Pub Date : 2015-03-03DOI: 10.1109/AISP.2015.7123487
M. Sigari, H. Soltanian-Zadeh, Vahid Kiani, Amid-Reza Pourreza
This paper presents a new method for counterattack detection using estimated camera motion and evaluates some classification methods to detect this event. To this end, video is partitioned to shots and view type of each shot is recognized first. Then, relative pan of the camera during far-view and medium-view shots is estimated. After weighting of pan value of each frame according to the type of shots, the video is partitioned to motion segments. Then, motion segments are refined to achieve better results. Finally, the features extracted from consecutive motion segments are investigated for counterattack detection. We propose two methods for counterattack detection: (1) rule-based (heuristic rules) and (2) SVM-based. Experiments show that the SVM classifier with linear or RBF kernel results in the best results.
{"title":"Counterattack detection in broadcast soccer videos using camera motion estimation","authors":"M. Sigari, H. Soltanian-Zadeh, Vahid Kiani, Amid-Reza Pourreza","doi":"10.1109/AISP.2015.7123487","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123487","url":null,"abstract":"This paper presents a new method for counterattack detection using estimated camera motion and evaluates some classification methods to detect this event. To this end, video is partitioned to shots and view type of each shot is recognized first. Then, relative pan of the camera during far-view and medium-view shots is estimated. After weighting of pan value of each frame according to the type of shots, the video is partitioned to motion segments. Then, motion segments are refined to achieve better results. Finally, the features extracted from consecutive motion segments are investigated for counterattack detection. We propose two methods for counterattack detection: (1) rule-based (heuristic rules) and (2) SVM-based. Experiments show that the SVM classifier with linear or RBF kernel results in the best results.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132677280","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 : 2015-03-03DOI: 10.1109/AISP.2015.7123514
A. Mirkazemi, S. E. Alavi, G. Akbarizadeh
In this paper, a new method for color image segmentation is presented. This method is based on histogram thresholding and correlation between the difference of color components. Hence, nearly all histogram thresholding methods work only in one or two dimensions of gray scale histogram, neighborhood, probability function or entropy. The proposed method will try to use color components as the main features of segmentation by finding the correlation between the peaks of histogram in each color component. It will help us to find main color components of each object and the background of image. While, we have main color components; it will be easy to use parallel processing to segment entire image at once without using any neighborhood window or losing any data in color space transform into gray scale. With these benefits, a fast and accurate method based on adaptive histogram thresholding is presented in this paper for segmentation of color images. The experimental results on benchmark datasets demonstrate the efficiency of the proposed method.
{"title":"Fast image segmentation based on adaptive histogram thresholding","authors":"A. Mirkazemi, S. E. Alavi, G. Akbarizadeh","doi":"10.1109/AISP.2015.7123514","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123514","url":null,"abstract":"In this paper, a new method for color image segmentation is presented. This method is based on histogram thresholding and correlation between the difference of color components. Hence, nearly all histogram thresholding methods work only in one or two dimensions of gray scale histogram, neighborhood, probability function or entropy. The proposed method will try to use color components as the main features of segmentation by finding the correlation between the peaks of histogram in each color component. It will help us to find main color components of each object and the background of image. While, we have main color components; it will be easy to use parallel processing to segment entire image at once without using any neighborhood window or losing any data in color space transform into gray scale. With these benefits, a fast and accurate method based on adaptive histogram thresholding is presented in this paper for segmentation of color images. The experimental results on benchmark datasets demonstrate the efficiency of the proposed method.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114591734","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 : 2015-03-03DOI: 10.1109/AISP.2015.7123508
Mehran Jodavi, M. Abadi, Elham Parhizkar
JavaScript code obfuscation has become a major technique used by malware writers to evade static analysis techniques. Over the past years, a number of dynamic analysis techniques have been proposed to detect obfuscated malicious JavaScript code at runtime. However, because of their runtime overheads, these techniques are slow and thus not widely used in practice. On the other hand, since a large quantity of benign JavaScript code is obfuscated to protect intellectual property, it is not effective to use the intrinsic features of obfuscated JavaScript code for static analysis purposes. Therefore, we are forced to distinguish between obfuscated and non-obfuscated JavaScript code so that we can devise an efficient and effective analysis technique to detect malicious JavaScript code. In this paper, we address this issue by presenting JSObfusDetector, a novel one-class classifier ensemble to detect obfuscated JavaScript code. To construct the classifier ensemble, we apply a binary particle swarm optimization (PSO) algorithm, called ParticlePruner, on an initial ensemble of one-class SVM classifiers to find a sub-ensemble whose members are both accurate and have diversity in their outputs. We evaluate JSObfusDetector using a dataset of obfuscated and non-obfuscated JavaScript code. The experimental results show that JSObfusDetector can achieve about 97% precision, 91 % recall, and 94% F-measure.
