2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)最新文献

Point-of-Care Microchip Electrophoresis Test for Glycosylated Hemoglobin 糖化血红蛋白的即时微芯片电泳检测

2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) Pub Date : 2022-03-10 DOI: 10.1109/HI-POCT54491.2022.9744066

Zoe Sekyonda, A. Fraiwan, R. An, U. Gurkan

引用次数: 2

CoughNet-V2: A Scalable Multimodal DNN Framework for Point-of-Care Edge Devices to Detect Symptomatic COVID-19 Cough CoughNet-V2:用于检测症状性COVID-19咳嗽的护理点边缘设备的可扩展多模态DNN框架

2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) Pub Date : 2022-03-10 DOI: 10.1109/HI-POCT54491.2022.9744064

Hasib-Al Rashid, Mohammad M. Sajadi, T. Mohsenin

{"title":"CoughNet-V2: A Scalable Multimodal DNN Framework for Point-of-Care Edge Devices to Detect Symptomatic COVID-19 Cough","authors":"Hasib-Al Rashid, Mohammad M. Sajadi, T. Mohsenin","doi":"10.1109/HI-POCT54491.2022.9744064","DOIUrl":"https://doi.org/10.1109/HI-POCT54491.2022.9744064","url":null,"abstract":"With the emergence of COVID-19 pandemic, new attention has been given to different acoustic bio-markers of the respiratory disorders. Deep Neural Network (DNN) has become very popular with the audio classification task due to its impressive performance for speech detection, audio event classification etc. This paper presents CoughNet-V2 - a scalable multimodal DNN framework to detect symptomatic COVID-19 cough. The framework was designed to be implemented on point-of-care edge devices to help the doctors at pre-screening stage for COVID-19 detection. A crowd-sourced multimodal data resource which contains subjects’ cough audio along with other relevant medical information was used to design the CoughNet-V2 framework. CoughNet-V2 shows multimodal integration of cough audio along with medical records improves the classification performance than that of any unimodal frameworks. Proposed CoughNet-V2 achieved an area-under-curve (AUC) of 88.9% for the binary classification task of symptomatic COVID-19 cough detection. Finally, measurement of the deployment attributes of the CoughNet-V2 model onto processing components of an NVIDIA TX2 development board is presented as a proposition to bring the healthcare system to consumers’ fingertips.Clinical relevance—CoughNet-V2 will help medical practitioners to asses whether the patients need intensive medical help without physically interacting with them.","PeriodicalId":283503,"journal":{"name":"2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131305775","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}

引用次数: 5

Smartphone IoT-Based Point of Care Method for Arrhythmia Detection 基于智能手机物联网的心律失常检测护理点方法

2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) Pub Date : 2022-03-10 DOI: 10.1109/HI-POCT54491.2022.9744077

Andrew Boggs, Hannah Chapman, B. Askarian

{"title":"Smartphone IoT-Based Point of Care Method for Arrhythmia Detection","authors":"Andrew Boggs, Hannah Chapman, B. Askarian","doi":"10.1109/HI-POCT54491.2022.9744077","DOIUrl":"https://doi.org/10.1109/HI-POCT54491.2022.9744077","url":null,"abstract":"In this paper, a novel method for continuously monitoring heart rate to detect arrhythmia is proposed. According to modern trends, wearable sensors have become promising for their use in the healthcare industry due to their convenience, ubiquity for patients, and the ability to gather real-time data. Technological advancements in new heart rate monitoring devices, such as wearable sensors and wireless monitors, are needed to help improve arrhythmia detection for patients. We propose a novel non-invasive, portable, and wireless method for monitoring heart rate by using Electrocardiogram (ECG) signals gathered using a Smartphone IoT-based system. Our experimental approach uses the measurement of peak-to-peak intervals between two successive signal peaks to estimate the heart rate of a test subject. The hardware used in the experiment includes a Node MCU Arduino platform to gather the raw data that is analyzed in MATLAB. Furthermore, a combination of filtering algorithms and peak detection of Electrocardiogram (ECG) signals is performed to remove noise and process the signals appropriately. The algorithm is tested on a healthy subject for seven minutes. Statistical data analysis is performed and the performance in terms of accuracy, sensitivity, and specificity was 96.1%, 95.2%, and 94.8% respectively.","PeriodicalId":283503,"journal":{"name":"2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127121691","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}

引用次数: 0

Particle Quantification from a Smartphone-based Biosensor using Deep Convolutional Neural Network for Clinical Diagnosis 基于智能手机的生物传感器粒子量化，应用深度卷积神经网络进行临床诊断

2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) Pub Date : 2022-03-10 DOI: 10.1109/HI-POCT54491.2022.9744062

Harshitha Govindaraju, M. Sami, U. Hassan

引用次数: 2

Sensitive and Selective Determination of multiple Diagnostic Targets using a Modular, ASSURED POC Platform called ESSENCE 使用模块化、有保证的POC平台ESSENCE对多个诊断目标进行敏感和选择性的测定

2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) Pub Date : 2022-03-10 DOI: 10.1109/HI-POCT54491.2022.9744075

