2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)最新文献_第2页

Towards Artificial Intelligence-enabled Medical Pre-operative Airway Assessment 面向人工智能的医疗术前气道评估

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982781

Qinjie Lin, Chin-Boon Chng, J. Too, Jinshuo Zhang, Haobing Liu, T. Foong, Will Loh, C. Chui

{"title":"Towards Artificial Intelligence-enabled Medical Pre-operative Airway Assessment","authors":"Qinjie Lin, Chin-Boon Chng, J. Too, Jinshuo Zhang, Haobing Liu, T. Foong, Will Loh, C. Chui","doi":"10.1109/HealthCom54947.2022.9982781","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982781","url":null,"abstract":"For surgeries which require general anesthesia, airway management is imperative. Difficult airway, which inhibits proper intubation, can be fatal. As such, pre-operative airway assessments are conducted by clinicians to determine the ease of intubation as well as to identify patients with difficult airway. To improve the process, artificial intelligence (AI) methods can be employed to predict such difficult airway situations so that suitable preparations can be made beforehand. However, due to the need for explainability of AI models required by healthcare regulations, typical black box models which work best with most data-driven AI methods cannot be used. Therefore, in the current work, a machine learning model has been established to predict the specific medical facial landmarks that are currently used by clinicians. These include the eyes, mentum, thyroid notch, suprasternal notch, forehead, tragus and radix. The model is based on convolutional neural network and a practical facial landmark detector concept. Furthermore, k-fold cross-validation sampling and the Adabelief optimizer have been utilized. The model prediction results display accurate prediction of the features, with the testing loss exhibiting good stability and maintaining well below 0.01 throughout. Attributed to that, the current model can lead to meaningful diagnosis of difficult airway during airway assessments.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128760435","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

A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids 一种基于云的多模态助听器视听语音增强框架结构

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982772

A. Bishnu, Ankit Gupta, M. Gogate, K. Dashtipour, Ahsan Adeel, Amir Hussain, M. Sellathurai, T. Ratnarajah

{"title":"A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids","authors":"A. Bishnu, Ankit Gupta, M. Gogate, K. Dashtipour, Ahsan Adeel, Amir Hussain, M. Sellathurai, T. Ratnarajah","doi":"10.1109/HealthCom54947.2022.9982772","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982772","url":null,"abstract":"In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low latency requirements of future multimodal hearing assistive technology. The innovative design needs to meet multiple challenging constraints including up/down link communications, delay of transmission and signal processing, and real-time AV SE models processing. The transceiver includes device detection, frame detection, frequency offset estimation, and channel estimation capabilities. We develop both uplink (hearing aid to the cloud) and downlink (cloud to hearing aid) frame structures based on the data rate and latency requirements. Due to the varying nature of uplink information (audio and lip-reading), the uplink channel supports multiple data rate frame structure, while the downlink channel has a fixed data rate frame structure. In addition, we evaluate the latency of different PHY layer blocks of the transceiver for developed frame structures using LabVIEW NXG. This can be used with software defined radio (such as Universal Software Radio Peripheral) for real-time demonstration scenarios.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128793447","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

Sleep Stage Identification based on Single-Channel EEG Signals using 1-D Convolutional Autoencoders 基于一维卷积自编码器的单通道脑电信号睡眠阶段识别

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982775

M. Dutt, Surender Redhu, Morten Goodwin, C. Omlin

{"title":"Sleep Stage Identification based on Single-Channel EEG Signals using 1-D Convolutional Autoencoders","authors":"M. Dutt, Surender Redhu, Morten Goodwin, C. Omlin","doi":"10.1109/HealthCom54947.2022.9982775","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982775","url":null,"abstract":"Automatic sleep stage classification can play a vital role when measuring sleep quality and diagnosing different sleep-related ailments. Several automated sleep stage identification algorithms have been proposed using various physiological signals. However, most of these methods use hand-crafted features or multiple Electroencephalography (EEG) signals. This work proposes a one-dimensional convolutional autoencoder (1D-CAE) based on a single-channel EEG signal for sleep stage identification. A total of five 1-D CAEs models are implemented, and each model is trained to reconstructs a specific sleep stage with the lowest reconstruction error, thus enabling the sleep stage identification based on this error. Furthermore, the proposed approach is evaluated on the Sleep EDF expanded datasets and achieved an overall classification accuracy of 87.2% using a single-channel EEG FPz-Cz signal. Also, our approach demonstrated the highest sleep stage identification accuracy compared with the recent algorithms, especially for sleep stage N1, a short period that transitions between sleep stages during a sleep cycle.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117046158","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

