2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)最新文献_第2页

Development of A Low-Cost Wearable Breast Cancer Detection Device 低成本可穿戴乳腺癌检测装置的研制

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) Pub Date : 2018-07-01 DOI: 10.1109/ICBAPS.2018.8527419

Ulta Delestri Laila Fadhillah, Zainal Abidin Nur Afikah, Nur Ezzaty Nazihah Safiee, Abdul Wahab Asnida, A. K. Mohammed Rafiq, M. H. Ramlee

{"title":"Development of A Low-Cost Wearable Breast Cancer Detection Device","authors":"Ulta Delestri Laila Fadhillah, Zainal Abidin Nur Afikah, Nur Ezzaty Nazihah Safiee, Abdul Wahab Asnida, A. K. Mohammed Rafiq, M. H. Ramlee","doi":"10.1109/ICBAPS.2018.8527419","DOIUrl":"https://doi.org/10.1109/ICBAPS.2018.8527419","url":null,"abstract":"Breast cancer is one of the leading causes of death in women worldwide. It happens when cells grow out of control and invade nearby tissues. Early detection of breast cancer is important to improve the survival rate of the patient. Common modalities used in the detection are mammography, ultrasound and Magnetic Resonance Imaging (MRI). However, these methods are impractical to be used as personal monitoring device due to its high cost and uncomfortable procedures on the patient. Therefore, this study proposes the use of multiple thermal sensors positioned on brassiere cloth covering all four quadrants to provide continuous monitoring of temperature changes on the breasts. To test the reliability of the developed device, breast phantom and heater were used to mimic women breasts and the tumor respectively. Thermal imaging camera was used to verify the changes of surface temperature on breast phantom. Result obtained shows that the reading of thermal sensors on each quadrant with a tumor to have higher temperature compared to the rest. Comparative analysis shows that there were no significant differences on the reading obtained by the thermal sensors and thermal imaging camera which indicates that this low-cost wearable device can be potentially used as breast cancer monitoring tool.","PeriodicalId":103255,"journal":{"name":"2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115496480","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: 12

Detection of Pathological Condition of Heart Using Texture Complexity of the Heart Sound Signals in Kernel Space 利用心音信号核空间纹理复杂度检测心脏病理状态

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) Pub Date : 2018-07-01 DOI: 10.1109/ICBAPS.2018.8527399

A. Mondal, R. Palaniappan

引用次数: 0

An Assessment of Radiation Hazard Index of Licensed and Illegal Cigarette 正牌卷烟与非法卷烟的辐射危害指数评价

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) Pub Date : 2018-07-01 DOI: 10.1109/ICBAPS.2018.8527386

Muhammad Shafiq Najemee Mohd Ariff, H. Abdullah, S. A. Jalil

引用次数: 0

GSM and GPS Based Real-Time Remote Physiological Signals Monitoring and Stress Levels Classification 基于GSM和GPS的实时远程生理信号监测与应激水平分类

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) Pub Date : 2018-07-01 DOI: 10.1109/ICBAPS.2018.8527406

A. G. Airij, R. Sudirman, U. U. Sheikh

{"title":"GSM and GPS Based Real-Time Remote Physiological Signals Monitoring and Stress Levels Classification","authors":"A. G. Airij, R. Sudirman, U. U. Sheikh","doi":"10.1109/ICBAPS.2018.8527406","DOIUrl":"https://doi.org/10.1109/ICBAPS.2018.8527406","url":null,"abstract":"Physiological signals are significant indicators that can help anticipate harmful underlying conditions in humans. Recent advancements in medicine and electronics have allowed monitoring of physiological signals cost effectively and noninvasively. People living in remote areas are usually deprived of basic healthcare facilities and the available remote physiological signals monitoring techniques make use of Bluetooth and WLAN technologies which are inoperable in such areas. The system proposed in this paper solves this issue by making use of GSM and GPS communication techniques due to their vast availability even at remote locations. The proposed system monitors three physiological signals namely; heart rate, skin conductance and skin temperature non-invasively and also classifies stress levels. Finally, the physiological signals and stress levels data is stored for record maintenance and sent to a doctor so that he/she may monitor the patient remotely. A rule-based fuzzy logic algorithm is used for stress classification and the results shows that it achieved the highest accuracy when compared to other algorithms found in previous works. In addition to that, a stress levels dataset is also presented in this paper which can be further refined in future research.","PeriodicalId":103255,"journal":{"name":"2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131075141","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: 20

