Body area network is widely used in remote healthcare area. In this paper, smartphone based body area network system (SBBANS) is proposed. SBBANS is constituted by smartphone and multi parameter monitors, using Bluetooth and serial port for data transmission, and provides comprehensive health information. The system is capable of transferring data with healthcare centre servers over 3G/4G/Wlan. A simple real-time lossless electrocardiogram (ECG) compression algorithm is applied before data transfer. To ensure the stability of the transmission in wireless networks, a "transmit-acknowledge-require-retransmit" mechanism is implemented on the application layer of Internet protocol in this study.
{"title":"Smartphone Based Body Area Network System","authors":"Youqun Shi, Yue Zhang","doi":"10.1109/ICMB.2014.42","DOIUrl":"https://doi.org/10.1109/ICMB.2014.42","url":null,"abstract":"Body area network is widely used in remote healthcare area. In this paper, smartphone based body area network system (SBBANS) is proposed. SBBANS is constituted by smartphone and multi parameter monitors, using Bluetooth and serial port for data transmission, and provides comprehensive health information. The system is capable of transferring data with healthcare centre servers over 3G/4G/Wlan. A simple real-time lossless electrocardiogram (ECG) compression algorithm is applied before data transfer. To ensure the stability of the transmission in wireless networks, a \"transmit-acknowledge-require-retransmit\" mechanism is implemented on the application layer of Internet protocol in this study.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114827306","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}
Classification with imbalanced data-sets has become one of the most popular issues in machine learning, since it prevails in various applications. For binary-class problem, the amount of instances from the majority class is significant larger than that from the minority class. Consequently, traditional classifiers achieve a better performance over the majority class, while unsatisfactory predictive accuracy over the minority class. The emergence of ensemble learning provides a possible solution of solving this concern. And there are many recent researches indicate that the combination of Boosting and/or Bagging with pre-processing techniques is an effective way to enhance the classification performance of imbalanced data-sets. Centered on binary-class imbalanced problem, to overcome the drawbacks of state-of-the-art approaches, this paper introduces a novel technique (DBBoost) based on the combination of AdaBoost with an adaptive sampling approach. Through supporting by statistical analysis, experiments show that DBBoost outperforms the state-of-the-art methods based on ensemble.
{"title":"DBBoost-Enhancing Imbalanced Classification by a Novel Ensemble Based Technique","authors":"Chunkai Zhang, Pengfei Jia","doi":"10.1109/ICMB.2014.45","DOIUrl":"https://doi.org/10.1109/ICMB.2014.45","url":null,"abstract":"Classification with imbalanced data-sets has become one of the most popular issues in machine learning, since it prevails in various applications. For binary-class problem, the amount of instances from the majority class is significant larger than that from the minority class. Consequently, traditional classifiers achieve a better performance over the majority class, while unsatisfactory predictive accuracy over the minority class. The emergence of ensemble learning provides a possible solution of solving this concern. And there are many recent researches indicate that the combination of Boosting and/or Bagging with pre-processing techniques is an effective way to enhance the classification performance of imbalanced data-sets. Centered on binary-class imbalanced problem, to overcome the drawbacks of state-of-the-art approaches, this paper introduces a novel technique (DBBoost) based on the combination of AdaBoost with an adaptive sampling approach. Through supporting by statistical analysis, experiments show that DBBoost outperforms the state-of-the-art methods based on ensemble.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130079311","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}
Radial arterial pulse is an important physiological signal that has been applied in Traditional Chinese Medicine (TCM) for thousands of years. From ancient times, pulse has been recognized as an empirical science and plays a decisive influence on the TCM diagnosis. However it's objective and lack visible database, which blocks the development of TCM. In Recent years, many pulse systems based on various kinds of sensors have been introduced to collect the computerized pulse waveforms. Meanwhile, pulse diagnosis using statistical learning theory is attracting more and more attention. This paper mainly presents the pulse feature extraction algorithm for removing the redundant and irrelevant information. Though many researches on pulse feature have been published, most of them emphasize on a certain aspect and hardly utilize the experience in TCM. We propose an integrated framework of pulse features and introduce the corresponding extraction algorithms. The experiments show that the features are extracted accurately and they performance well in disease diagnosis.
