Pub Date : 2015-05-26DOI: 10.1109/ICBAPS.2015.7292223
Marlina Yakno, J. Mohamad-Saleh, B. A. Rosdi
Region of Interest (ROI) extraction is a crucial step in automatic hand vein biometric and biomedical systems. The aim of ROI extraction is to decide which part of the image is suitable for hand vein feature extraction. The majority vein patterns sometimes can be determined at different locations; left, right and centre of the back of hand. The existing methods have not been able to extract more vein patterns at the right and left borders of the ROI. This paper proposes a hand vein ROI extraction method which is robust at avoiding loss of vein patterns information along the right and left borders of the ROI. First, we determine the threshold value, which will be used to segment the hand region. Second, the hand image is traced using boundary tracing. Third, the Euclidean distance is measured between reference point and hand boundary. Fourth, the distribution diagrams are constructed for the feature points selection. Finally, four coordinates are determined prior to ROI extraction. The experimental results show that the proposed method can extract ROI more accurately and effectively compared with other methods.
{"title":"New technique for larger ROI extraction of hand vein images","authors":"Marlina Yakno, J. Mohamad-Saleh, B. A. Rosdi","doi":"10.1109/ICBAPS.2015.7292223","DOIUrl":"https://doi.org/10.1109/ICBAPS.2015.7292223","url":null,"abstract":"Region of Interest (ROI) extraction is a crucial step in automatic hand vein biometric and biomedical systems. The aim of ROI extraction is to decide which part of the image is suitable for hand vein feature extraction. The majority vein patterns sometimes can be determined at different locations; left, right and centre of the back of hand. The existing methods have not been able to extract more vein patterns at the right and left borders of the ROI. This paper proposes a hand vein ROI extraction method which is robust at avoiding loss of vein patterns information along the right and left borders of the ROI. First, we determine the threshold value, which will be used to segment the hand region. Second, the hand image is traced using boundary tracing. Third, the Euclidean distance is measured between reference point and hand boundary. Fourth, the distribution diagrams are constructed for the feature points selection. Finally, four coordinates are determined prior to ROI extraction. The experimental results show that the proposed method can extract ROI more accurately and effectively compared with other methods.","PeriodicalId":243293,"journal":{"name":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121496344","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}
Pub Date : 2015-05-26DOI: 10.1109/ICBAPS.2015.7292221
Hannah Sofian, J. Than, N. Mohd Noor, H. Dao
In this paper we present an automated segmentation method to detect the boundary between adventitia and media on the cross sectional view of the artery of patients who have plaques. The problem encounter is that the boundaries of the adventitia, media, intima and lumen are embedded when plaques exist. Moreover, the artery disease has damaged the tissue layers. This paper proposed a method in segmenting and detecting the outer boundary which is the media adventitia area of the artery using intravascular ultrasound (IVUS) images. The proposed method for segmentation is to use Otsu thresholding, followed by empirical thresholding and binary - morphological operation. The data used in this study was 10 samples from dataset B of IVUS images, courtesy of Simone Balocco (Training set, Computer Vision Center, Bellaterra, Universitat de Barcelona, Dept. Matemàtica Aplicada i Anàlisi, Barcelona). The proposed method shows promising result in detecting and segmenting the media adventitia boundary of the IVUS images.