Lung Disease Classification Using Different Deep Learning Architectures and Principal Component Analysis 使用不同深度学习架构和主成分分析的肺部疾病分类

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) Pub Date : 2018-07-01 DOI: 10.1109/ICBAPS.2018.8527385

Joel Than Chia Ming, N. Noor, O. M. Rijal, R. M. Kassim, A. Yunus

{"title":"Lung Disease Classification Using Different Deep Learning Architectures and Principal Component Analysis","authors":"Joel Than Chia Ming, N. Noor, O. M. Rijal, R. M. Kassim, A. Yunus","doi":"10.1109/ICBAPS.2018.8527385","DOIUrl":"https://doi.org/10.1109/ICBAPS.2018.8527385","url":null,"abstract":"Lung disease is among the leading diseases that cause mortality worldwide. Most cases of lung diseases are detected when the disease is in the advanced stages. Therefore the development of systems and methods that enable faster and early diagnosis will play a vital role in the world today. Computer Aided Diagnosis (CADx) systems play such a role and are currently being expanded. This study explores the potential of using deep learning features from pre-trained deep learning architectures to provide rich and robust features. These features were compared to the conventionally used Gray-level Co-occurrence Matrix (GLCM). Deep features produced the highest accuracy of 100% as compared to 93.52% produced by using GLCM features. This study also compared the classification of deep features with five different classifiers and Support Vector Machine (SVM) showed the highest result. This high accuracy was also reproduced with Linear Discriminant Analysis (LDA) and Regression classifiers. Principal Component Analysis (PCA) was also done to evaluate the usage of reduced number of features and its effect on the classification performance. Using deep features produced 4096 features and a classification accuracy of 100%. When PCA is introduced, only 79 features were used however the accuracy produced was the same. Thus, there is promising use of deep features together with PCA to reduce the number of features in the classification of diseased lungs.","PeriodicalId":103255,"journal":{"name":"2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132642369","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: 6

A Mini Review on Graft Fixation Devices for Anterior Cruciate Ligament Reconstruction - Techniques, Materials and Complications 前交叉韧带重建用移植物固定装置技术、材料及并发症综述

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) Pub Date : 2018-07-01 DOI: 10.1109/ICBAPS.2018.8527418

N. A. Zainal Abidin, Asnida Abdul Wahab, M. H. Ramlee, M. A. Abdul Kadir

引用次数: 3

Signal Conditioning and Processing of Motionlabs Pre-Amplifier EMG Sensor Motionlabs前置放大器肌电信号传感器的信号调理与处理

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) Pub Date : 2018-07-01 DOI: 10.1109/ICBAPS.2018.8527389

Saad Bin Ubaid, Ahmad Shoaib, M. Waseem, Malik Umair, M. Tiwana

引用次数: 1

A Hybrid Filter-Wrapper Gene Selection Method for Cancer Classification 一种用于癌症分类的混合过滤-包装基因选择方法

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) Pub Date : 2018-07-01 DOI: 10.1109/ICBAPS.2018.8527392