{"title":"Feature Extraction of Radial Arterial Pulse","authors":"Dimin Wang, David Zhang, J. Chan","doi":"10.1109/ICMB.2014.15","DOIUrl":"https://doi.org/10.1109/ICMB.2014.15","url":null,"abstract":"Radial arterial pulse is an important physiological signal that has been applied in Traditional Chinese Medicine (TCM) for thousands of years. From ancient times, pulse has been recognized as an empirical science and plays a decisive influence on the TCM diagnosis. However it's objective and lack visible database, which blocks the development of TCM. In Recent years, many pulse systems based on various kinds of sensors have been introduced to collect the computerized pulse waveforms. Meanwhile, pulse diagnosis using statistical learning theory is attracting more and more attention. This paper mainly presents the pulse feature extraction algorithm for removing the redundant and irrelevant information. Though many researches on pulse feature have been published, most of them emphasize on a certain aspect and hardly utilize the experience in TCM. We propose an integrated framework of pulse features and introduce the corresponding extraction algorithms. The experiments show that the features are extracted accurately and they performance well in disease diagnosis.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133367317","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}
Dongmei Zheng, Wenai Song, Zhendong Dai, Hongmo Wang
This paper report a new system developed for objectifying study of Color inspections of traditional Chinese medicine (CITCM), which is based on the digital image technologies. In this scheme, the entire system includes two parts, which are the hardware and the software. The hardware is an image acquisition device in a standard lighting conditions, which mainly includes a xenon lamp with a Color Temperature of 5500K to act as light source, an integrating sphere which is used for diffusing light and a high resolution CCD camera. The software is used for digital image processing, the procedure is divided into three steps. Firstly the skin/ non-skin classification is performed by utilizing the threshold in chrominance channels of the RGB color space. Secondly, the facial features are localized by employing the image segmentation and coordinates sorting. Finally, the facial special region (SI) corresponding to five internal organs are achieved by utilizing masks designed to take advantage of morphology. Subsequently, the chromaticity is calculated. The system is carried out by taking 100 samples. Experimental results demonstrate that the proposed scheme exhibits better performance for objectifying research of CITCM.
{"title":"The Objectifying System Using for Color Inspection of Traditional Chinese Medicine Based on the Digital Image Technology","authors":"Dongmei Zheng, Wenai Song, Zhendong Dai, Hongmo Wang","doi":"10.1109/ICMB.2014.11","DOIUrl":"https://doi.org/10.1109/ICMB.2014.11","url":null,"abstract":"This paper report a new system developed for objectifying study of Color inspections of traditional Chinese medicine (CITCM), which is based on the digital image technologies. In this scheme, the entire system includes two parts, which are the hardware and the software. The hardware is an image acquisition device in a standard lighting conditions, which mainly includes a xenon lamp with a Color Temperature of 5500K to act as light source, an integrating sphere which is used for diffusing light and a high resolution CCD camera. The software is used for digital image processing, the procedure is divided into three steps. Firstly the skin/ non-skin classification is performed by utilizing the threshold in chrominance channels of the RGB color space. Secondly, the facial features are localized by employing the image segmentation and coordinates sorting. Finally, the facial special region (SI) corresponding to five internal organs are achieved by utilizing masks designed to take advantage of morphology. Subsequently, the chromaticity is calculated. The system is carried out by taking 100 samples. Experimental results demonstrate that the proposed scheme exhibits better performance for objectifying research of CITCM.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122111333","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}
Breath analysis systems contain arrays of correlated chemical sensors. For such systems, sensor selection is needed. From the process of sensor selection, some insight behind the performance of different sensor arrays can be gotten. Thus, we can know more about the sensors, which could help us with the selection work in turn. In this paper, a breath analysis system for diabetes diagnosis with 16 sensors is described. Based on this system, several methods are proposed to evaluate the importance, unique discriminant information and redundancy of each sensor. They are based on the results of exhaustive sensor selection. These methods are made convenient to observe and draw intuitive conclusions. They are applied to the breath analysis system and some useful discoveries about the sensors in the system are made accordingly.
{"title":"Sensor Evaluation in a Breath Analysis System","authors":"Ke Yan, David Zhang","doi":"10.1109/ICMB.2014.14","DOIUrl":"https://doi.org/10.1109/ICMB.2014.14","url":null,"abstract":"Breath analysis systems contain arrays of correlated chemical sensors. For such systems, sensor selection is needed. From the process of sensor selection, some insight behind the performance of different sensor arrays can be gotten. Thus, we can know more about the sensors, which could help us with the selection work in turn. In this paper, a breath analysis system for diabetes diagnosis with 16 sensors is described. Based on this system, several methods are proposed to evaluate the importance, unique discriminant information and redundancy of each sensor. They are based on the results of exhaustive sensor selection. These methods are made convenient to observe and draw intuitive conclusions. They are applied to the breath analysis system and some useful discoveries about the sensors in the system are made accordingly.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128978635","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}
Sublingual excrescence is an important diagnostic measurement in tongue diagnosis, which can assist diagnosing a variety of diseases and syndromes. This paper proposes the framework for automatic segmentation of sublingual excrescences in color sublingual images. Simply initialized Grab Cut based on the visual saliency is firstly applied to segment out the dorsum of tongue where sublingual excrescence located on. And then, a stepwise method for segmenting the sublingual excrescence is proposed. There into, we use the over-detection of the light-reflecting regions especially designed for the sublingual images with sublingual excrescences to eliminate the interference of regions with high brightness. Multi-threshold Otsu method is then applied to coarsely segment the image of the dorsum of tongue, and obtain the candidate excrescence regions. Finally, the fact that the protuberance of sublingual excrescence always produces shadows in its neighborhood help extract the final contour of sublingual excrescences.
