Pub Date : 2012-07-02DOI: 10.1109/ISSPA.2012.6310655
Ines Ben Messaoud, H. Amiri, H. E. Abed, V. Märgner
Document annotation is considered as the definition of logical and physical structures of document. Several works for page layout analysis have been presented but there is no standard annotation for one document. In order to draw the relationship between these different presentations, a Mapping should be applied between different annotations. The question that should be answered is how efficient is this Mapping? We present in this paper a framework allowing the generation of a standard annotation for each document database. The description of the page layout analysis is based on XML metadata. The Mapping is applied between the standard document and a new document in order to allow the correspondence between them. The Mapping is evaluated using a proposed model based on Markov models. The results show that the probability rate for the Mapping evaluation varies from 0.6 to 1.
{"title":"New evaluation framework for metadata mapping approaches based on Markov models","authors":"Ines Ben Messaoud, H. Amiri, H. E. Abed, V. Märgner","doi":"10.1109/ISSPA.2012.6310655","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310655","url":null,"abstract":"Document annotation is considered as the definition of logical and physical structures of document. Several works for page layout analysis have been presented but there is no standard annotation for one document. In order to draw the relationship between these different presentations, a Mapping should be applied between different annotations. The question that should be answered is how efficient is this Mapping? We present in this paper a framework allowing the generation of a standard annotation for each document database. The description of the page layout analysis is based on XML metadata. The Mapping is applied between the standard document and a new document in order to allow the correspondence between them. The Mapping is evaluated using a proposed model based on Markov models. The results show that the probability rate for the Mapping evaluation varies from 0.6 to 1.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117143747","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 : 2012-07-02DOI: 10.1109/ISSPA.2012.6310558
M. Giacalone, G. Berrettini, S. Iannaccone, L. Potí
This work presents the latest simulations and experimental results of an innovative module for functional diffused optical imaging based on double encoding of wavelength and position of the emitting light. A spread spectrum approach to near infrared diffusive optical imaging is implemented: a pseudo-random sequence is used to modulate the light before entering a turbid medium; when the coded sequence propagates through the tissue, it is split into a group of components that have different path lengths. The correlation of the detected signals with the sequence can pick up each component with a specific delay, with the consequence of obtaining time domain information of arriving replicas. We propose to improve that approach, adopting a code division multiple access technology. A module which is expected to enhance the performances in terms of measured time and SNR is presented. We called this technique Wavelength and Space Code Division Multiplexing (WS-CDM).
{"title":"Wavelength and code division multiplexing toward diffuse optical imaging","authors":"M. Giacalone, G. Berrettini, S. Iannaccone, L. Potí","doi":"10.1109/ISSPA.2012.6310558","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310558","url":null,"abstract":"This work presents the latest simulations and experimental results of an innovative module for functional diffused optical imaging based on double encoding of wavelength and position of the emitting light. A spread spectrum approach to near infrared diffusive optical imaging is implemented: a pseudo-random sequence is used to modulate the light before entering a turbid medium; when the coded sequence propagates through the tissue, it is split into a group of components that have different path lengths. The correlation of the detected signals with the sequence can pick up each component with a specific delay, with the consequence of obtaining time domain information of arriving replicas. We propose to improve that approach, adopting a code division multiple access technology. A module which is expected to enhance the performances in terms of measured time and SNR is presented. We called this technique Wavelength and Space Code Division Multiplexing (WS-CDM).","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114192945","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 : 2012-07-02DOI: 10.1109/ISSPA.2012.6310587
Y. Petetin, F. Desbouvries
Multi-target filtering aims at tracking an unknown number of targets from a set of observations. The Probability Hypothesis Density (PHD) Filter is a promising solution but cannot be implemented exactly. Suboptimal implementation techniques include Gaussian Mixture (GM) solutions, which hold only in linear and Gaussian models, and Sequential Monte Carlo (SMC) algorithms, which estimate the number of targets and their state parameters for a more general class of models. In this paper, we address the case of Gaussian models where the state can be decomposed into a linear component and a non-linear one, and we show that the use of SMC methods in such models can indeed be reduced. Our technique not only improves the estimate of the number of targets but also that of their state. We finally adapt the technique to linear and Gaussian jump Markov state space systems (JMSS) in order to reduce the intractability of existing solutions, and to JMSS with partially linear and partially non-linear state vector.
