2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)最新文献_第3页

Gene Regulatory Network Inference through Link Prediction using Graph Neural Network 基于图神经网络链接预测的基因调控网络推断

2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014835

S. Ganeshamoorthy, L. Roden, D. Klepl, F. He

引用次数: 0

Comparative Analysis of Functional Connectivity Metrics in EEG Datasets 脑电数据集功能连接度量的比较分析

2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014890

A. Maratova, P. Lencastre, A. Yazidi, P. Lind

引用次数: 0

EEG Changes Correlated with Ischemia Across the Sexes in Carotid Endarterectomy 颈动脉内膜切除术中脑电变化与缺血的相关性

2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014816

K. Du, V. Pedapati, A. Mina, A. Bradley, J. Espino, K. Batmanghelich, P. Thirumala, S. Visweswaran

引用次数: 0

Automatic Circulating Tumor Cell Segmentation and Enumeration in Digital Pathology by Using Deep Learning and Ball-scale Based Filtering Techniques 基于深度学习和球尺度滤波技术的数字病理循环肿瘤细胞自动分割和计数

2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014848

L. Tong, Y. Wan

{"title":"Automatic Circulating Tumor Cell Segmentation and Enumeration in Digital Pathology by Using Deep Learning and Ball-scale Based Filtering Techniques","authors":"L. Tong, Y. Wan","doi":"10.1109/SPMB55497.2022.10014848","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014848","url":null,"abstract":"Circulating tumor cells (CTCs) shed from the primary tumor, intravasate into blood, and translocate to distant tissues via circulation [1]. CTC enumeration allows cancer detection, treatment monitoring, and survival prediction [2], [3]. In the clinical setting immunofluorescence-based CTC enumeration is primarily used by expert cytopathologists. Manual enumeration requires cytopathologists with rich experience to read hundreds to thousands of images in hours. Despite the seemingly high number, this poor efficiency hinders the relevant clinical implementation. Therefore, high-automation enumeration is missing but highly desired [4]. Here, we proposed an automatic CTC segmentation and enumeration method in digital pathology by using deep learning techniques. To prepare for enumeration, peripheral blood mononuclear cells (PBMC) were extracted from cancer patient blood followed by infection with a reengineered adenovirus, i.e., rAdCTC, which is a CD46-targeting, DF3 promoter-selective, and GFP-overexpression adenovirus. The rAdCTC ensures detection specificity and efficiency of expression for CTCs. Subsequently, PBMCs were stained with anti-CD45 fluorescence-labeled antibody and DNA staining dye DAPI. Finally, the acquired fluorescence images were used for automatic segmentation and enumeration [5]. It is noteworthy that the fluorescence images used in this study contain three channels. The green, red, and blue signals respectively represent overexpressed GFP in infected cells, CD45 staining on leukocyte membranes, and nuclear staining.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122512497","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

Seizure Classification Using BERT NLP and a Comparison of Source Isolation Techniques with Two Different Time-Frequency Analysis 基于BERT NLP的癫痫分类和两种不同时频分析的源隔离技术的比较

2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014769

S. Davidson, N. McCallan, K. Y. Ng, P. Biglarbeigi, D. Finlay, B.L. Lan, J. Mclaughlin

{"title":"Seizure Classification Using BERT NLP and a Comparison of Source Isolation Techniques with Two Different Time-Frequency Analysis","authors":"S. Davidson, N. McCallan, K. Y. Ng, P. Biglarbeigi, D. Finlay, B.L. Lan, J. Mclaughlin","doi":"10.1109/SPMB55497.2022.10014769","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014769","url":null,"abstract":"Epilepsy is one of the most common neurological disorders in the world [1], affecting about 50 million people worldwide [2]. Epileptic seizures occur when millions of neurons are synchronously excited, resulting in a wave of electrical activity in the cerebral cortex [3]. Electroencephalography (EEG) is a noninvasive tool that measures cortical activity with millisecond temporal resolution. EEGs record the electrical potentials generated by the cerebral cortex nerve cells [4]. Therefore, this tool is commonly used for the analysis and detection of seizures [5]. Epilepsy causes many difficulties in relation to the quality of life of the patient. It is therefore vital that automatic detection algorithms exist to aid neurologists to accurately classify the different types of seizures. Roy et al. [10] used different machine learning techniques to achieve an average F1-score of 0.561 using 2 s windows whilst Vanabelle et al. [11] used 1 s windows and achieved an accuracy of 51.33%, which shows that reducing the time window would also decrease the accuracy of classification. This paper aims to show that an NLP can be used for hierarchical classification, following upon an earlier work on combining simple partial and complex partial seizures [9]. The second aim is to show a pipeline that can be used to separate the seizures back into their original labels using neural networks. This method is quick, effective, and requires less training.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127903404","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

