Continuous monitoring of propofol in human serum with fouling compensation by support vector classifier

Aiassa, Simone; Hanitra, Ivan Ny; Sandri, Gabriele; Totu, Tiberiu; Grassi, Francesco; Criscuolo, Francesca; Micheli, Giovanni De; Carrara, Sandro; Demarchi, Danilo

doi:10.1016/j.bios.2020.112666

Cited by 19 publications

(10 citation statements)

References 31 publications

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“…In another work, Aiassa et al [ 32 ] developed a new method for electrochemical sensing of propofol with compensation of the fouling effect through the ML model, as shown in Fig. 2 A .…”

Section: Supervised Models Solving Specific Challenges In Chemical An...mentioning

confidence: 99%

“…Experimental setup and the representation of a propofol molecule (1), scheme of the proposed ML approach that assured accurate determinations in spite of the electrode fouling (2), and the confusion matrices for the results in undiluted human serum with the standard linear analytical curve (3) and with the ML-based model (4). Adapted from [ 32 ], Copyright 2022 with permission from ACS. B Bifunctional metal mesh working as gas diffusion membrane and electrode for the accurate electrochemical quantification of ethanol in liquid samples (donor) by overcoming matrix effect–related interferences.…”

Section: Supervised Models Solving Specific Challenges In Chemical An...mentioning

confidence: 99%

“…As noted in the two prior works [7,32] and following in this section, supervised ML models are a powerful strategy to afford direct analyses as the use of experimental methods to inhibit the interfering issue is avoided and ML-fitted algorithms can be automatically performed, e.g., in mobile phones toward the development of sample-to-answer fashions. The latter facilitates detection at the point of need because no data treatment or interpretation by the user is required.…”

Section: Electrode Foulingmentioning

confidence: 99%

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Machine learning toward high-performance electrochemical sensors

et al. 2023

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The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications. Graphical Abstract

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“…In another work, Aiassa et al [ 32 ] developed a new method for electrochemical sensing of propofol with compensation of the fouling effect through the ML model, as shown in Fig. 2 A .…”

Section: Supervised Models Solving Specific Challenges In Chemical An...mentioning

confidence: 99%

Section: Supervised Models Solving Specific Challenges In Chemical An...mentioning

confidence: 99%

Section: Electrode Foulingmentioning

confidence: 99%

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Machine learning toward high-performance electrochemical sensors

et al. 2023

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“…An external computer, running custom software, and connected to a monitor, processes the measurement to provide smart and easy-to-read information to the anesthesiologist. This software contains a graphical user interface and a Support Vector Machine (SVM) classifier, taking advantages of [39], [40].…”

Section: Ml-based Classifiermentioning

confidence: 99%

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

Aiassa

Ros

Hanitra

et al. 2021

IEEE Trans. Biomed. Circuits Syst.

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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 batteryoperated 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 µM, 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.

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“…ML has been previously used in electrochemical biosensors, although its use has still been limited [13]. Aiassa et al [14] and Rivera et al [15] used ML to detect a single analyte to reduce the effect of background noise. Qian et al [16], used statistical machine learning to quantify E.Coli concentration from water samples.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-based approach for Simultaneous Detection of Interfering Analytes in Electrochemical Nanobiosensors

Jain,

Verma,

Dutta

2024

Preprint

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Electrochemical biosensors can be used to detect analytes of importance precisely. These sensors generate rapid and accurate electrical signals that reveal the presence and concentration of the targeted analyte. Detecting multiple analytes simultaneously with an electrochemical biosensor is advantageous. It provides cost and time efficiency, multiplexing capability, and flexibility, making it valuable in diverse applications such as medical diagnostics, environmental monitoring, and industrial processes. However, simultaneous detection of analytes may suffer from the problem of interference. The interference effect causes the signal of an analyte at a particular concentration to deviate from the expected one. We observe a similar effect in the simultaneous detection of Folic Acid and Uric Acid using a nanomaterial-based electrochemical sensor. To address this effect, we propose a machine learning (ML) approach. ML algorithms handle complex interactions by autonomously identifying patterns, dependencies, and nonlinear relationships within data, enabling it to make predictions and decisions in intricate and dynamic scenarios. Our approach can be generalised to any two analytes showing interference and would scale well to interference between multiple analytes. We test several regression algorithms and compare their performance to the standard calibration plot method. As compared to the standard method our approach shows a 4.49 µM decrease in concentration prediction error.

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Continuous monitoring of propofol in human serum with fouling compensation by support vector classifier

Cited by 19 publications

References 31 publications

Machine learning toward high-performance electrochemical sensors

Machine learning toward high-performance electrochemical sensors

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

A Machine Learning-based approach for Simultaneous Detection of Interfering Analytes in Electrochemical Nanobiosensors

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