基于心电信号的情感识别研究
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摘要
情感计算是一个高度综合化的新兴研究领域,目的是通过赋予计算机识别、理解和适应人类的情感能力来建立和谐的人机环境,并使计算机具有更高的、全面的智能。情感识别是情感计算的一个重要部分,它研究的内容包括语音信号、身体姿态、面部表情和生理信号等方面。由于生理信号具有真实的、可靠和难以伪造的特点,用生理信号进行情感识别成为其中的一个热点方向。美国麻省理工学院媒体实验室情感计算研究小组首先用生理信号作为研究对象进行情感识别,且取得一定的硕果,这也为生理信号情感识别的研究提供了可靠的支撑。
心电、心率信号蕴含丰富的情感特征,可以明显的反映出人类在不同情感状态下的变化。因此文中利用心电、心率信号来进行情感识别,并验证了在高兴、惊奇、厌恶、悲伤、愤怒和恐惧这六种情感状态下情感用户模型建立的可行性。其过程有四个主要步骤：1、情感数据采集；2、情感信号的特征提取；3、情感信号的特征子集选择；4、分类器的设计。
文中设计了周密的方案保证用于研究的心电、心率信号包含着某种特定情感：选取有效的电影片段激发被试情感,且让被试记录当时看完后片段的感受,也通过隐藏的摄像头观察被试观看电影片段的情况,并在Superlab采集软件上做上相应的标记。采用美国Biopac公司提供的多导生理记录仪MP150,为了有效地激发被试情感,文中只对300位西南大学在校大一学生进行信号的采集,建立了情感心电、心率信号数据库。这个方案中对素材电影片段的有效选取、被试的要求,保证了采集的信号包含有某种特定情感,文中激发被试的情感状态有：高兴、惊奇、厌恶、悲伤、愤怒和恐惧六种情感；
心电信号的特征提取关键在于P-QRS-T波位置检测,然而,采集的心电信号容易存在基线漂移等噪声干扰而难以准确进行P-QRS-T波的检测。小波变换具有良好的时、频局部化,在时频域都具有表征信号局部特征的能力,且在图像分析、去噪和压缩等方面得到广泛的应用。文中采用连续小波变换将原始心电信号进行5层分解,根据R波的频率范围,使用第一层小波系数准确检测到R波位置,然后检测Q,S,P和T波位置,且能自动检测出信噪比过小的信号段,对该信号段不进行特征提取以保证研究结果的正确性,然后通过几何平均法去除了心电的高频噪声,对心率信号的高频噪声也进行了平滑处理。
由于提取到的大量冗余和无效的心电、心率的特征会影响情感识别的效果,需要使用特征选择筛选出有效的特征用于情感分类。特征选择问题是一个组合优化问题,其计算复杂度随着维数的加大成指数倍的增加,它需要使用有效的搜索算法来解决。离散二进制粒子群算法(BPSO)是一种智能的全局优化算法,它因具有计算速度快、算法参数简单和易于实现而被广泛应用,目前已应用于组合优化问题、函数优化、信号处理、神经网络训练、数据挖掘和数据聚类等应用领域；序列后向选择算法(SBS)也是一种有效的搜索算法。因此,论文研究将BPSO、SBS算法应用于心电、心率的特征选择问题上,以提高情感状态的识别率。针对BPSO易陷入局部导致早熟收敛现象,文中提出两种改进策略：一种改进算法是基于邻域搜索的方法(IBPSO),让粒子群有更多机会的跳出局部最优,向全局最优方向前进；另一种是将遗传操作(交叉和变异)引入到BPSO中,用来增加种群的多样性。同时,由于fisher分类器具有高效、准确率高的特点,文中采用fisher分类器,并且将其与BPSO、SBS算法结合共同解决特征选择问题。
实验结果表明,心率特征用于情感识别优于心电特征,特别是在恐惧、惊奇情绪下,在两种改进的BPSO算法中,心率最佳特征组合的平均验证识别率都高于心电特征10个百分点左右。对两种改进的BPSO来说,对于完全相同的训练集、测试集和验证集却得到了不同的最佳特征组合,导致得到的验证识别率有较大的区别。一般来说,IBPSO算法得到的平均验证识别率都高于或和GBPSO算法相差不大(除了高兴情绪状态),而且IBPSO算法得到的平均特征维数也明显少于GBPSO算法,这表明IBPSO算法得到的最佳特征组合更适用于情感用户模型的建立。而就SBS算法而言,虽然选择的最佳特征组合的测试、验证结果都差于两种改进的BPSO算法结果,但是在进行高兴情感状态的识别中,获得了很少的特征,却达到了与BPSO算法一样的效果,而且此时选中的特征很大一部分在BPSO算法中也被选中,证明这个特征组合适合于高兴情感用户模型的建立。
Affective Computing is a highly integrated research area that has rapidly evolved recently, aiming at giving the computer more emotional abilities in identifying, understanding and adapting for building a harmonious man-machine environment and further possessing a higher and comprehensive intelligence. As an important part of affective computing, emotion recognition mainly contains diverse studies including voice signals, body posture, facial expressions and physiological signals and so on. Owing to the characteristics of truth, credibility, and difficulties to counterfeit, it is becoming one of the hottest research fields based on physiological signals. Affective Computing Research Group from MIT Media Lab first has a research on emotion recognition from physiological signals, and had already reached some good achievement. Therefore, it also provides a reliable support for emotion recognition from physiological signals.