Multi-target filtering aims at tracking an unknown number of targets from a set of observations. 概率假设密度(PHD)滤波器是一种很有前途的解决方案，但不能精确实现。次优实现技术包括高斯混合(GM)解决方案，它只适用于线性和高斯模型，以及顺序蒙特卡罗(SMC)算法，它可以估计更一般类型的模型的目标数量及其状态参数。在本文中，我们讨论了高斯模型的情况，其中状态可以分解为一个线性分量和一个非线性分量，并且我们表明在这种模型中使用SMC方法确实可以减少。该方法不仅提高了对目标数量的估计，而且提高了对目标状态的估计。最后，我们将该技术应用于线性和高斯跃马尔可夫状态空间系统(JMSS)，以减少现有解的难解性，以及部分线性和部分非线性状态向量的JMSS。
{"title":"Marginalized PHD Filters for multi-target filtering","authors":"Y. Petetin, F. Desbouvries","doi":"10.1109/ISSPA.2012.6310587","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310587","url":null,"abstract":"Multi-target filtering aims at tracking an unknown number of targets from a set of observations. The Probability Hypothesis Density (PHD) Filter is a promising solution but cannot be implemented exactly. Suboptimal implementation techniques include Gaussian Mixture (GM) solutions, which hold only in linear and Gaussian models, and Sequential Monte Carlo (SMC) algorithms, which estimate the number of targets and their state parameters for a more general class of models. In this paper, we address the case of Gaussian models where the state can be decomposed into a linear component and a non-linear one, and we show that the use of SMC methods in such models can indeed be reduced. Our technique not only improves the estimate of the number of targets but also that of their state. We finally adapt the technique to linear and Gaussian jump Markov state space systems (JMSS) in order to reduce the intractability of existing solutions, and to JMSS with partially linear and partially non-linear state vector.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"368 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114379528","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 : 2012-07-02DOI: 10.1109/ISSPA.2012.6310668
M. Kedir-Talha, S. Ould-Slimane
The diagnosis of cardiac dysfunctions requires the analysis of long-term ECG signal recordings, often containing hundreds to thousands of heartbeats. The purpose of this work is to propose a diagnostic system for modelling and classification of heartbeat, by use of time features and Support vector machines (SVM) classification algorithm. Neural Networks learning allow us to select a features of each heart beat on the basis of Generalized Orthogonal Forward Regression (GOFR) algorithm and a library of 132 Gaussians with different standard deviations and different means, each beat is represented by five Gaussians with different amplitudes. The parameters of this system are determined and its performance is evaluated for the MIT-BIH arrhythmia database. For a database of 364 normal heartbeats and 1148 abnormal heartbeats, we apply the SVM algorithm with Radial Basis Function kernel. Our results demonstrate that the testing performance of the neural network and SVM diagnostic system is found to be very satisfactory with a recognition rate of 99.67%.
{"title":"Neural networks and SVM for heartbeat classification","authors":"M. Kedir-Talha, S. Ould-Slimane","doi":"10.1109/ISSPA.2012.6310668","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310668","url":null,"abstract":"The diagnosis of cardiac dysfunctions requires the analysis of long-term ECG signal recordings, often containing hundreds to thousands of heartbeats. The purpose of this work is to propose a diagnostic system for modelling and classification of heartbeat, by use of time features and Support vector machines (SVM) classification algorithm. Neural Networks learning allow us to select a features of each heart beat on the basis of Generalized Orthogonal Forward Regression (GOFR) algorithm and a library of 132 Gaussians with different standard deviations and different means, each beat is represented by five Gaussians with different amplitudes. The parameters of this system are determined and its performance is evaluated for the MIT-BIH arrhythmia database. For a database of 364 normal heartbeats and 1148 abnormal heartbeats, we apply the SVM algorithm with Radial Basis Function kernel. Our results demonstrate that the testing performance of the neural network and SVM diagnostic system is found to be very satisfactory with a recognition rate of 99.67%.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125740680","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 : 2012-07-02DOI: 10.1109/ISSPA.2012.6310476
Yu-Chen Kao, Berlin Chen
Modulation spectrum processing of speech features has recently become an active area of intensive research in the speech recognition community. As for normalization of modulation spectra, spectral histogram equalization (SHE) seems to be one of the most effective techniques that have been used to compensate the nonlinear distortion. In this paper, we investigate a novel use of polynomial-fitting techniques for modulation histogram equalization, which has the advantages of lower storage and time consumption when compared with the conventional SHE methods. Further, we also investigated the possibility of combining our approach with other temporal feature normalization methods. The automatic speech recognition (ASR) experiments were carried out on the Aurora-2 standard noise-robust ASR task. The performance of the proposed approach was thoroughly tested and verified by comparisons with the other popular modulation spectrum normalization methods, which suggests the utility of the proposed approach.