{"title":"JSObfusDetector: A binary PSO-based one-class classifier ensemble to detect obfuscated JavaScript code","authors":"Mehran Jodavi, M. Abadi, Elham Parhizkar","doi":"10.1109/AISP.2015.7123508","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123508","url":null,"abstract":"JavaScript code obfuscation has become a major technique used by malware writers to evade static analysis techniques. Over the past years, a number of dynamic analysis techniques have been proposed to detect obfuscated malicious JavaScript code at runtime. However, because of their runtime overheads, these techniques are slow and thus not widely used in practice. On the other hand, since a large quantity of benign JavaScript code is obfuscated to protect intellectual property, it is not effective to use the intrinsic features of obfuscated JavaScript code for static analysis purposes. Therefore, we are forced to distinguish between obfuscated and non-obfuscated JavaScript code so that we can devise an efficient and effective analysis technique to detect malicious JavaScript code. In this paper, we address this issue by presenting JSObfusDetector, a novel one-class classifier ensemble to detect obfuscated JavaScript code. To construct the classifier ensemble, we apply a binary particle swarm optimization (PSO) algorithm, called ParticlePruner, on an initial ensemble of one-class SVM classifiers to find a sub-ensemble whose members are both accurate and have diversity in their outputs. We evaluate JSObfusDetector using a dataset of obfuscated and non-obfuscated JavaScript code. The experimental results show that JSObfusDetector can achieve about 97% precision, 91 % recall, and 94% F-measure.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128579778","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 : 2015-03-03DOI: 10.1109/AISP.2015.7123496
Maryam Dashti, S. S. Ghidary, Tahmineh Hosseinian, Mohammadreza Pourfard, K. Faez
The Sparsity concept has been widely used in image processing applications. In this paper, an approach for super-resolution has been proposed which uses sparse transform. This approach has mixed the inpainting concept with zooming via a sparse representation. A dictionary is being trained from a low-resolution image and then a zoomed version of this low resolution image will use that dictionary in a few iterations to fill the undefined image pixels. Experimental results confirm the strength of this algorithm against the other interpolation algorithms.
{"title":"Super-resolution via a patch-based sparse algorithm","authors":"Maryam Dashti, S. S. Ghidary, Tahmineh Hosseinian, Mohammadreza Pourfard, K. Faez","doi":"10.1109/AISP.2015.7123496","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123496","url":null,"abstract":"The Sparsity concept has been widely used in image processing applications. In this paper, an approach for super-resolution has been proposed which uses sparse transform. This approach has mixed the inpainting concept with zooming via a sparse representation. A dictionary is being trained from a low-resolution image and then a zoomed version of this low resolution image will use that dictionary in a few iterations to fill the undefined image pixels. Experimental results confirm the strength of this algorithm against the other interpolation algorithms.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125550947","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 : 2015-03-03DOI: 10.1109/AISP.2015.7123524
Seyyedeh Hamideh Sojoudi Ziyabari, A. Shahbahrami
Optimization can be defined as the act of getting the best result under given circumstances. Evolutionary algorithms are widely used for solving optimization problems. One of these evolutionary algorithms is Particle Swarm Optimization (PSO). Different kinds of PSO such as Adaptive Particle Swarm Optimization (APSO), have been presented to improve the original PSO and eliminate its disadvantages. Although APSO can overcome the problem of premature convergence and accelerate the convergence speed at the same time, it is computationally intensive because of its nested loops. The goal of this paper is high performance implementation of APSO algorithm based on GPU. In order to analyze this algorithm and evaluate its computational time, we have implemented APSO on both CPU and GPU. Different parallelisms such as loop-level parallelism have been exploited and we have achieved significant speedup up to 152x compared to CPU based implementation.
