IEEE Sensors Letters最新文献

{"title":"Optimized Quantification of Multiple Drug Concentrations by WeightedMSE With Machine Learning on Electrochemical Sensor","authors":"Tatsunori Matsumoto;Lin Du;Francesca Rodino;Yann Thoma;Chinthaka Premachandra;Sandro Carrara","doi":"10.1109/LSENS.2024.3452009","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3452009","url":null,"abstract":"Quantification of multiple drugs is of great importance and urgently needed in therapeutic drug monitoring (TDM) and personalized therapy. Especially, based on cyclic voltammograms (CVs) obtained by electrochemical sensors, the use of artificial neural networks (ANNs) has been widely attempted in the accurate quantification of drug concentrations, enabling the development of point-of-care and potentially system-level wearable devices. However, most of the work only considers the accuracy of how the predicted value is close to the actual value, which does not consider whether the predicted drug concentration is underestimated. In practical drug quantification, potential toxicity due to overexposure with underestimated quantification can lead to endangering the patient's body. Therefore, avoiding underestimating the concentration of drugs based on existing quantification models is required and necessary to optimize the conventional loss function at the output stage of ANN. In this letter, a novel loss function based on mean squared error (MSE), WeightedMSE, is proposed for avoiding underestimated quantification. It can be changed flexibly by adjusting parameters in order to adapt the acceptable overestimation range corresponding to the different types of drugs. A simulated dataset and a real dataset of etoposide and methotrexate are used as drug models, demonstrating that the proposed method can avoid underestimation in predicted values by over 98% in quantifying the concentration of multiple drugs and showing significant effectiveness for the development of point-of-care and wearable monitoring systems.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Microfluidic System for Capacitive Detection Via Magnetophoretic Separation of Malaria-Infected Red Blood Cells","authors":"Amirmahdi Tavakolidakhrabadi;Théo Domange;Clémentine Naım;Francesca Rodino;Ali Meimandi;Cédric Bessire;Sandro Carrara","doi":"10.1109/LSENS.2024.3451238","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3451238","url":null,"abstract":"Malaria continues to pose a significant global health challenge, with substantial impediments arising from the need for more reliable, effective, and economically viable diagnostic tools, particularly for early detection. This research introduces a novel microfluidic device designed for malaria-diagnostics through the detection of hemozoin (Hz), a prevalent biomarker for the disease. Our methodology involves the collection of a minimal blood sample, which is subsequently processed through a microfluidic system. This system exploits the paramagnetic properties of Hz to isolate infected blood cells using magnetophoretic separation. The detection process employs a relative capacitive measurement technique capable of quantifying Hz concentrations ranging from 417 \u0000<inline-formula><tex-math>$fM$</tex-math></inline-formula>\u0000 to 17 \u0000<inline-formula><tex-math>$pM$</tex-math></inline-formula>\u0000, facilitating and enhancing malaria diagnosis. Simulations results confirm the efficacy of our device in providing a rapid, cost-effective, and readily producible diagnostic solution. This research demonstrates the potential of integrating advanced microfluidic technology and sensitive detection systems into a compact, portable unit, offering significant improvements over existing malaria diagnostic tools.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Protein Enrichment and Detection Microfluidic Platform Based on Ion Concentration Polarization (ICP) and Electrochemical Impedance Spectroscopy (EIS) Techniques","authors":"Chi Tran Nhu;Loc Do Quang;Chun-Ping Jen;Trinh Chu Duc;Tung Bui Thanh","doi":"10.1109/LSENS.2024.3450498","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3450498","url":null,"abstract":"In this letter, a protein enrichment microfluidic platform with an integrated bioelectrochemical sensing system has been proposed and demonstrated for the first time, enabling protein preconcentration and detection. The proposed chip was composed of an electrochemical biosensor integrated into a preconcentrator with a dual-gate structure. The bioelectrochemical sensor had three electrodes, including working, counter, and reference electrodes. The working and counter electrodes were made of gold, while the reference electrode was made of Ag/AgCl. The preconcentrator was designed with three microchannels, with a main channel electrically connected to two subchannels through Nafion ion-selective membranes. The chip was fabricated using