Multi-Channel Front-End for Electrochemical Sensing of Metabolites, Drugs, and Electrolytes

A multi-channel front-end for electrochemical sensing is presented. It consists of a multiplexed four-channel readout interface supporting amperometric, voltammetric, and potentiometric measurements. The electronic interface is co-designed according to the target biomarker specifications, and exhibits excellent linearity in both current and voltage sensing. The sensing front-end is characterized with lactate, paracetamol, and lithium sensing, yielding sensitivity of 1.2 ± 0.3 μ A/mM, 69.6 ± 2 nA/μM, and 55.6 mV/decade, respectively. These performances are comparable with the ones obtained with a bulky commercial Autolab potentiostat. Moreover, the limit of detection achieved are of 37 ± 8 μM, 2.1 ± 1.22 μM, and 11 ± 3.5 μM, respectively, for the aforementioned sensors. These values are more than one order of magnitude lower than the relevant detection range. This successful characterization demonstrates the ability of the proposed system to monitor, in a broader sense, metabolites, drugs, and electrolytes. The programmability, versatility and portability of the front-end interface paves the way for a continuous monitoring of different families of biomarkers, suitable for advanced healthcare diagnosis and wearable physiology.

Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum With Machine Learning

Simone Aiassa, Sandro Carrara, Danilo Demarchi, Mandresy Ivan Ny Hanitra

Continuous monitoring of anaesthetics infusion is demanded by anaesthesiologists to help in defining personalized dose, hence reducing risks and side effects. We propose the first piece of technology tailored explicitly to close the loop between anaesthesiologist and patient with continuous drug monitoring. Direct detection of drugs is achieved with electrochemical techniques, and several options are present in literature to measure propofol (widely used anaesthetics). Still, the sensors proposed do not enable in-situ detection, they do not provide this information continuously, and they are based on bulky and costly lab equipment. In this paper, we present a novel smart pen-shaped electronic system for continuous monitoring of propofol in human serum. The system consists of a needle-shaped sensor, a quasi digital front-end, a smart machine learning data processing, in a single wireless battery-operated embedded device featuring Bluetooth Low Energy (BLE) communication. The system has been tested and characterized in real, undiluted human serum, at 37 ∘ C. The device features a limit of detection of 3.8 μ M, meeting the requirement of the target application, with an electronics system 59% smaller and 81% less power consuming w.r.t. the state-of-the-art, using a smart machine learning classification for data processing, which guarantees up to twenty continuous measure.

2021

Distributed sensor failure detection in sensor networks

Pascal Frossard, Nikolaos Thomos, Tamara Tosic

We investigate the problem of distributed sensors' failure detection in networks with a small number of defective sensors, whose measurements differ significantly from neighboring sensor measurements. Defective sensors are represented by non-zero values in binary sparse signals. We build on the sparse nature of the binary sensor failure signals and propose a new distributed detection algorithm based on Group Testing (GT). The distributed GT algorithm estimates the set of defective sensors from a small number of linearly independent binary messages exchanged by the sensors. The distributed GT algorithm uses a low complexity distance decoder that is robust to noisy messages. We first consider networks with only one defective sensor and determine the minimal number of linearly independent messages needed for detection of the defective sensor with high probability. We then extend our study to the detection of multiple defective sensors by modifying appropriately the message exchange protocol and the decoding procedure. We show through experimentation that, for small and medium sized networks, the number of messages required for successful detection is actually smaller than the minimal number computed in the analysis. Simulations demonstrate that the proposed method outperforms methods based on random walk measurements collection in terms of detection performance and convergence rate. Finally, the proposed method is resilient to network dynamics due to the effective gossip-based message dissemination protocol.

2011

Distributed sensor failure detection in sensor networks

Pascal Frossard, Nikolaos Thomos, Tamara Tosic

We investigate the problem of distributed sensors' failure detection in networks with a small number of defective sensors, whose measurements differ significantly from neighbouring sensor measurements. We build on the sparse nature of the binary sensor failure signals and propose a new distributed detection algorithm based on Group Testing. The distributed GT algorithm estimates the set of defective sensors by a low complexity distance decoder from a small number of linearly independent binary messages exchanged by the sensors. We first consider networks with one defective sensor and determine the minimal number of linearly independent messages needed for detection of the defective sensor with high probability. We then extend our study to the detection of multiple defective sensors by modifying appropriately the message exchange protocol and the decoding procedure. We show through experimentation that, for small and medium sized networks, the number of messages required for successful detection is actually smaller than the minimal number computed in the analysis. Finally, simulations demonstrate that the proposed method outperforms methods based on random walks in terms of detection performance and convergence rate.

Elsevier Science Bv2013