{"title":"High performance implementation of APSO algorithm using GPU platform","authors":"Seyyedeh Hamideh Sojoudi Ziyabari, A. Shahbahrami","doi":"10.1109/AISP.2015.7123524","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123524","url":null,"abstract":"Optimization can be defined as the act of getting the best result under given circumstances. Evolutionary algorithms are widely used for solving optimization problems. One of these evolutionary algorithms is Particle Swarm Optimization (PSO). Different kinds of PSO such as Adaptive Particle Swarm Optimization (APSO), have been presented to improve the original PSO and eliminate its disadvantages. Although APSO can overcome the problem of premature convergence and accelerate the convergence speed at the same time, it is computationally intensive because of its nested loops. The goal of this paper is high performance implementation of APSO algorithm based on GPU. In order to analyze this algorithm and evaluate its computational time, we have implemented APSO on both CPU and GPU. Different parallelisms such as loop-level parallelism have been exploited and we have achieved significant speedup up to 152x compared to CPU based implementation.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124179567","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 : 2015-03-03DOI: 10.1109/AISP.2015.7123516
A. Ahmadi, Atefeh Mozafarinia, Azadeh Mohebi
Particle swarm optimization (PSO) is a practical and effective optimization approach that has been applied recently for data clustering in many applications. While various non-evolutionary optimization and clustering algorithms have been applied for clustering multivariate time series in some applications such as customer segmentation, they usually provide poor results due to their dependency on the initial values and their poor performance in manipulating multiple objectives. In this paper, a particle swarm optimization algorithm is proposed for clustering multivariate time series data. Since the time series data sometimes do not have the same length and they usually have missing data, the regular Euclidean distance and dynamic time warping can not be applied for such data to measure the similarity. Therefore, a hybrid similarity measure based on principal component analysis and Mahalanobis distance is applied in order to handle such limitations. The comparison between the results of the proposed method with the similar ones in the literature shows the superiority of the proposed method.
{"title":"Clustering of multivariate time series data using particle swarm optimization","authors":"A. Ahmadi, Atefeh Mozafarinia, Azadeh Mohebi","doi":"10.1109/AISP.2015.7123516","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123516","url":null,"abstract":"Particle swarm optimization (PSO) is a practical and effective optimization approach that has been applied recently for data clustering in many applications. While various non-evolutionary optimization and clustering algorithms have been applied for clustering multivariate time series in some applications such as customer segmentation, they usually provide poor results due to their dependency on the initial values and their poor performance in manipulating multiple objectives. In this paper, a particle swarm optimization algorithm is proposed for clustering multivariate time series data. Since the time series data sometimes do not have the same length and they usually have missing data, the regular Euclidean distance and dynamic time warping can not be applied for such data to measure the similarity. Therefore, a hybrid similarity measure based on principal component analysis and Mahalanobis distance is applied in order to handle such limitations. The comparison between the results of the proposed method with the similar ones in the literature shows the superiority of the proposed method.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130783054","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 : 2015-03-03DOI: 10.1109/AISP.2015.7123505
Mehran Fotouhi, Afshin Bozorgpour, S. Kasaei
Automatic analysis in soccer scenes is still a difficult task in the absence of soccer field information. The first and most important step in almost all analysis, is soccer field line recognition and homography extraction. The aim of this paper is introducing a novel approach for automatic detection and recognition of soccer field lines and arcs by minimal information. A simple camera model and perspective map is assumed to reduce unknown parameters. An accurate method is utilized for detecting line pixels. The side of playfield area is determined based on the orientation of lines and arcs. Based on the detected playfield area side, an initial perspective map is obtained. An optimization algorithm then adjusts the parameters of perspective transform and camera. The proposed method needs only some minimal information in theory and practice. It is applied to some typical soccer videos. The achieved results demonstrate its robustness and accuracy.