Y. Cheng, Charmi Chande, Liang Zhenglong, Sreerag Kaaliveetil, S. Basuray

{"title":"Sensitive and Selective Determination of multiple Diagnostic Targets using a Modular, ASSURED POC Platform called ESSENCE","authors":"Y. Cheng, Charmi Chande, Liang Zhenglong, Sreerag Kaaliveetil, S. Basuray","doi":"10.1109/HI-POCT54491.2022.9744075","DOIUrl":"https://doi.org/10.1109/HI-POCT54491.2022.9744075","url":null,"abstract":"The sensor platform, ESSENCE, uses a Shear-Enhanced, flow-through non-planar 3D Nanoporous Electrode to overcome current electrochemical sensors limitations as a POC sensor, specifically selectivity and sensitivity limitations. ESSENCE consists of a microfluidic channel packed with a transducer like carbon nanotubes, functionalized with capture molecules surrounded by interdigitated electrodes. The porous electrode architecture enhances shear forces leading to high selectivity. The increased convective fluxes disrupt diffusive processes like the electric double layer, leading to rapid measurements. The enhanced electric field penetration due to the 3D electrode leads to a significant increase in signal from target molecule acquisition. The removal of parasitic noises from the double layer and the measurements at high frequency leads to substantial enhancement in the signal-to-noise ratio and thus high sensitivity. The unique chip architecture allows us to assemble the chip at room temperature (modular, solving cold chain issues). The transducer material can be easily exchanged to target different classes of biomolecules, thus giving making ESSENCE a universal modular platform. ESSENCE detects DNA and proteins with fM and pg/L sensitivity, respectively, against other background molecules in undiluted artificial urine. Interestingly protocol optimizations allow us to run ESSENCE in 10 minutes, making it a rapid POC test.Clinical Relevance—ESSENCE is specifically designed to be modular. It allows it to be a one-stop instrument for clinicians to screen for infectious diseases, liquid biopsy, and toxin detection to detect emerging pathogens while significantly reducing false positives and false positives negatives.","PeriodicalId":283503,"journal":{"name":"2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127590057","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}

引用次数: 0

Universal Estimation of an Intercity Social Distancing in Covid-19 Epochs Covid-19时期城市间社会距离的普遍估计

2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) Pub Date : 2022-03-10 DOI: 10.1109/HI-POCT54491.2022.9744061

H. Nieto-Chaupis

引用次数: 0

A One Inch in Diameter Point-of-Care Reader Head for the Measurement of Different Bio-Analytes Concentrations 直径一英寸的即时读取头，用于测量不同的生物分析物浓度

2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) Pub Date : 2022-03-10 DOI: 10.1109/HI-POCT54491.2022.9744069

Amir Tofighi Zavareh, Brian Serivuth Ko, Richard Horner, C. Lewis, M. Mcshane

引用次数: 1

A Rapid and Ultra-sensitive Sensing Strategy based on Tunable Dielectrophoresis for Robust POC Detection 基于可调介质电泳的快速超灵敏POC鲁棒检测策略

2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) Pub Date : 2022-03-10 DOI: 10.1109/HI-POCT54491.2022.9744059

Yu Jiang, Jayne Wu, S. Eda

引用次数: 0

A Bioelectronic Hand-Held Spectrophotometer for Biospecimen Analysis for Global Health Applications 用于全球健康应用的生物标本分析的手持式生物电子分光光度计

2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) Pub Date : 2022-03-10 DOI: 10.1109/HI-POCT54491.2022.9744058

Pragya Hooda, O. Tekin, M. Sami, U. Hassan

引用次数: 0

Enabling Clinically Relevant and Interpretable Deep Learning Models for Cardiopulmonary Exercise Testing 为心肺运动测试启用临床相关和可解释的深度学习模型

2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) Pub Date : 2022-03-10 DOI: 10.1109/HI-POCT54491.2022.9744068

James A. Jablonski, S. Angadi, Suchetha Sharma, Donald E. Brown

{"title":"Enabling Clinically Relevant and Interpretable Deep Learning Models for Cardiopulmonary Exercise Testing","authors":"James A. Jablonski, S. Angadi, Suchetha Sharma, Donald E. Brown","doi":"10.1109/HI-POCT54491.2022.9744068","DOIUrl":"https://doi.org/10.1109/HI-POCT54491.2022.9744068","url":null,"abstract":"Cardiopulmonary exercise testing (CPET) provides a safe, objective, and reliable assessment of cardiorespiratory fitness and is a valuable method used by clinical practitioners to predict and improve patient outcomes. However, CPET produces complex data consisting of multiple time-series that requires specialized training to interpret. This paper demonstrates accurate disease diagnosis by the use of deep learning models applied to these data using a small set of patients with known health conditions. Despite limited data availability, data augmentation enabled predictions with that consistently outperformed traditional interpretation methods and produced models that focused on clinically relevant regions of the multivariate time-series. Visual explanations of model decisions, projected through the nine-panel plot commonly used to interpret CPET, demonstrate the clinical relevance of model features, and provide insights that can benefit future training, interpretation, and research.Clinical relevance—This method can assist clinical practitioners by providing interpretable and reliable diagnosis recommendations with CPET data.","PeriodicalId":283503,"journal":{"name":"2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"284 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132228859","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}

引用次数: 2