{"title":"Automatic Segmentation of Sublingual Excrescences in Color Sublingual Images","authors":"Zifei Yan, Haolun Ding, Naimin Li","doi":"10.1109/ICMB.2014.22","DOIUrl":"https://doi.org/10.1109/ICMB.2014.22","url":null,"abstract":"Sublingual excrescence is an important diagnostic measurement in tongue diagnosis, which can assist diagnosing a variety of diseases and syndromes. This paper proposes the framework for automatic segmentation of sublingual excrescences in color sublingual images. Simply initialized Grab Cut based on the visual saliency is firstly applied to segment out the dorsum of tongue where sublingual excrescence located on. And then, a stepwise method for segmenting the sublingual excrescence is proposed. There into, we use the over-detection of the light-reflecting regions especially designed for the sublingual images with sublingual excrescences to eliminate the interference of regions with high brightness. Multi-threshold Otsu method is then applied to coarsely segment the image of the dorsum of tongue, and obtain the candidate excrescence regions. Finally, the fact that the protuberance of sublingual excrescence always produces shadows in its neighborhood help extract the final contour of sublingual excrescences.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132496515","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}
In recent years, palm print identification technology has been widely carried out and used in fields such as identity recognition. At the same time, some features of palm vividly reveal information about diseases and health condition of the human body. We can research the application of palm diagnosis in traditional Chinese medicine with the help of digital image processing technology. In the palm diagnosis, palm print features and color features of visceral reflex regions are very important pathological features. Specific palm prints and color change of different reflex regions indicate different diseases. We want to take advantage of digital image processing technology to process palm images, in order to locate and segment the visceral reflex regions and extract certain palm print and color information, helping herbalist doctors diagnose diseases with palm diagnosis theory. This dissertation mainly focuses on approaches and methods of palm image pre-processing, prepared for further research and achieved certain results. The main work we have done is summarized as follows: Research on image acquisition conditions, median filtering, image binaryzation, binary image optimization, palm extraction, palm edge extraction and corner detection.
{"title":"Image Processing Technology in the Palm Diagnosis in Traditional Chinese Medicine","authors":"M. Fang, Zhi Liu, Hongjun Wang","doi":"10.1109/ICMB.2014.24","DOIUrl":"https://doi.org/10.1109/ICMB.2014.24","url":null,"abstract":"In recent years, palm print identification technology has been widely carried out and used in fields such as identity recognition. At the same time, some features of palm vividly reveal information about diseases and health condition of the human body. We can research the application of palm diagnosis in traditional Chinese medicine with the help of digital image processing technology. In the palm diagnosis, palm print features and color features of visceral reflex regions are very important pathological features. Specific palm prints and color change of different reflex regions indicate different diseases. We want to take advantage of digital image processing technology to process palm images, in order to locate and segment the visceral reflex regions and extract certain palm print and color information, helping herbalist doctors diagnose diseases with palm diagnosis theory. This dissertation mainly focuses on approaches and methods of palm image pre-processing, prepared for further research and achieved certain results. The main work we have done is summarized as follows: Research on image acquisition conditions, median filtering, image binaryzation, binary image optimization, palm extraction, palm edge extraction and corner detection.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131748527","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}
Peng Wang, Shanpeng Hou, Hongzhi Zhang, W. Zuo, David Zhang
Pulse signal contains important information about health status and pulse diagnosis has been extensively applied in oriental medicine. In recent years more and more research interests have been given on computerized pulse diagnosis. Pulse feature extraction plays an important role in computerized pulse diagnosis. The most popular pulse feature extraction methods can be grouped into two categories, i.e. time domain feature extraction method and frequency domain feature extraction method. The pulse signal is a pseudo periodic signal while the common feature extraction methods usually assume it is a periodic signal and only a typical period or an averaged period was used in the feature extraction, while the difference between periods was less emphasized. In this paper we use complex network to transform the pulse signal from time domain to network domain and use the statistics parameters which describe the organization of the complex network as the features to characterize the difference between pulse periods. The experiment shows that the complex network features are useful in characterizing the relationship between different pulse periods the diagnosis performance on diabetes are similar with the multi scale sample entropy. By combining complex network features with sample entropy features, higher diagnosis performance can be further obtained.