As ECG and heart rate of abundant emotional features, they can clearly reflect human's emotional states under the different changes. Therefore, this paper has a research for emotion recognition and validation of user model from the extracted data on ECG and heart rate (six emotion states: happy, anger, surprise, fear, sadness and disgust). Main contents are as follows: data acquisition, feature extraction, feature selection and classifier design.
This paper has planed elaborate rules for guaranteeing ECG and heart rate containing some special characteristics of emotions. In the experiment, the program is just designed to ensure that our chose movie clips are enough effective to elicit subject's emotion, which also need subjects to record their own feelings after watching over. Besides, operator will also carefully observe everything from subjects by the camera hidden in somewhere, and momentarily give the according marks on capture software Superlab. Through U.S. Multi-Polygraph MP150 from Biopac, this paper acquires emotion data from 300 freshman from Southwest University and then sets up emotion database on ECG and heart rate. For the effectively rigid selection on film clips and subjects, it meets ideal requirement at some extent that the collected signals must contain certain emotion which includes happy, surprise, disgust, sadness, anger and fear.
Feature extraction of emotion signals on ECG and heart rate is mainly depending on position detection of P-QRS-T wave, but there are also some difficulties in accurately detecting P-QRS-T wave with noise interference such as ECG baseline drift, etc. Wavelet transform has a good time and frequency localization, and could reflect local characteristics of signals in the time-frequency domain, so it is widely be used in image analysis, denoising and compressing, etc. In this paper, continuous wavelet transform is suitably adopted to ECG signal for 5-layer decomposition. According to frequency range of R-wave based on the first level of wavelet coefficients, R-wave position can be exactly detected, and then Q, S, P and T-wave locations would be easily found. Through automatically filtering signal intervals of lower signal-noise ratio, it can finally remove the high-frequency noise and smoothen effective signal by the geometric mean..
