECG signals. Our main contribution is a lightweight change-detection algorithm, that can reliably detect fish feeding in noisy heart-rate data based on unique statistical  properties of feeding-induced changes in heart-rate.

We evaluate our approach using an in-house biologger (produced by our colleagues in O&A business unit) that we surgically implant in twelve coral trouts over a period of ten weeks. We show that our signal processing pipeline performs well with noisy ECG signals overall. Specifically, our heart-rate estimation algorithm achieves errors of less than one beat per minute even in scenarios where popular algorithms used by domain scientists perform poorly.

Furthermore, our feeding detection algorithm achieves good accuracy and matches the performance of state-of-the-art algorithms while requiring significantly less memory and computational resources. This work is an important first step towards long-term monitoring of high-level condition and health of marine animals in the wild.

Figure 1: Use of implantable biosensor for monitoring fish energetics in marine ecosystems.

Y. Shen, R. Arablouei, F. D. Hoog, J. Malan, J. Sharp, S. Shouri, T. D. Clark, C. Lefevre, F. Kroon, A. Severati, and B. Kusy. Estimating Heart Rate and Detecting Feeding Events of Fish Using an Implantable Biologger.  International Conference on Information Processing in Sensor Networks (IPSN) 2020.

This work was done in collaboration between CSIRO Data61, CSIRO Oceans & Atmosphere, AIMS, and Deakin university researchers and was supported and sponsored by CSIRO’s Active Integrated Matter Future Science Platform.

Download the full paper here.

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