2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)最新文献

Epileptic Seizure Prediction Based on Region Correlation of EEG Signal 基于脑电信号区域相关性的癫痫发作预测

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00030

Xuefei Liu, Jinbao Li, M. Shu

{"title":"Epileptic Seizure Prediction Based on Region Correlation of EEG Signal","authors":"Xuefei Liu, Jinbao Li, M. Shu","doi":"10.1109/CBMS49503.2020.00030","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00030","url":null,"abstract":"The existing methods of epileptic seizure prediction usually analyze the electroencephalogram (EEG) signals in the time domain, frequency domain or time-frequency domain. Although some good results have been achieved, the research and utilization of spatial information is still insufficient. Moreover, some studies extracted different features for different patients and achieved good results, but these methods are not universal and robust. Different from the previous methods, this paper propose a new feature processing method of EEG signal. All electrode signals on the scalp are considered as a whole, and fusing data from different regions to obtain spatial information. Then the correlation of first derivatives is used to obtain fluctuation information of signal caused by epilepsy, which further enlarge difference of signal in different seizures stages. In addition, we also design a post-processing strategy, which uses time-series information to rectify prediction results, so that the final result is more accurate. Finally, experimental results from the CHBMIT dataset show effectiveness of proposed method and strategy, while the extensive result confirms that our method is superior to several state-of-the-art methods in recent years.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123720499","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

Exploring Visual Attention and Machine Learning in 3D Visualization of Medical Temporal Data 探索视觉注意和机器学习在医学时间数据三维可视化中的应用

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00035

Leonardo Souza Silva, R. V. Aranha, Matheus A. O. Ribeiro, L. R. Nakamura, Fátima L. S. Nunes

引用次数: 1

Enhancing Recall Using Data Cleaning for Biomedical Big Data 生物医学大数据数据清洗提高召回率

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00057

P. Deshpande, A. Rasin, Roselyne B. Tchoua, J. Furst, D. Raicu, Sameer Kiran Antani

引用次数: 2

A Data-Driven Approach for Analyzing Healthcare Services Extracted from Clinical Records 用于分析从临床记录中提取的医疗保健服务的数据驱动方法

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00044

M. Scurti, Ernestina Menasalvas Ruiz, Maria-Esther Vidal, M. Torrente, D. Vogiatzis, G. Paliouras, M. Provencio, A. R. González

{"title":"A Data-Driven Approach for Analyzing Healthcare Services Extracted from Clinical Records","authors":"M. Scurti, Ernestina Menasalvas Ruiz, Maria-Esther Vidal, M. Torrente, D. Vogiatzis, G. Paliouras, M. Provencio, A. R. González","doi":"10.1109/CBMS49503.2020.00044","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00044","url":null,"abstract":"Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122041139","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

Fully-Automated Analysis of Scoliosis from Spinal X-Ray Images 脊柱x射线图像脊柱侧凸的全自动分析

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00029

A. Imran, Chao Huang, Hui Tang, Wei Fan, K. Cheung, M. To, Zhen Qian, D. Terzopoulos

{"title":"Fully-Automated Analysis of Scoliosis from Spinal X-Ray Images","authors":"A. Imran, Chao Huang, Hui Tang, Wei Fan, K. Cheung, M. To, Zhen Qian, D. Terzopoulos","doi":"10.1109/CBMS49503.2020.00029","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00029","url":null,"abstract":"Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Radiography is the most cost-effective and accessible modality for imaging the spine. Conventional spinal assessment, diagnosis of scoliosis, and treatment planning relies on tedious and time-consuming manual analysis of spine radiographs that is susceptible to observer variation. A reliable, fully-automated method that can accurately identify vertebrae, a crucial step in image-guided scoliosis assessment, is presently unavailable in the literature. Leveraging a novel, deep-learning-based image segmentation model, we develop an end-to-end spine radiograph analysis pipeline that automatically provides an accurate segmentation and identification of the vertebrae, culminating in the reliable estimation of the Cobb angle, the most widely used measurement to quantify the magnitude of scoliosis. Our experimental results with anterior-posterior spine X-ray images indicate that our system is effective in the identification and labeling of vertebrae, and can potentially provide assistance to medical practitioners in the assessment of scoliosis.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121532429","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: 4

Towards The Use of Smart Home Sensor Networks to Generate Predictive Activity Models 迈向使用智能家居传感器网络来生成预测活动模型