As effect of emotion recognition will be disturbed by the extracted features of redundant and ineffective characteristics in ECG and heart rate, then it is necessary to select effective feature for classifying emotion. Feature selection is a combinatorial optimization problem and its computational complexity would be exponentially enhanced with the increasingly amount of dimension, so it requires effective search algorithms to solve this troubles. Discrete binary particle swarm optimization (BPSO) is an intelligent global optimization algorithm, which has been widely applied at present to combinatorial optimization problems, function optimization, signal processing, neural network training, data mining and data clustering and other applications with more advantages in speed computing, simple algorithm parameters and easily implementing. Besides, sequence backward selection algorithm (SBS) is also an effective search algorithm. Therefore, this paper will satisfactorily introduce BPSO, SBS to feature selection on ECG and heart rate to improve recognition rate of emotion states. For BPSO can be easily lead to local search bringing into premature convergence, this paper proposes two kinds of improvement strategies: an improved algorithm is based on neighborhood search method (IBPSO), and it can conveniently supply particle swarm more opportunities to jump out of the local optimum to the global optimum; the other perfectly introduce the genetic operations (crossover and mutation) into the BPSO, and it mainly increase the diversity of evolution population. Moreover, as fisher classifier of high efficiency and accuracy, it is just imported to feature selection with BPSO and SBS.
Ultimately, the experimental results show that the features of heart rate are superior to ECG for emotion recognition, especially in fear, surprise. In both improved BPSO algorithm, the average recognition rate attained by the best feature combination in heart rate is nearly respectively 10% higher than in ECG. Furthermore, for the same training, test and validation set, the best combinations are exactly different. So there are greater differences in recognition rate for validation among them. In general, the average recognition rate for validation by IBPSO is higher or as much as GBPSO (in addition to happy), and the dimension of the selected feature subset is obviously less accordingly, which shows that the best feature combination is more suitable for affective user modeling. And for SBS, although the best feature combinations for test and validation are worse than the two improved BPSO above, it gladly gained a few features that could achieve the same effect as BPSO during emotion recognition for happy. And surprisingly, some selected features also mostly existing in BPSO. So it does show that the feature combination is suitable for affective user modeling in happy
心电、心率信号蕴含丰富的情感特征,可以明显的反映出人类在不同情感状态下的变化。因此文中利用心电、心率信号来进行情感识别,并验证了在高兴、惊奇、厌恶、悲伤、愤怒和恐惧这六种情感状态下情感用户模型建立的可行性。其过程有四个主要步骤：1、情感数据采集；2、情感信号的特征提取；3、情感信号的特征子集选择；4、分类器的设计。
文中设计了周密的方案保证用于研究的心电、心率信号包含着某种特定情感：选取有效的电影片段激发被试情感,且让被试记录当时看完后片段的感受,也通过隐藏的摄像头观察被试观看电影片段的情况,并在Superlab采集软件上做上相应的标记。采用美国Biopac公司提供的多导生理记录仪MP150,为了有效地激发被试情感,文中只对300位西南大学在校大一学生进行信号的采集,建立了情感心电、心率信号数据库。这个方案中对素材电影片段的有效选取、被试的要求,保证了采集的信号包含有某种特定情感,文中激发被试的情感状态有：高兴、惊奇、厌恶、悲伤、愤怒和恐惧六种情感；