Non-invasive Evaluation of Muscle Fatigue Using Mechanomyography and Surface Electromyography - A Pilot Study 肌力学图和表面肌电图对肌肉疲劳的无创评估-一项初步研究

2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014721

A. Benwali, Q.R. Ferguson, S. Farahat, R. Sandler, E. Hill, H. Mansy

{"title":"Non-invasive Evaluation of Muscle Fatigue Using Mechanomyography and Surface Electromyography - A Pilot Study","authors":"A. Benwali, Q.R. Ferguson, S. Farahat, R. Sandler, E. Hill, H. Mansy","doi":"10.1109/SPMB55497.2022.10014721","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014721","url":null,"abstract":"Muscle fatigue is defined as a decline in the ability to maintain a desired force against a load. Muscle fatigue may also be described as a decline in the muscle's maximum force during contraction. In contrast to muscle damage or weakness, characterized by a compromise in the ability of well-rested muscles to generate force, muscle fatigue is generally reversible with rest [2]. In a muscle experiencing fatigue, the nerves cannot sustain the high frequency signal necessary to reach the Maximum Contraction (MC) for a long time, resulting in a decline in muscle force during a sustained contraction. Due to its utility in providing information about nerve signaling and muscle's electrical activity, surface electromyography (sEMG) is currently the dominant method to detect muscle fatigue [2]. Mechanomyography (MMG) can reveal unique information that cannot be derived from the sEMG signal alone about the physiological behavior of muscles during contraction. However, more information may be needed about the ability of MMG to measure changes in muscle's activation patterns and mechanical properties that occur with muscle fatigue. Additionally, investigating the force-dependent characteristics of the MMG signal can provide information about physiological properties such as muscle activation strategies and fiber type distribution, which can be used to explore factors contributing to fatigue responses [1]. The purpose of this study is to examine and analyze the electrical and mechanical muscle responses to submaximal isometric contractions, as well as force-varying trapezoidal contractions in the rectus femoris muscle.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123247259","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

DMD Muscle Characteristics in the Time and Frequency Domain 时域和频域的DMD肌肉特性

2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014773

S. Ahdy, R. Sandler, M. Monduy, E. Baker, J. Wells, H. Mansy

引用次数: 0

Investigating the Need for Pediatric-Specific Automatic Seizure Detection 调查儿科专用自动癫痫检测的需求

2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014911

L. Wei, C. Mooney

引用次数: 0

Audible and Subaudible Components of the First and Second Heart Sounds 第一心音和第二心音的可听和不可听成分

2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014777

D. King, A. Voyatzoglou, R. Dhar, R. Sandler, H. Mansy

引用次数: 0

Time-Frequency Ridge Analysis of Sleep Stage Transitions 睡眠阶段转换的时频脊分析

2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Pub Date : 2022-12-03 DOI: 10.1109/SPMB55497.2022.10014897

C. McCausland, P. Biglarbeigi, R. Bond, G. Yadollahikhales, D. Finlay

{"title":"Time-Frequency Ridge Analysis of Sleep Stage Transitions","authors":"C. McCausland, P. Biglarbeigi, R. Bond, G. Yadollahikhales, D. Finlay","doi":"10.1109/SPMB55497.2022.10014897","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014897","url":null,"abstract":"The development of automated sleep apnea detection algorithms is an emerging topic of interest [1], [2]. The main aim of automation is to reduce the time and cost associated with manually scoring polysomnogram (PSG) tests [3]. To automate the process, traditional algorithms attempt to mimic the human observer by implementing a series of predefined rules, such as the American Academy of Sleep Medicine's (AASM) scoring guidelines [4]. Recently, data driven methods have emerged [5]. Electroencephalogram (EEG) frequency is known to be an important feature for both the human observer and data driven methods for sleep staging classification. This study presents the initial findings for a novel approach to sleep stage analysis. EEG time-frequency analysis is used to characterise the dominant frequency with respect to time, specifically at the point of sleep stage transition. Poor inter-scorer agreement at sleep stage transitions is a noted limitation of current manual and automated methods as the point of transition is poorly defined [6]. The goal of this study is to further discuss on the topic of sleep staging automation and explore alternative and novel features to improve the inter-scorer reliability of sleep staging.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127030955","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