Privacy concerns in healthcare have gained interest recently via GDPR, with a rising need for privacy-preserving data collection methods that keep personal information hidden in otherwise usable data. Sometimes data needs to be encrypted for several authentication levels, where a semi-authorized user gains access to data stripped of personal or sensitive information, while a fullyauthorized user can recover the full signal. In this paper, we propose a compressive sensing based multi-level encryption to ECG signals to mask possible heartbeat anomalies from semi-authorized users, while preserving the beat structure for heart rate monitoring. Masking is performed both in time and frequency domains. Masking effectiveness is validated using 1D convolutional neural networks for heartbeat anomaly classification, while masked signal usefulness is validated comparing heartbeat detection accuracy between masked and recovered signals. The proposed multi-level encryption method can decrease classification accuracy of heartbeat anomalies by up to 50%, while maintaining a fairly high R-peak detection accuracy.

Privacy concerns in healthcare have gained interest recently via GDPR, with a rising need for privacy-preserving data collection methods that keep personal information hidden in otherwise usable data. Sometimes data needs to be encrypted for several authentication levels, where a semi-authorized user gains access to data stripped of personal or sensitive information, while a fullyauthorized user can recover the full signal. In this paper, we propose a compressive sensing based multi-level encryption to ECG signals to mask possible heartbeat anomalies from semi-authorized users, while preserving the beat structure for heart rate monitoring. Masking is performed both in time and frequency domains. Masking effectiveness is validated using 1D convolutional neural networks for heartbeat anomaly classification, while masked signal usefulness is validated comparing heartbeat detection accuracy between masked and recovered signals. The proposed multi-level encryption method can decrease classification accuracy of heartbeat anomalies by up to 50%, while maintaining a fairly high R-peak detection accuracy.

Drift or downstream dispersal is a fundamental process in the life cycle of many riverine organisms. In the face of rapidly declining freshwater biodiversity, there is a need to enhance our capacity to study the drift of riverine organisms, by overcoming the limitations of traditional labour‐intensive sampling methods that result in data of low temporal and spatial resolution. To address this need, we developed a new technology, the Riverine Organism Drift Imager (RODI), which combines in situ imaging with machine‐learning classification. This technique expands on the traditional methodology by replacing the collection cup of a drift net with a camera system that continuously images riverine organisms as they drift through the device. After being imaged, organisms are released into the environment unharmed. A machine‐learning classifier is used after field sampling to identify drifting organisms. Therefore, RODI provides a non‐invasive sampling method that can quantify organism drift at unprecedented temporal resolution. Multiple deployments have served to validate the performance of the technology in the field. In its current implementation, images are captured continuously for 1.5 h at 50 frames per second. We demonstrate that the quality of the resulting images enables a convolutional neural network classifier to identify organisms to the family level. The weighted F1 score, a metric for the performance of the classifier, was 94%, based on training and testing on a field‐collected dataset consisting of 4598 images of 285 organisms belonging to seven classes (one species, five families and one order). In conclusion, this work provides a proof of concept, demonstrating the viability of the deployment of RODI as an automated, in situ organism drift sampler. This novel approach offers the possibility to advance our fundamental understanding of the drift of riverine organisms and how this is affected by human impacts in natural streams while, at the same time, can serve as a cost‐effective tool for biodiversity monitoring.