A New XAI-based Evaluation of Generative Adversarial Networks for IMU Data Augmentation 一种新的基于xai的IMU数据增强生成对抗网络评价

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982780

Sara Narteni, V. Orani, Enrico Ferrari, Damiano Verda, E. Cambiaso, M. Mongelli

{"title":"A New XAI-based Evaluation of Generative Adversarial Networks for IMU Data Augmentation","authors":"Sara Narteni, V. Orani, Enrico Ferrari, Damiano Verda, E. Cambiaso, M. Mongelli","doi":"10.1109/HealthCom54947.2022.9982780","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982780","url":null,"abstract":"Data augmentation is a widespread innovative technique in Artificial Intelligence: it aims at creating new synthetic data given an existing real baseline, thus allowing to overcome the issues arising from the lack of labelled data for proper training of classification algorithms. Our paper focuses on how a common data augmentation methodology, the Generative Adversarial Networks (GANs), which is widespread for images and timeseries data, can be also applied to generate multivariate data. We propose a novel scheme for GANs evaluation, based on the performance of an explainable AI (XAI) algorithm and an innovative definition of rule similarity. In particular, we will consider an application dealing with the augmentation of Inertial Movement Units (IMU) data for physical fatigue monitoring in two age subgroups (under and over 40 years old) of the original data. We will show how our innovative rule similarity metric can drive the selection of the best fake dataset among a set of different candidates, corresponding to different GAN training runs.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131479755","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

Signal processing for remote monitoring of home-based rehabilitation support activities 用于远程监测家庭康复支持活动的信号处理

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982749

Federica Ferraro, Giulia Iaconi, Marina Simonini, S. Dellepiane

引用次数: 0

A Quantitative Approach and Preliminary Application in Healthy Subjects and Patients with Valvular Heart Disease for 24-h Breathing Patterns Analysis Using Wearable Devices 可穿戴设备在健康受试者和瓣膜性心脏病患者24小时呼吸模式分析中的定量方法及初步应用

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982736

Jiachen Wang, Zhicheng Yang, Yuqiang Wang, Chenbin Ma, Jian Zhang, Peng-ming Yu, Ying-qiang Guo, Zheng Zhang

{"title":"A Quantitative Approach and Preliminary Application in Healthy Subjects and Patients with Valvular Heart Disease for 24-h Breathing Patterns Analysis Using Wearable Devices","authors":"Jiachen Wang, Zhicheng Yang, Yuqiang Wang, Chenbin Ma, Jian Zhang, Peng-ming Yu, Ying-qiang Guo, Zheng Zhang","doi":"10.1109/HealthCom54947.2022.9982736","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982736","url":null,"abstract":"The 24-h breathing patterns may be closely related to health status as well as disease progression. However, there is no consistent and widely accepted approach for mining the potential value in 24-h respiratory signals based on wearable device monitoring. This study presented a reference approach including signal quality assessment, calibration of tidal volume, and breathing patterns parameters based on a wearable continuous physiological parameter monitoring system for 24-h breathing patterns analysis, including time domain, frequency domain and nonlinear domain. 70 healthy subjects and 76 patients undergoing heart valve surgery were enrolled in this study. The normal reference range of breathing patterns was calculated based on healthy subjects. A subgroup study was conducted based on whether patients developed postoperative pulmonary complications (PPCs). Compared with non-PPCs group, the coefficient of variation of breathing rate in the recumbent position was smaller in the PPCs group. During the daytime, the kurtosis of breathing rate and contribution of the abdomen was smaller in PPCs group. During the nighttime, the coefficient of variation of breathing rate and SD2 was smaller in the PPCs group. The quantitative method proposed in this study fills the gap in the field of quantifying 24-h breathing patterns which is effective in discriminating different populations and is expected to be used widely in the context of COVID-19 epidemic.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134036116","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