在本文中，我们提出了一种自动分割方法，以检测有斑块的患者动脉横切面上的外膜和介质之间的边界。遇到的问题是，当斑块存在时，外膜、中膜、内膜和管腔的边界被嵌入。此外，动脉疾病已经破坏了组织层。本文提出了一种利用血管内超声(IVUS)图像分割和检测动脉外边界(中外膜区域)的方法。本文提出的分割方法是先采用Otsu阈值分割，再采用经验阈值分割和二值形态分割。本研究使用的数据来自IVUS图像数据集B的10个样本，由Simone Balocco提供(训练集，计算机视觉中心，Bellaterra，巴塞罗那大学，Dept. Matemàtica applied i Anàlisi，巴塞罗那)。该方法在IVUS图像介质外边界的检测和分割方面取得了良好的效果。
{"title":"Segmentation and detection of media adventitia coronary artery boundary in medical imaging intravascular ultrasound using otsu thresholding","authors":"Hannah Sofian, J. Than, N. Mohd Noor, H. Dao","doi":"10.1109/ICBAPS.2015.7292221","DOIUrl":"https://doi.org/10.1109/ICBAPS.2015.7292221","url":null,"abstract":"In this paper we present an automated segmentation method to detect the boundary between adventitia and media on the cross sectional view of the artery of patients who have plaques. The problem encounter is that the boundaries of the adventitia, media, intima and lumen are embedded when plaques exist. Moreover, the artery disease has damaged the tissue layers. This paper proposed a method in segmenting and detecting the outer boundary which is the media adventitia area of the artery using intravascular ultrasound (IVUS) images. The proposed method for segmentation is to use Otsu thresholding, followed by empirical thresholding and binary - morphological operation. The data used in this study was 10 samples from dataset B of IVUS images, courtesy of Simone Balocco (Training set, Computer Vision Center, Bellaterra, Universitat de Barcelona, Dept. Matemàtica Aplicada i Anàlisi, Barcelona). The proposed method shows promising result in detecting and segmenting the media adventitia boundary of the IVUS images.","PeriodicalId":243293,"journal":{"name":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126123873","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}
Pub Date : 2015-05-26DOI: 10.1109/ICBAPS.2015.7292218
Farah Hanani Mohammad Khasasi, A. M. Ali, Zulkhairi Mohd Yusof
An Automated Storage and Retrieval System (ASRS) is an integrated automated system consists of hardware, software and networking system which communicates with each other over a fieldbus network. It allows a range of control strategies to be investigated using the design and algorithm developed. This storage system commonly operates under computerized control known as Computer Supervisory Control (CSC) system to store and retrieve the items either raw materials, semi-finished products, or finished-products. It can be manually operated as a stand-alone unit, but all warehouses nowadays are looking for an integrated automated system which can operate without any interference of an operator for efficiency and better performance of a warehouse. Thus, the Microcontroller Arduino UNO, Bluetooth technology, and Servo Motor are used in this experiment to investigate how efficient these devices can support the working mechanism of an ASRS.
{"title":"Development of an Automated Storage and Retrieval System in dynamic industrial environment","authors":"Farah Hanani Mohammad Khasasi, A. M. Ali, Zulkhairi Mohd Yusof","doi":"10.1109/ICBAPS.2015.7292218","DOIUrl":"https://doi.org/10.1109/ICBAPS.2015.7292218","url":null,"abstract":"An Automated Storage and Retrieval System (ASRS) is an integrated automated system consists of hardware, software and networking system which communicates with each other over a fieldbus network. It allows a range of control strategies to be investigated using the design and algorithm developed. This storage system commonly operates under computerized control known as Computer Supervisory Control (CSC) system to store and retrieve the items either raw materials, semi-finished products, or finished-products. It can be manually operated as a stand-alone unit, but all warehouses nowadays are looking for an integrated automated system which can operate without any interference of an operator for efficiency and better performance of a warehouse. Thus, the Microcontroller Arduino UNO, Bluetooth technology, and Servo Motor are used in this experiment to investigate how efficient these devices can support the working mechanism of an ASRS.","PeriodicalId":243293,"journal":{"name":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130396348","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}
Pub Date : 2015-05-26DOI: 10.1109/ICBAPS.2015.7292217
C. R. Ng, J. Than, N. Noor, O. M. Rijal
Brain segmentation is important in the field of neuropsychiatric disorders. With Computed Tomography (CT) scan being the gold standard in brain scan, brain segmentation in CT images is also very important in the detection of many pathology related to the brain. Fuzzy c-Means (FCM) is a popular method in data clustering and also in image segmentation due to it being robust. Graph cut is a segmentation algorithm that is able to separate the image into several partitions based on the similarity between each nodes in the image. In this paper, the CT scan images were first processed with FCM optimization and are separated into clusters based on pixel intensity. After that the post-FCM images were then loaded into the graph cut algorithm to separate the images into partitions, allowing users to manually select the appropriate partitions that best represent the brain region. The results showed that the images are less erroneous when they are clustered first with FCM before going through the graph cut algorithm.