{"title":"Leveraging distributional characteristics of modulation spectra for robust speech recognition","authors":"Yu-Chen Kao, Berlin Chen","doi":"10.1109/ISSPA.2012.6310476","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310476","url":null,"abstract":"Modulation spectrum processing of speech features has recently become an active area of intensive research in the speech recognition community. As for normalization of modulation spectra, spectral histogram equalization (SHE) seems to be one of the most effective techniques that have been used to compensate the nonlinear distortion. In this paper, we investigate a novel use of polynomial-fitting techniques for modulation histogram equalization, which has the advantages of lower storage and time consumption when compared with the conventional SHE methods. Further, we also investigated the possibility of combining our approach with other temporal feature normalization methods. The automatic speech recognition (ASR) experiments were carried out on the Aurora-2 standard noise-robust ASR task. The performance of the proposed approach was thoroughly tested and verified by comparisons with the other popular modulation spectrum normalization methods, which suggests the utility of the proposed approach.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125124171","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 : 2012-07-02DOI: 10.1109/ISSPA.2012.6310541
Mohammadreza Mostajabi, I. Gholampour
Processing time and segmentation quality are two main factors in evaluation of image segmentation methods. Oversegmentation is one of the most challenging problems that significantly degrade the segmentation quality. This paper presents a framework for decreasing the oversegmentation rate and improving the processing time. Significant variations in both color and texture spaces are the main reasons that lead to oversegmentation. We exploit Affine Invariant Region Detectors to mark regions with high variations in both color and texture spaces. The results are then utilized to reduce the oversegmentation rate of image segmentation algorithms. The performance of the proposed framework is evaluated in decreasing the oversegmentation rate of the well-known Mean Shift method. In conjunction with the proposed framework, we have applied some optimizations on the Mean Shift method to reduce the processing time. In comparison with the original Mean Shift, our experimental results show a twofold speedup and improved segmentation quality.
{"title":"A framework based on the Affine Invariant Regions for improving unsupervised image segmentation","authors":"Mohammadreza Mostajabi, I. Gholampour","doi":"10.1109/ISSPA.2012.6310541","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310541","url":null,"abstract":"Processing time and segmentation quality are two main factors in evaluation of image segmentation methods. Oversegmentation is one of the most challenging problems that significantly degrade the segmentation quality. This paper presents a framework for decreasing the oversegmentation rate and improving the processing time. Significant variations in both color and texture spaces are the main reasons that lead to oversegmentation. We exploit Affine Invariant Region Detectors to mark regions with high variations in both color and texture spaces. The results are then utilized to reduce the oversegmentation rate of image segmentation algorithms. The performance of the proposed framework is evaluated in decreasing the oversegmentation rate of the well-known Mean Shift method. In conjunction with the proposed framework, we have applied some optimizations on the Mean Shift method to reduce the processing time. In comparison with the original Mean Shift, our experimental results show a twofold speedup and improved segmentation quality.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124005078","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 : 2012-07-02DOI: 10.1109/ISSPA.2012.6310469
X. Zhong, Z. Madadi, A. Benjamin Premkumar
This paper considers the problem of tracking an acoustic sources in three dimensional (3-D) space by using a vertical acoustic vector sensor (AVS) array in a shallow ocean environment. The innovations of this work are double fold: 1) a particle filtering (PF) approach is developed to track the position of an acoustic source; and 2) based on the source motion and wave propagation models, the posterior Cramér-Rao bound (PCRB) is derived to provide a lower performance bound of 3-D position tracking in shallow ocean. The PF approach uses a number of samples to approximate the posterior distribution of interested parameters, by which a complex 3-D search can be avoided for 3-D position estimation. Also, due to incorporating both the source dynamic and measurement information, the tracking approach is able to provide a lower performance bound than the traditional localization approach. The tracking performance is further demonstrated by numerical experiments.