心电信号的特征提取关键在于P-QRS-T波位置检测,然而,采集的心电信号容易存在基线漂移等噪声干扰而难以准确进行P-QRS-T波的检测。小波变换具有良好的时、频局部化,在时频域都具有表征信号局部特征的能力,且在图像分析、去噪和压缩等方面得到广泛的应用。文中采用连续小波变换将原始心电信号进行5层分解,根据R波的频率范围,使用第一层小波系数准确检测到R波位置,然后检测Q,S,P和T波位置,且能自动检测出信噪比过小的信号段,对该信号段不进行特征提取以保证研究结果的正确性,然后通过几何平均法去除了心电的高频噪声,对心率信号的高频噪声也进行了平滑处理。
由于提取到的大量冗余和无效的心电、心率的特征会影响情感识别的效果,需要使用特征选择筛选出有效的特征用于情感分类。特征选择问题是一个组合优化问题,其计算复杂度随着维数的加大成指数倍的增加,它需要使用有效的搜索算法来解决。离散二进制粒子群算法(BPSO)是一种智能的全局优化算法,它因具有计算速度快、算法参数简单和易于实现而被广泛应用,目前已应用于组合优化问题、函数优化、信号处理、神经网络训练、数据挖掘和数据聚类等应用领域；序列后向选择算法(SBS)也是一种有效的搜索算法。因此,论文研究将BPSO、SBS算法应用于心电、心率的特征选择问题上,以提高情感状态的识别率。针对BPSO易陷入局部导致早熟收敛现象,文中提出两种改进策略：一种改进算法是基于邻域搜索的方法(IBPSO),让粒子群有更多机会的跳出局部最优,向全局最优方向前进；另一种是将遗传操作(交叉和变异)引入到BPSO中,用来增加种群的多样性。同时,由于fisher分类器具有高效、准确率高的特点,文中采用fisher分类器,并且将其与BPSO、SBS算法结合共同解决特征选择问题。
实验结果表明,心率特征用于情感识别优于心电特征,特别是在恐惧、惊奇情绪下,在两种改进的BPSO算法中,心率最佳特征组合的平均验证识别率都高于心电特征10个百分点左右。对两种改进的BPSO来说,对于完全相同的训练集、测试集和验证集却得到了不同的最佳特征组合,导致得到的验证识别率有较大的区别。一般来说,IBPSO算法得到的平均验证识别率都高于或和GBPSO算法相差不大(除了高兴情绪状态),而且IBPSO算法得到的平均特征维数也明显少于GBPSO算法,这表明IBPSO算法得到的最佳特征组合更适用于情感用户模型的建立。而就SBS算法而言,虽然选择的最佳特征组合的测试、验证结果都差于两种改进的BPSO算法结果,但是在进行高兴情感状态的识别中,获得了很少的特征,却达到了与BPSO算法一样的效果,而且此时选中的特征很大一部分在BPSO算法中也被选中,证明这个特征组合适合于高兴情感用户模型的建立。
Affective Computing is a highly integrated research area that has rapidly evolved recently, aiming at giving the computer more emotional abilities in identifying, understanding and adapting for building a harmonious man-machine environment and further possessing a higher and comprehensive intelligence. As an important part of affective computing, emotion recognition mainly contains diverse studies including voice signals, body posture, facial expressions and physiological signals and so on. Owing to the characteristics of truth, credibility, and difficulties to counterfeit, it is becoming one of the hottest research fields based on physiological signals. Affective Computing Research Group from MIT Media Lab first has a research on emotion recognition from physiological signals, and had already reached some good achievement. Therefore, it also provides a reliable support for emotion recognition from physiological signals.
As ECG and heart rate of abundant emotional features, they can clearly reflect human's emotional states under the different changes. Therefore, this paper has a research for emotion recognition and validation of user model from the extracted data on ECG and heart rate (six emotion states: happy, anger, surprise, fear, sadness and disgust). Main contents are as follows: data acquisition, feature extraction, feature selection and classifier design.
This paper has planed elaborate rules for guaranteeing ECG and heart rate containing some special characteristics of emotions. In the experiment, the program is just designed to ensure that our chose movie clips are enough effective to elicit subject's emotion, which also need subjects to record their own feelings after watching over. Besides, operator will also carefully observe everything from subjects by the camera hidden in somewhere, and momentarily give the according marks on capture software Superlab. Through U.S. Multi-Polygraph MP150 from Biopac, this paper acquires emotion data from 300 freshman from Southwest University and then sets up emotion database on ECG and heart rate. For the effectively rigid selection on film clips and subjects, it meets ideal requirement at some extent that the collected signals must contain certain emotion which includes happy, surprise, disgust, sadness, anger and fear.