Long COVID Diary - Design and Development of a Support Application for People with Long COVID 长COVID日记-长COVID患者支持应用程序的设计和开发

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982758

A. Hausberger, René Baranyi, Sylvia Winkler, B. Tappeiner, T. Grechenig

引用次数: 0

Detecting Face-Mask Wearing Status Using Motion Sensors in Commercially Available Smartwatches 在商用智能手表中使用运动传感器检测口罩佩戴状态

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982766

Shota Ono, Yuuki Nishiyama, K. Sezaki

引用次数: 1

Non-uniform spatial priors for multi-dipole localization from MEG/EEG data 基于脑磁图/脑电数据的多偶极子定位非均匀空间先验

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982792

Alessandro Viani, Gianvittorio Luria, A. Sorrentino

{"title":"Non-uniform spatial priors for multi-dipole localization from MEG/EEG data","authors":"Alessandro Viani, Gianvittorio Luria, A. Sorrentino","doi":"10.1109/HealthCom54947.2022.9982792","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982792","url":null,"abstract":"Localization of current dipoles from magneto/electro-encephalographic data is a key step in several applications, from basic neuroscience to pre-surgical evaluation of epileptic patients.SESAME is a Monte Carlo algorithm that can automatically localize an a priori unknown number of dipolar sources from M/EEG data, and provides a posterior probability map representing uncertainty on the source locations.SESAME has been shown to provide accurate localization in case of multi-dipole configurations. So far, SESAME has always been applied using a uniform prior distribution on the source location, corresponding to complete lack of information about the source location. However, in many practical contexts the experimenter (or clinician) might have some more or less vague information about where the sources could be.In this work, we investigate whether the use of non-uniform priors within SESAME can contribute to increasing the accuracy of source localization.We provide numerical results on simulated data, showing that the use of non-uniform priors can effectively increase the source localization accuracy when the prior distribution is correct (i.e., higher around the true source locations), without substantially worsening the performances when, as it may happen, the prior information is wrong.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123625512","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

Medical Image Authentication using Watermarking and Blockchain 使用水印和区块链的医学图像认证

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982793

Abrar Alsehli, Wadood Abdul, Saleh Almowuena, S. Ghouzali, Souad Larabi-Marie-Sainte

{"title":"Medical Image Authentication using Watermarking and Blockchain","authors":"Abrar Alsehli, Wadood Abdul, Saleh Almowuena, S. Ghouzali, Souad Larabi-Marie-Sainte","doi":"10.1109/HealthCom54947.2022.9982793","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982793","url":null,"abstract":"In recent years the use of images in communication has significantly increased. It is important to secure images from unauthorized access and manipulation. Copyright protection approaches protect data from attacks that result in modification, illegal copying or removal. Our proposed approach uses watermarking and blockchain to confirm authentication. Watermarking is a data-hiding technique whereby data is hidden inside an image to make it difficult to modify. Blockchain at its core, is a distributed-ledger that stores secured and encrypted information while ensuring that it is not changed by someone. We present an imperceptible and secure watermarking approach for digital images using the discrete wavelet transform (DWT). The encrypted watermark is saved in the blockchain. When an image is received and it is to be authenticated, the watermark is retrieved from the blockchain and compared with the watermark that is extracted from the given image. The visual quality of the proposed watermarking approach is tested by comparing the original and watermarked images using peak signal-to-noise ratio (PSNR). The basic challenge that we face is finding suitable locations for inserting the watermark while satisfying the imperceptibility aspects along with the security of the algorithm to reach its highest effectiveness.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129639840","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