{"title":"Performance study of vertical acoustic vector sensor array based 3-D position tracking in a shallow ocean environment","authors":"X. Zhong, Z. Madadi, A. Benjamin Premkumar","doi":"10.1109/ISSPA.2012.6310469","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310469","url":null,"abstract":"This paper considers the problem of tracking an acoustic sources in three dimensional (3-D) space by using a vertical acoustic vector sensor (AVS) array in a shallow ocean environment. The innovations of this work are double fold: 1) a particle filtering (PF) approach is developed to track the position of an acoustic source; and 2) based on the source motion and wave propagation models, the posterior Cramér-Rao bound (PCRB) is derived to provide a lower performance bound of 3-D position tracking in shallow ocean. The PF approach uses a number of samples to approximate the posterior distribution of interested parameters, by which a complex 3-D search can be avoided for 3-D position estimation. Also, due to incorporating both the source dynamic and measurement information, the tracking approach is able to provide a lower performance bound than the traditional localization approach. The tracking performance is further demonstrated by numerical experiments.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121690856","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 : 2012-07-02DOI: 10.1109/ISSPA.2012.6310511
Lawrence R. Chen
We present a summary of our recent work on using lattice-form Mach-Zehnder interferometers implemented using silica-based planar lightwave circuit technology for optical signal processing. In particular, we demonstrate that the same device structure (either based on 6 taps or 12 taps) can be used to perform various signal processing functions ranging from pulse repetition rate multiplication to arbitrary waveform generation.
{"title":"Optical signal processing with planar lightwave circuits","authors":"Lawrence R. Chen","doi":"10.1109/ISSPA.2012.6310511","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310511","url":null,"abstract":"We present a summary of our recent work on using lattice-form Mach-Zehnder interferometers implemented using silica-based planar lightwave circuit technology for optical signal processing. In particular, we demonstrate that the same device structure (either based on 6 taps or 12 taps) can be used to perform various signal processing functions ranging from pulse repetition rate multiplication to arbitrary waveform generation.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121723938","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 : 2012-07-02DOI: 10.1109/ISSPA.2012.6310564
M. H. Bahari, H. V. hamme
In this paper, a new method called Maximum Likelihood General Regression (MLGR) is introduced for speaker adaptation. Gaussian means of a speaker independent (SI) model are adapted to the data of a new speaker by assuming a non-linear mapping from the SI Gaussian means to the adapted Gaussian means. MLGR performs a non-linear regression between ML estimates of the means and the SI means using General Regression Neural Network. The proposed method is evaluated on the Wall Street Journal database. Evaluation results show that the suggested scheme outperforms different conventional approaches in the case of short adaptation utterances. We also mathematically prove that the Gaussian means of the adapted model using the MLGR converges to their ML estimates in the case of long adaptation utterances.
{"title":"Speaker adaptation using Maximum Likelihood General Regression","authors":"M. H. Bahari, H. V. hamme","doi":"10.1109/ISSPA.2012.6310564","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310564","url":null,"abstract":"In this paper, a new method called Maximum Likelihood General Regression (MLGR) is introduced for speaker adaptation. Gaussian means of a speaker independent (SI) model are adapted to the data of a new speaker by assuming a non-linear mapping from the SI Gaussian means to the adapted Gaussian means. MLGR performs a non-linear regression between ML estimates of the means and the SI means using General Regression Neural Network. The proposed method is evaluated on the Wall Street Journal database. Evaluation results show that the suggested scheme outperforms different conventional approaches in the case of short adaptation utterances. We also mathematically prove that the Gaussian means of the adapted model using the MLGR converges to their ML estimates in the case of long adaptation utterances.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122108640","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 : 2012-07-02DOI: 10.1109/ISSPA.2012.6310674
Erfan Sayyari, Mohsen Farzi, Roohollah Rezaei Estakhrooeieh, F. Samiee, M. Shamsollahi
Migraine is a common type of headache with neurovascular origin. In this paper, a quantitative analysis of spontaneous EEG patterns is used to examine the migraine patients with maximum and minimum pain levels. The analysis is based on alpha band phase synchronization algorithm. The efficiency of extracted features are examined through one-way ANOVA test. we reached the P-value of 0.0001, proving that the EEG patterns are statistically discriminant in maximum and minimum pain levels. We also used a Neural Network based approach in order to classify the EEG patterns, distinguishing between minimum and maximum pain levels. We achieved the total accuracy of 90.9 %.
{"title":"Migraine analysis through EEG signals with classification approach","authors":"Erfan Sayyari, Mohsen Farzi, Roohollah Rezaei Estakhrooeieh, F. Samiee, M. Shamsollahi","doi":"10.1109/ISSPA.2012.6310674","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310674","url":null,"abstract":"Migraine is a common type of headache with neurovascular origin. In this paper, a quantitative analysis of spontaneous EEG patterns is used to examine the migraine patients with maximum and minimum pain levels. The analysis is based on alpha band phase synchronization algorithm. The efficiency of extracted features are examined through one-way ANOVA test. we reached the P-value of 0.0001, proving that the EEG patterns are statistically discriminant in maximum and minimum pain levels. We also used a Neural Network based approach in order to classify the EEG patterns, distinguishing between minimum and maximum pain levels. We achieved the total accuracy of 90.9 %.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123278251","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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