{"title":"Automatic soccer field line recognition by minimum information","authors":"Mehran Fotouhi, Afshin Bozorgpour, S. Kasaei","doi":"10.1109/AISP.2015.7123505","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123505","url":null,"abstract":"Automatic analysis in soccer scenes is still a difficult task in the absence of soccer field information. The first and most important step in almost all analysis, is soccer field line recognition and homography extraction. The aim of this paper is introducing a novel approach for automatic detection and recognition of soccer field lines and arcs by minimal information. A simple camera model and perspective map is assumed to reduce unknown parameters. An accurate method is utilized for detecting line pixels. The side of playfield area is determined based on the orientation of lines and arcs. Based on the detected playfield area side, an initial perspective map is obtained. An optimization algorithm then adjusts the parameters of perspective transform and camera. The proposed method needs only some minimal information in theory and practice. It is applied to some typical soccer videos. The achieved results demonstrate its robustness and accuracy.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128993117","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 : 2015-03-03DOI: 10.1109/AISP.2015.7123481
A. Sadeghian, H. Nezamabadi-pour
Text Mining is a field that is considered as an extension of data mining. In the context of text mining, document clustering is used to set apart likewise documents of a collection into the identical category, called cluster, and divergent documents to distinctive groups. Since every dataset has its own characteristics, finding an appropriate clustering algorithm that can manage all kinds of clusters, is a big challenge. Clustering algorithms has theirs unique approaches for computing the number of clusters, imposing a structure on the data, and attesting the out coming clusters. The idea of combining different clustering is an effort to overwhelm the faults of single algorithms and further enhance their executions. On the other hand, inspired by the gravitational law, different clustering algorithms have been introduced that each one attempted to cluster complex datasets. Gravitational Ensemble Clustering (GEC) is an ensemble method that employs both the concepts of gravitational clustering and ensemble clustering to reach a better clustering result. This paper represents an application of GEC to the problem of document clustering. The proposed method uses a modification of the original GEC algorithm. This modification tries to produce a more varied clustering ensemble using new parameter setting. The GEC algorithm is assessed using document datasets. Promising results of the presented method were obtained in comparison with competing algorithms.
{"title":"Document clustering using gravitational ensemble clustering","authors":"A. Sadeghian, H. Nezamabadi-pour","doi":"10.1109/AISP.2015.7123481","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123481","url":null,"abstract":"Text Mining is a field that is considered as an extension of data mining. In the context of text mining, document clustering is used to set apart likewise documents of a collection into the identical category, called cluster, and divergent documents to distinctive groups. Since every dataset has its own characteristics, finding an appropriate clustering algorithm that can manage all kinds of clusters, is a big challenge. Clustering algorithms has theirs unique approaches for computing the number of clusters, imposing a structure on the data, and attesting the out coming clusters. The idea of combining different clustering is an effort to overwhelm the faults of single algorithms and further enhance their executions. On the other hand, inspired by the gravitational law, different clustering algorithms have been introduced that each one attempted to cluster complex datasets. Gravitational Ensemble Clustering (GEC) is an ensemble method that employs both the concepts of gravitational clustering and ensemble clustering to reach a better clustering result. This paper represents an application of GEC to the problem of document clustering. The proposed method uses a modification of the original GEC algorithm. This modification tries to produce a more varied clustering ensemble using new parameter setting. The GEC algorithm is assessed using document datasets. Promising results of the presented method were obtained in comparison with competing algorithms.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127357155","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 : 2015-03-03DOI: 10.1109/AISP.2015.7123528
Mohsen Fayyaz, M. H. Saffar, M. Sabokrou, M. Hoseini, M. Fathy
Signature verification techniques employ various specifications of a signature. Feature extraction and feature selection have an enormous effect on accuracy of signature verification. Feature extraction is a difficult phase of signature verification systems due to different shapes of signatures and different situations of sampling. This paper presents a method based on feature learning, in which a sparse autoencoder tries to learn features of signatures. Then learned features have been employed to present users' signatures. Finally, users' signatures have been classified using one-class classifiers. The proposed method is signature shape independent thanks to learning features from users' signatures using autoencoder. Verification process of proposed system is evaluated on SVC2004 signature database, which contains genuine and skilled forgery signatures. The experimental results indicate error reduction and accuracy enhancement.