{"title":"Preliminary brain region segmentation using FCM and graph cut for CT scan images","authors":"C. R. Ng, J. Than, N. Noor, O. M. Rijal","doi":"10.1109/ICBAPS.2015.7292217","DOIUrl":"https://doi.org/10.1109/ICBAPS.2015.7292217","url":null,"abstract":"Brain segmentation is important in the field of neuropsychiatric disorders. With Computed Tomography (CT) scan being the gold standard in brain scan, brain segmentation in CT images is also very important in the detection of many pathology related to the brain. Fuzzy c-Means (FCM) is a popular method in data clustering and also in image segmentation due to it being robust. Graph cut is a segmentation algorithm that is able to separate the image into several partitions based on the similarity between each nodes in the image. In this paper, the CT scan images were first processed with FCM optimization and are separated into clusters based on pixel intensity. After that the post-FCM images were then loaded into the graph cut algorithm to separate the images into partitions, allowing users to manually select the appropriate partitions that best represent the brain region. The results showed that the images are less erroneous when they are clustered first with FCM before going through the graph cut algorithm.","PeriodicalId":243293,"journal":{"name":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128451995","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}
Pub Date : 2015-05-26DOI: 10.1109/ICBAPS.2015.7292226
Suraya Mohammad, D. T. Morris
In this paper, we present our ongoing work on glaucoma classification using fundus images. The approach makes use of texture analysis based on Binary Robust Independent Elementary Features (BRIEF). This texture measurement is chosen because it can address the illumination issues of the retinal images and has a lower degree of computational complexity than most of the existing texture measurement methods currently used in the literature. Contrary to other approaches, the texture measures are extracted from the whole retina image without targeting any specific region. The method was tested on a set of 196 images composed of 110 healthy retina images and 86 glaucomatous images and achieved an area under curve (AUC) of 84%. A comparison performance with other texture measurements is also included, which shows our method to be superior.
{"title":"Texture analysis for glaucoma classification","authors":"Suraya Mohammad, D. T. Morris","doi":"10.1109/ICBAPS.2015.7292226","DOIUrl":"https://doi.org/10.1109/ICBAPS.2015.7292226","url":null,"abstract":"In this paper, we present our ongoing work on glaucoma classification using fundus images. The approach makes use of texture analysis based on Binary Robust Independent Elementary Features (BRIEF). This texture measurement is chosen because it can address the illumination issues of the retinal images and has a lower degree of computational complexity than most of the existing texture measurement methods currently used in the literature. Contrary to other approaches, the texture measures are extracted from the whole retina image without targeting any specific region. The method was tested on a set of 196 images composed of 110 healthy retina images and 86 glaucomatous images and achieved an area under curve (AUC) of 84%. A comparison performance with other texture measurements is also included, which shows our method to be superior.","PeriodicalId":243293,"journal":{"name":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"1073-1076 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127349961","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}
Pub Date : 2015-05-26DOI: 10.1109/ICBAPS.2015.7292228
Ahmed Fadhil Hassoney Almurshedi, Abd. Khamim Ismail
Measure Projection Analysis (MPA) method based on EEGLAB and Matlab Toolbox is used to analyze the projections of brain signal sources that are responsible for the measured potentials at the scalp electrodes. These projections are based on probabilistic multi subject algorithm abandoning the notion of distinct independent component clusters. It examines voxel by voxel for brain regions having event related independent components process dynamics that exhibit statistically significant consistency across subjects by probability density representation. Neuron source locations are responsible in generating current in different brain regions through the measured potentials. The projections of visual evoked potentials (VEP) sources in different age groups are investigated. The result shows a slight difference in the projections with respect to the age. These findings represent the maturity level and re-grasp the development of brain and visual pathway with age.