O. Alomari, A. Khader, M. Al-Betar, Zaid Abdi Alkareem Alyasseri

{"title":"A Hybrid Filter-Wrapper Gene Selection Method for Cancer Classification","authors":"O. Alomari, A. Khader, M. Al-Betar, Zaid Abdi Alkareem Alyasseri","doi":"10.1109/ICBAPS.2018.8527392","DOIUrl":"https://doi.org/10.1109/ICBAPS.2018.8527392","url":null,"abstract":"The advent of DNA microarray technology has paved the way to providing increased opportunities to the molecular biologists to analyze the expression level of thousands of genes (features) in one experiment. The gene expression level provides the possibility of diagnosing various diseases such as cancer. In this regard, several computational techniques such as pattern classification approaches can be applied. However, the existence of a huge quantity of genes and very few patients' samples available hinders the classifier or machine learning techniques from producing accurate classification results. Most of these genes are irrelevant and redundant, which may deteriorate the classification performance. Therefore, gene selection is needed to select the most relevant genes. This paper proposes hybrid filter-wrapper gene selection method using Minimum Redundancy Maximum Relevancy (MRMR) as the filter approach and flower pollination algorithm (FPA) as the wrapper approach. MRMR was used to find the most important genes from all genes in the gene expression data, and FPA is employed in order to locate the most informative gene subset from the reduce set that obtained by MRMR. To test the accuracy and performance of the study's proposed method, extensive experiments are conducted and three microarray datasets are used. They include Colon, Breast, and Ovarian. A similar procedure has been performed on the Genetic algorithm (GA) in comparison with the proposed method (MRMR-FPA) in this study. The results concluded that the MRMR-FPA can be used as an alternative method to address the gene selection problem.","PeriodicalId":103255,"journal":{"name":"2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128917867","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: 28

2nd International Conference on BioSignal Analysis, Processing and System (ICBAPS 2018) 第二届生物信号分析、处理与系统国际会议(ICBAPS 2018)

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) Pub Date : 2018-07-01 DOI: 10.1109/icbaps.2018.8527379

引用次数: 0

A Pilot Study of Malnutrition Among Elderly in a Malaysia Elderly Care Centre - Part I: Assessment of Handgrip Strength Measurement 马来西亚老年护理中心老年人营养不良试点研究-第一部分:握力测量评估

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) Pub Date : 2018-07-01 DOI: 10.1109/ICBAPS.2018.8527416

N. Bani, M. Z. Hassan, H. Kaidi, Nor Fazilah Mohd Hashim, R. Dziyauddin, Rafizah Musa, M. A. Suhot, S. Usman, F. Muhammad-Sukki, S. Abu-Bakar, A. Mas’ud

{"title":"A Pilot Study of Malnutrition Among Elderly in a Malaysia Elderly Care Centre - Part I: Assessment of Handgrip Strength Measurement","authors":"N. Bani, M. Z. Hassan, H. Kaidi, Nor Fazilah Mohd Hashim, R. Dziyauddin, Rafizah Musa, M. A. Suhot, S. Usman, F. Muhammad-Sukki, S. Abu-Bakar, A. Mas’ud","doi":"10.1109/ICBAPS.2018.8527416","DOIUrl":"https://doi.org/10.1109/ICBAPS.2018.8527416","url":null,"abstract":"The number of elderly population in Malaysia of aged 60 years and above is expected to increase every year. Apart from that, the life expectancy of the elderly population is also estimated to rise up with female tend to live longer than male. These uncontrollable factors can lead to a declining healthy lifestyle if the current health care system is inadequate and inappropriate in providing services to the elderly population especially to those that require rehabilitation program to return to their premorbid function. In some cases, these frail elderly were sent to Elderly Care Centre by their primary care taker due to limited nursing ability, time and space. In this pilot study, handgrip strength among elderly and pre-elderly community residing at an Elderly Care Center in Branang, Malaysia is measured using a cross-sectional study design. In overall, 12 male occupants (8 elderly, 4 pre-elderly people) aged 50 years and above were interviewed to obtain data on their sociodemographic characteristics, medical history, hand functionality and hand strength measurements. The overall sample mean (standard deviation) age are 63 (8) years. The mean (standard deviation) of handgrip measurements is 12.47 (6.69) kg.","PeriodicalId":103255,"journal":{"name":"2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124139225","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