photolithography and soft lithography techniques. Ag and AgCl layers were deposited on the gold electrode to form the reference electrode. The Nafion membrane was created using the microflow patterning technique. Then, the gold electrode surface was modified to attach anti-albumin antibodies (anti-bovine serum albumin—anti-BSA) and form the biosensor. Bovine serum albumin–fluorescein isothiocyanate conjugate was specifically bound to anti-BSA through the protein preconcentration process at the biosensor area. The experimental results show that bovine serum albumin (BSA) proteins were concentrated successfully after applying potentials to the ends of the microchannels. The protein concentration increased 25 times after 80 s. The change in the electrochemical impedance spectroscopy (EIS) signal demonstrates the specific binding between BSA and anti-BSA on the electrode surface. In addition, the results also show the significant effectiveness of the protein preconcentration process for improving the binding ability and electrical signal amplification of the bioelectrochemical sensor. With the obtained results, a lab-on-a-chip system can be developed to quantify protein concentration and diagnose some cancer diseases.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142152115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drone Inspection System Based on the Electrochemical Impedance Detector by Dengue NS1 Biomarkers in Water Environments","authors":"Sung-Lin Tsai;Jiunn-Jye Wey;Szu-Chia Lai;You-Qian Lin;Chiao-Jou Chang;Pao-Cheng Huang","doi":"10.1109/LSENS.2024.3449342","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3449342","url":null,"abstract":"Dengue viruses are currently one of the deadliest mosquito-borne infectious diseases; there is no effective treatment, and a vaccine for dengue fever is not yet available. Therefore, monitoring and preventing virus transmission is currently the most effective controlling method. This study focuses on environmental traces of dengue transmission, and a low-cost portable drone system for dengue virus inspection based on water sources is presented, which is equipped with a drone, a water collector, a microfluidic chip, and an electrochemical impedance converter using an Arduino development broad and an Analog Devices AD5934 chip. Water samples are carried back by a drone with a water collector, which can be measured and analyzed outdoors, that is not required to be brought back to the laboratory. The concentration of 10- and 20-μg/cc dengue nonstructural protein 1 can be identified by impedance magnitude in the microfluidic chip using the bead-based biomarker technology. The presented novel device using a drone-based collector with a low-cost electrochemical impedance sensor may have great potential for the creation of dengue maps, becoming a valuable technique that is beneficial to trace dengue transmission. In the future, it may quickly identify differences in impedance spectroscopy between numerous viruses for environmental investigation.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Signal Quality-Aware Frequency Demodulation-Based ECG-Derived Respiration Rate Estimation Method With Reduced False Alarms","authors":"Aditya Nalwaya;M. Sabarimalai Manikandan;Ram Bilas Pachori","doi":"10.1109/LSENS.2024.3449328","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3449328","url":null,"abstract":"In this letter, we present an automated signal quality-aware frequency demodulation (FD)-based electrocardiogram (ECG)-derived respiration rate (FD-ECG-derived RR) estimation method with reduced false alarms under noisy ECG signals, which are unavoidable in resting and ambulatory health monitoring applications. The proposed FD-ECG-derived RR estimation method includes three major steps of signal quality checking to discard noisy ECG signals, respiratory-induced frequency variation (RIFV) waveform extraction using a frequency demodulation envelope detector by determining peaks of the derivative ECG waveform using a simple R-peak detector, and respiration rate estimation using the Fourier magnitude spectrum of the extracted RIFV waveform. On the standard Capnobase and BIDMC databases, the proposed FD-ECG-derived RR estimation method provides promising results with mean absolute error values of 5.01 and 5.37 breaths/min, respectively. The signal quality-aware RR estimation method used can reduce false alarm rate of 84.85\u0000<inline-formula><tex-math>${%}$</tex-math></inline-formula>\u0000 by discarding noisy ECG signals with quality assessment accuracy of 85.25\u0000<inline-formula><tex-math>${%}$</tex-math></inline-formula>\u0000. The proposed simplistic method having lightweight signal processing approaches makes it suitable for real-time health monitoring applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ALS Detection Framework Based on Automatic Singular Spectrum Analysis