{"title":"Wrist Pulse Diagnosis Using Complex Network","authors":"Peng Wang, Shanpeng Hou, Hongzhi Zhang, W. Zuo, David Zhang","doi":"10.1109/ICMB.2014.10","DOIUrl":"https://doi.org/10.1109/ICMB.2014.10","url":null,"abstract":"Pulse signal contains important information about health status and pulse diagnosis has been extensively applied in oriental medicine. In recent years more and more research interests have been given on computerized pulse diagnosis. Pulse feature extraction plays an important role in computerized pulse diagnosis. The most popular pulse feature extraction methods can be grouped into two categories, i.e. time domain feature extraction method and frequency domain feature extraction method. The pulse signal is a pseudo periodic signal while the common feature extraction methods usually assume it is a periodic signal and only a typical period or an averaged period was used in the feature extraction, while the difference between periods was less emphasized. In this paper we use complex network to transform the pulse signal from time domain to network domain and use the statistics parameters which describe the organization of the complex network as the features to characterize the difference between pulse periods. The experiment shows that the complex network features are useful in characterizing the relationship between different pulse periods the diagnosis performance on diabetes are similar with the multi scale sample entropy. By combining complex network features with sample entropy features, higher diagnosis performance can be further obtained.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126749861","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}
Correct detection of ventricular fibrillation (VF) is of great importance to real-time electrocardiogram (ECG) monitoring systems and automatic external defibrillator (AED). First, the paper gives a brief review of threshold crossing sample count algorithm (TCSC), and analyzes this algorithm's drawbacks. Then the authors present an improved algorithm combined TCSC with support vector machine (SVM), which is more accuracy than the TCSC algorithm. For assessment of the performance of the algorithm, the complete CU database and MIT-BIH database are used. The authors compare the new algorithm with other VF detection methods under the same conditions. The ROC curve is created and the AUC is also calculated. The results show that the proposed algorithm has a high Accuracy of 91.2%, Specificity of 96.8%, and the AUC is 92.5. The new algorithm is fast, accurate and reliable, showing strong potential to be applied in real-time ECG monitor system.
{"title":"Ventricular Fibrillation Detection by an Improved Time Domain Algorithm Combined with SVM","authors":"Zhongjie Hou, Yue Zhang","doi":"10.1109/ICMB.2014.39","DOIUrl":"https://doi.org/10.1109/ICMB.2014.39","url":null,"abstract":"Correct detection of ventricular fibrillation (VF) is of great importance to real-time electrocardiogram (ECG) monitoring systems and automatic external defibrillator (AED). First, the paper gives a brief review of threshold crossing sample count algorithm (TCSC), and analyzes this algorithm's drawbacks. Then the authors present an improved algorithm combined TCSC with support vector machine (SVM), which is more accuracy than the TCSC algorithm. For assessment of the performance of the algorithm, the complete CU database and MIT-BIH database are used. The authors compare the new algorithm with other VF detection methods under the same conditions. The ROC curve is created and the AUC is also calculated. The results show that the proposed algorithm has a high Accuracy of 91.2%, Specificity of 96.8%, and the AUC is 92.5. The new algorithm is fast, accurate and reliable, showing strong potential to be applied in real-time ECG monitor system.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126210348","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}
In this paper, we propose new features based on multiwavelet transform for classification of human emotions from electroencephalogram (EEG) signals. The EEG signal measures electrical activity of the brain, which contains lot of information related to emotional states. The sub-signals obtained by multiwavelet decomposition of EEG signals are plotted in a 3-D phase space diagram using phase space reconstruction (PSR). The mean and standard deviation of Euclidian distances are computed from 3-D phase space diagram. These features have been used as input features set for multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet and Morlet wavelet kernel functions for classification of emotions. The proposed features based on multiwavelet transform of EEG signals with Morlet wavelet kernel function of MC-LS-SVM have provided better classification accuracy for classification of emotions.
{"title":"Human Emotion Classification from EEG Signals Using Multiwavelet Transform","authors":"V. Bajaj, R. B. Pachori","doi":"10.1109/ICMB.2014.29","DOIUrl":"https://doi.org/10.1109/ICMB.2014.29","url":null,"abstract":"In this paper, we propose new features based on multiwavelet transform for classification of human emotions from electroencephalogram (EEG) signals. The EEG signal measures electrical activity of the brain, which contains lot of information related to emotional states. The sub-signals obtained by multiwavelet decomposition of EEG signals are plotted in a 3-D phase space diagram using phase space reconstruction (PSR). The mean and standard deviation of Euclidian distances are computed from 3-D phase space diagram. These features have been used as input features set for multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet and Morlet wavelet kernel functions for classification of emotions. The proposed features based on multiwavelet transform of EEG signals with Morlet wavelet kernel function of MC-LS-SVM have provided better classification accuracy for classification of emotions.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133949937","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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