{"title":"Online signature verification based on feature representation","authors":"Mohsen Fayyaz, M. H. Saffar, M. Sabokrou, M. Hoseini, M. Fathy","doi":"10.1109/AISP.2015.7123528","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123528","url":null,"abstract":"Signature verification techniques employ various specifications of a signature. Feature extraction and feature selection have an enormous effect on accuracy of signature verification. Feature extraction is a difficult phase of signature verification systems due to different shapes of signatures and different situations of sampling. This paper presents a method based on feature learning, in which a sparse autoencoder tries to learn features of signatures. Then learned features have been employed to present users' signatures. Finally, users' signatures have been classified using one-class classifiers. The proposed method is signature shape independent thanks to learning features from users' signatures using autoencoder. Verification process of proposed system is evaluated on SVC2004 signature database, which contains genuine and skilled forgery signatures. The experimental results indicate error reduction and accuracy enhancement.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133768297","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 : 2015-03-03DOI: 10.1109/AISP.2015.7123515
Atiye Riasi, M. Mohebbi
Ventricular tachyarrhythmia particularly ventricular tachycardia (VT) and ventricular fibrillation (VF) are the main causes of sudden cardiac death in the world. A reliable predictor of an imminent episode of ventricular tachycardia that could be incorporated in an implantable defibrillator capable of preventive therapy would have important clinical utilities. As variability of T wave, ST segment and QT interval are indicators of cardiac instability, these changes can lead us to develop accurate predictor for VT. In this study, we present an algorithm that predicts VT using morphological features of electrical signal of ventricles activity obtained from Electrocardiogram (ECG). Changes in T wave, ST segment, QT interval and numbers of premature ventricular complexes(PVCs) are considered as effective indicators of VT. Classification of selected features by a Support Vector Machine (SVM) can identify hidden patterns in ECG signals before VT occurrence. Evaluation of this algorithm on 40 recods of VT patient and 40 control records shows that the proposed algorithm can reach sensitivity of 88% and specificity of 100% in VT prediction.
{"title":"Prediction of ventricular tachycardia using morphological features of ECG signal","authors":"Atiye Riasi, M. Mohebbi","doi":"10.1109/AISP.2015.7123515","DOIUrl":"https://doi.org/10.1109/AISP.2015.7123515","url":null,"abstract":"Ventricular tachyarrhythmia particularly ventricular tachycardia (VT) and ventricular fibrillation (VF) are the main causes of sudden cardiac death in the world. A reliable predictor of an imminent episode of ventricular tachycardia that could be incorporated in an implantable defibrillator capable of preventive therapy would have important clinical utilities. As variability of T wave, ST segment and QT interval are indicators of cardiac instability, these changes can lead us to develop accurate predictor for VT. In this study, we present an algorithm that predicts VT using morphological features of electrical signal of ventricles activity obtained from Electrocardiogram (ECG). Changes in T wave, ST segment, QT interval and numbers of premature ventricular complexes(PVCs) are considered as effective indicators of VT. Classification of selected features by a Support Vector Machine (SVM) can identify hidden patterns in ECG signals before VT occurrence. Evaluation of this algorithm on 40 recods of VT patient and 40 control records shows that the proposed algorithm can reach sensitivity of 88% and specificity of 100% in VT prediction.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121259965","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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