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00083

K. Morris, T. Giovannetti, Sarah M. Lehman

{"title":"Towards The Use of Smart Home Sensor Networks to Generate Predictive Activity Models","authors":"K. Morris, T. Giovannetti, Sarah M. Lehman","doi":"10.1109/CBMS49503.2020.00083","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00083","url":null,"abstract":"There are many use cases in the areas of cognition studies, physical therapy, and other medical related fields that stand to benefit from the ability to study the activities of individuals at home instead of a clinical environment. By monitoring their daily movements, various behavioral models can be generated that can aid in the early detection, diagnostic, and recovery processes relating to certain ailments. Many approaches to monitoring a person's behavior in the home focus on instrumenting the individual in some way, such as using a smart watch or band, and trying to determine the types of activities in which the user is engaged, such as eating, sleeping, etc. This can be burdensome to the user as it requires vigilance to ensure the device is able to perform its task. We propose a method to unobtrusively monitor a persons movements within the home to generate an activity model through the use of a smart home sensor network. Using this model, we explore various methods to measure model differences that can be used to determine when an individual's activities deviate from an established routine. Our platform, the Automatic eXtensible Inferential Occupancy Monitor, or AXIOM, allows seamless data collection from multiple sensors as well as multi-vector predictive analysis using the generated activity model.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114740285","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

Improved Skin Disease Classification Using Generative Adversarial Network 基于生成对抗网络的皮肤病分类改进

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00104

Bisakh Mondal, N. Das, K. Santosh, M. Nasipuri

引用次数: 5

Segmentation of Anterior Tissues in Craniofacial Cone-Beam CT Images 颅面锥束CT图像中前部组织的分割

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00021

Dharitri Misra, Michael Gill, Janice S. Lee, Sameer Kiran Antani

引用次数: 1

Catchicken: A Serious Game Based on the Go/NoGo Task to Estimate Inattentiveness and Impulsivity Symptoms 猫鸡:一个基于Go/NoGo任务来评估注意力不集中和冲动症状的严肃游戏

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00036

Prasetia Utama Putra, K. Shima, Koji Shimatani

{"title":"Catchicken: A Serious Game Based on the Go/NoGo Task to Estimate Inattentiveness and Impulsivity Symptoms","authors":"Prasetia Utama Putra, K. Shima, Koji Shimatani","doi":"10.1109/CBMS49503.2020.00036","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00036","url":null,"abstract":"We present a Go/NoGo 3D game equipped with an eye tracker that records subjects' responses and his gaze position on the monitor over time. The proposed system consists of two functions: training that allows an instructor to modify the game's parameters and make a customized test; and evaluation in which the instructor can fix the parameters to create a standardized test. During the experiment, subjects were required to respond only to Go character by pressing a spacebar. The experimental results from 59 participants demonstrated that one's response time and its variability correlated with one's gaze behavior. Subjects with higher gaze modulation tended to respond faster and more stable. We also observed that utilizing the proposed system we could monitor the improvements in an Autism Spectrum Disorder child during his rehabilitation: his gaze modulation increased and his response time became more steady. In brief, utilizing the proposed system, we could effectively measure participants' response time variability of NoGo errors and their gaze trajectory area, which previous studies found to have a strong relationship with symptoms of mental disorders.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115717697","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: 3

A Clustering Framework for Patient Phenotyping with Application to Adverse Drug Events 应用于药物不良事件的患者表型聚类框架

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00041

M. Bampa, P. Papapetrou, J. Hollmén

{"title":"A Clustering Framework for Patient Phenotyping with Application to Adverse Drug Events","authors":"M. Bampa, P. Papapetrou, J. Hollmén","doi":"10.1109/CBMS49503.2020.00041","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00041","url":null,"abstract":"We present a clustering framework for identifying patient groups with Adverse Drug Reactions from Electronic Health Records (EHRs). The increased adoption of EHRs has brought changes in the way drug safety surveillance is carried out and plays an important role in effective drug regulation. Unsupervised machine learning methods using EHRs as their input can identify patients that share common meaningful information, without the need for expert input. In this work, we propose a generalized framework that exploits the strengths of different clustering algorithms and via clustering aggregation identifies consensus patient cluster profiles. Moreover, the inherent hierarchical structure of diagnoses and medication codes is exploited. We assess the statistical significance of the produced clusterings by applying a randomization technique that keeps the data distribution margins fixed, as we are interested in evaluating information that is not conveyed by the marginal distributions. The experimental findings suggest that the framework produces medically meaningful patient groups with regard to adverse drug events by investigating two use-cases, i.e., aplastic anaemia and drug-induced skin eruption.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"483 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114647803","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: 1