{"title":"Measure Projection Analysis of VEP localization neuron generator","authors":"Ahmed Fadhil Hassoney Almurshedi, Abd. Khamim Ismail","doi":"10.1109/ICBAPS.2015.7292228","DOIUrl":"https://doi.org/10.1109/ICBAPS.2015.7292228","url":null,"abstract":"Measure Projection Analysis (MPA) method based on EEGLAB and Matlab Toolbox is used to analyze the projections of brain signal sources that are responsible for the measured potentials at the scalp electrodes. These projections are based on probabilistic multi subject algorithm abandoning the notion of distinct independent component clusters. It examines voxel by voxel for brain regions having event related independent components process dynamics that exhibit statistically significant consistency across subjects by probability density representation. Neuron source locations are responsible in generating current in different brain regions through the measured potentials. The projections of visual evoked potentials (VEP) sources in different age groups are investigated. The result shows a slight difference in the projections with respect to the age. These findings represent the maturity level and re-grasp the development of brain and visual pathway with age.","PeriodicalId":243293,"journal":{"name":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131845603","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}
Pub Date : 2015-05-26DOI: 10.1109/ICBAPS.2015.7292211
S. A. Jalil, H. Abdullah, M. Taib
All living body has been shown to emit radiation into space surrounding their body. The radiation field encloses the physical body and emits the characteristics of frequency radiation. This study discusses the analysis of human body radiation wave on the human torso and compares their frequency characteristics between genders. At first, the characteristic of radiation frequency is determined by employing statistical analysis of correlation and analysis of variance. The results show that the characteristic difference of radiation frequency between males and females in human torso is significant. Then, for the purpose of classification, the k-nearest neighbor is used as classification algorithm. The results show that the proposed technique properly classifies gender with accuracy of 100 percent. Experimental results recommend that the proposed technique is appropriate and capable to classify gender using frequency analysis of the human torso radiation.
{"title":"Human body radiation wave analysis on the human torso","authors":"S. A. Jalil, H. Abdullah, M. Taib","doi":"10.1109/ICBAPS.2015.7292211","DOIUrl":"https://doi.org/10.1109/ICBAPS.2015.7292211","url":null,"abstract":"All living body has been shown to emit radiation into space surrounding their body. The radiation field encloses the physical body and emits the characteristics of frequency radiation. This study discusses the analysis of human body radiation wave on the human torso and compares their frequency characteristics between genders. At first, the characteristic of radiation frequency is determined by employing statistical analysis of correlation and analysis of variance. The results show that the characteristic difference of radiation frequency between males and females in human torso is significant. Then, for the purpose of classification, the k-nearest neighbor is used as classification algorithm. The results show that the proposed technique properly classifies gender with accuracy of 100 percent. Experimental results recommend that the proposed technique is appropriate and capable to classify gender using frequency analysis of the human torso radiation.","PeriodicalId":243293,"journal":{"name":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121779473","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}
Pub Date : 2015-05-26DOI: 10.1109/ICBAPS.2015.7292222
Guang Yong, K. H. Hong Ping, Andrew Sia Chew Chie, S. W. Ng, T. Masri
Forward-Backward Time-Stepping (FBTS) technique is used for the detection, imaging and reconstruction of an embedded object which is formulated at the time-domain utilizing Finite-Difference Time-Domain (FDTD) method. In order to solve FBTS inverse scattering problem, edge-preserving regularization is integrated. Image reconstruction results illustrated that the FBTS integrated with an edge-preserving regularization technique has the potential to detect the presence of the embedded object accurately. In this paper, an extended algorithm is shown in time-domain image reconstruction.
{"title":"Preliminary study of Forward-Backward Time-Stepping technique with edge-preserving regularization for object detection applications","authors":"Guang Yong, K. H. Hong Ping, Andrew Sia Chew Chie, S. W. Ng, T. Masri","doi":"10.1109/ICBAPS.2015.7292222","DOIUrl":"https://doi.org/10.1109/ICBAPS.2015.7292222","url":null,"abstract":"Forward-Backward Time-Stepping (FBTS) technique is used for the detection, imaging and reconstruction of an embedded object which is formulated at the time-domain utilizing Finite-Difference Time-Domain (FDTD) method. In order to solve FBTS inverse scattering problem, edge-preserving regularization is integrated. Image reconstruction results illustrated that the FBTS integrated with an edge-preserving regularization technique has the potential to detect the presence of the embedded object accurately. In this paper, an extended algorithm is shown in time-domain image reconstruction.","PeriodicalId":243293,"journal":{"name":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123789621","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}
Pub Date : 2015-05-26DOI: 10.1109/ICBAPS.2015.7292234
H. Abdullah, D. Cvetkovic
Phase amplitude coupling of neuronal oscillations has been suggested to link with upper brain functions such as cognitive and memory process. It is suggested that cross frequency coupling (CFC) occurred when the amplitude of fast oscillation is modulated by the phase of slow oscillation. In this study, we assess CFC in terms of Modulation Index (MI) of theta, low gamma (LG) and high gamma (HG) in sleep stages N1, N2, N3 and REM of healthy and sleep apnoea patients. The results showed theta phase modulated more the HG band in all sleep stages. Theta-HG coupling was more pronounced in the sleep apnoea as compared to the healthy.