and Quantum Convolutional Neural Network From EMG Signals","authors":"Kiran Kumar Makam;Vivek Kumar Singh;Ram Bilas Pachori","doi":"10.1109/LSENS.2024.3449369","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3449369","url":null,"abstract":"Electromyogram (EMG) signals are recordings of the electrical activity in muscles, which are studied due to their informative nature regarding neuromuscular disorders. Analysis of EMG signals is invaluable for identifying various neuromuscular conditions. In this letter, an automatic singular spectrum analysis (Auto-SSA) and quantum convolutional neural network (QCNN)-based framework is proposed for the detection of amyotrophic lateral sclerosis (ALS) using EMG signals. The Auto-SSA effectively decomposes the EMG signals into reconstructed components, from which the particle swarm optimization extracts the most significant features. The QCNN classifies the extracted features for efficient ALS detection. The proposed framework outperforms the compared state-of-the-art ALS detection frameworks, achieving a testing accuracy of 98.50%. With the obtained performance, the proposed framework could be a valuable diagnostic tool for ALS neuromuscular conditions.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferior Myocardial Infarction Detection From Lead II of ECG: A Gramian Angular Field-Based 2D-CNN Approach","authors":"Asim Yousuf;Rehan Hafiz;Saqib Riaz;Muhammad Farooq;Kashif Riaz;Muhammad Mahboob Ur Rahman","doi":"10.1109/LSENS.2024.3450176","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3450176","url":null,"abstract":"This letter presents a novel method for inferior myocardial infarction (MI) detection using lead II of electrocardiogram (ECG). We evaluate our proposed method on a public dataset, namely, Physikalisch Technische Bundesanstalt (PTB) ECG dataset from PhysioNet. Under our proposed method, we first clean the noisy ECG signals using db4 wavelet, followed by an R-peak detection algorithm to segment the ECG signals into beats. We then translate the ECG timeseries dataset to an equivalent dataset of grayscale images using Gramian angular summation field (GASF) and Gramian angular difference field (GADF) operations. Subsequently, the grayscale images are fed into a custom 2-D convolutional neural network (CNN), which efficiently differentiates between a healthy subject and a subject with MI. Our proposed approach achieves an average classification accuracy of 99.68%, 99.80%, 99.82%, and 99.84% under GASF dataset with noise and baseline wander, GADF dataset with noise and baseline wander, GASF dataset with noise and baseline wander removed, and GADF dataset with noise and baseline wander removed, respectively. Most importantly, this work opens the floor for innovation in wearable devices to measure lead II ECG (e.g., by a smart watch worn on right wrist, along with a smart patch on left leg), in order to do accurate, real-time, and early detection of inferior wall MI.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement Offset Fault Detection Logic for PMSM Position Sensor","authors":"Hafiz Ahmed","doi":"10.1109/LSENS.2024.3447897","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3447897","url":null,"abstract":"High-performance control of permanent magnet synchronous motors (PMSMs) demands precise position information, but nonidealities and signal conversion issues may introduce a dc offset (DCO) in the motor position sensor output. This offset significantly degrades drive performance and efficiency. To address this, conventional state-machine-type algorithms adapt control bandwidths based on fault types. This letter introduces an intuitive decision logic (DL) for both forward and reverse motor operations, offering simplicity and ease of implementation. In contrast to complex signal processing methods, such as wavelet and Fourier transformation and neural network, the proposed lightweight DL can be efficiently implemented in a wide range of embedded devices. Experimental results using an industrial-grade PMSM servo motor across diverse operating conditions validate the efficacy of the proposed DL over long short-term memory network-based counterpart.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Sensors Letters Publication Information","authors":"","doi":"10.1109/LSENS.2024.3442591","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3442591","url":null,"abstract":"","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 8","pages":"C2-C2"},"PeriodicalIF":2.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Sensors Letters Subject Categories for Article Numbering Information","authors":"","doi":"10.1109/LSENS.2024.3442595","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3442595","url":null,"abstract":"","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 8","pages":"1-1"},"PeriodicalIF":2.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}