{"title":"Phase amplitude coupling of theta-gamma EEG frequency bands in sleep apnoea","authors":"H. Abdullah, D. Cvetkovic","doi":"10.1109/ICBAPS.2015.7292234","DOIUrl":"https://doi.org/10.1109/ICBAPS.2015.7292234","url":null,"abstract":"Phase amplitude coupling of neuronal oscillations has been suggested to link with upper brain functions such as cognitive and memory process. It is suggested that cross frequency coupling (CFC) occurred when the amplitude of fast oscillation is modulated by the phase of slow oscillation. In this study, we assess CFC in terms of Modulation Index (MI) of theta, low gamma (LG) and high gamma (HG) in sleep stages N1, N2, N3 and REM of healthy and sleep apnoea patients. The results showed theta phase modulated more the HG band in all sleep stages. Theta-HG coupling was more pronounced in the sleep apnoea as compared to the healthy.","PeriodicalId":243293,"journal":{"name":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121265172","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}
Pub Date : 2015-05-26DOI: 10.1109/ICBAPS.2015.7292209
Fatema-tuz-Zohra Iqbal, K. Sidek
In this paper, a Cardioid graph based feature extraction technique is applied to perform compressed Electrocardiogram (ECG) biometric at different physiological conditions. To the best of our knowledge, Cardioid graph based method has not been implemented on compressed ECG before. Another merit of this methodology is that no decompression of the compressed ECG signal is necessary before the recognition step. The QRS complexes obtained from the ECG signal is compressed using Discrete Wavelet Transform (DWT), followed by the Cardioid graph retrieval procedure. Compression is performed in three decomposition levels and with the first three Daubechies wavelets. Classification is conducted on all the three levels using Multilayer Perceptron (MLP) Neural Network. Maximum compression of 88.3% is achieved with an accuracy rate of 93.06%. For compression rate of 85%, the identification rate obtained is 95.3%. Highest recognition rate of 96.4% is attained when the compression ratio is 75%. The classification accuracy rates suggest that compressed ECG biometric in varying physiological conditions with Cardioid graph based feature extraction is feasible and is capable of producing a robust biometric system.
{"title":"Cardioid graph based ECG biometric using compressed QRS complex","authors":"Fatema-tuz-Zohra Iqbal, K. Sidek","doi":"10.1109/ICBAPS.2015.7292209","DOIUrl":"https://doi.org/10.1109/ICBAPS.2015.7292209","url":null,"abstract":"In this paper, a Cardioid graph based feature extraction technique is applied to perform compressed Electrocardiogram (ECG) biometric at different physiological conditions. To the best of our knowledge, Cardioid graph based method has not been implemented on compressed ECG before. Another merit of this methodology is that no decompression of the compressed ECG signal is necessary before the recognition step. The QRS complexes obtained from the ECG signal is compressed using Discrete Wavelet Transform (DWT), followed by the Cardioid graph retrieval procedure. Compression is performed in three decomposition levels and with the first three Daubechies wavelets. Classification is conducted on all the three levels using Multilayer Perceptron (MLP) Neural Network. Maximum compression of 88.3% is achieved with an accuracy rate of 93.06%. For compression rate of 85%, the identification rate obtained is 95.3%. Highest recognition rate of 96.4% is attained when the compression ratio is 75%. The classification accuracy rates suggest that compressed ECG biometric in varying physiological conditions with Cardioid graph based feature extraction is feasible and is capable of producing a robust biometric system.","PeriodicalId":243293,"journal":{"name":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115234425","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}
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