Feature extraction of emotion signals on ECG and heart rate is mainly depending on position detection of P-QRS-T wave, but there are also some difficulties in accurately detecting P-QRS-T wave with noise interference such as ECG baseline drift, etc. Wavelet transform has a good time and frequency localization, and could reflect local characteristics of signals in the time-frequency domain, so it is widely be used in image analysis, denoising and compressing, etc. In this paper, continuous wavelet transform is suitably adopted to ECG signal for 5-layer decomposition. According to frequency range of R-wave based on the first level of wavelet coefficients, R-wave position can be exactly detected, and then Q, S, P and T-wave locations would be easily found. Through automatically filtering signal intervals of lower signal-noise ratio, it can finally remove the high-frequency noise and smoothen effective signal by the geometric mean..
As effect of emotion recognition will be disturbed by the extracted features of redundant and ineffective characteristics in ECG and heart rate, then it is necessary to select effective feature for classifying emotion. Feature selection is a combinatorial optimization problem and its computational complexity would be exponentially enhanced with the increasingly amount of dimension, so it requires effective search algorithms to solve this troubles. Discrete binary particle swarm optimization (BPSO) is an intelligent global optimization algorithm, which has been widely applied at present to combinatorial optimization problems, function optimization, signal processing, neural network training, data mining and data clustering and other applications with more advantages in speed computing, simple algorithm parameters and easily implementing. Besides, sequence backward selection algorithm (SBS) is also an effective search algorithm. Therefore, this paper will satisfactorily introduce BPSO, SBS to feature selection on ECG and heart rate to improve recognition rate of emotion states. For BPSO can be easily lead to local search bringing into premature convergence, this paper proposes two kinds of improvement strategies: an improved algorithm is based on neighborhood search method (IBPSO), and it can conveniently supply particle swarm more opportunities to jump out of the local optimum to the global optimum; the other perfectly introduce the genetic operations (crossover and mutation) into the BPSO, and it mainly increase the diversity of evolution population. Moreover, as fisher classifier of high efficiency and accuracy, it is just imported to feature selection with BPSO and SBS.
Ultimately, the experimental results show that the features of heart rate are superior to ECG for emotion recognition, especially in fear, surprise. In both improved BPSO algorithm, the average recognition rate attained by the best feature combination in heart rate is nearly respectively 10% higher than in ECG. Furthermore, for the same training, test and validation set, the best combinations are exactly different. So there are greater differences in recognition rate for validation among them. In general, the average recognition rate for validation by IBPSO is higher or as much as GBPSO (in addition to happy), and the dimension of the selected feature subset is obviously less accordingly, which shows that the best feature combination is more suitable for affective user modeling. And for SBS, although the best feature combinations for test and validation are worse than the two improved BPSO above, it gladly gained a few features that could achieve the same effect as BPSO during emotion recognition for happy. And surprisingly, some selected features also mostly existing in BPSO. So it does show that the feature combination is suitable for affective user modeling in happy
引文
[1]. Minsky M. The Society of Mind, Simon & Schuster, New York, NY,1985.
[2]. Picard, R. W. Affective Computing, MIT Press, London, England,1997.
[3].胡包钢,谭铁牛,王珏.情感计算-计算机科技发展的新课题,科学时报,2000，03.
[4]. Picard R. W., Healey J. Affective Wearable. In Proceedings of the First International Symposium on Wearable Computers. Cambridge, MA, Oct 1997,1:231～240.
[5].阎克乐,张文彩,张月娟,封文波,袁立壮,王兰爽,唐一源.心率变异性在心身疾病和情绪障碍研究中的应用.心理科学进展.2006，14(2)：261-265.
[6]. Ekman P. An Argument for Basic Emotion. Cognition and Emotion.1992,6:169～200.
[7]. Haag A., Goronzy S., Schaich P., Williams J. Emotion Recognition Using Bio-Sensors:First Step Towards an Automatic System. Affective Dialogue Systems. Kloster Irsee, Germany.2004, 3068:36～48.
[8]. Mi-hee Lee, Gyunghye Yang, Hyoung-Ki Lee and Seokwon Bang. Development Stress monitoring System based on Personal Digital Assistant (PDA). Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco. CA, USA,2004,2364～2367.
[9]. Jing Zhai and Armando Barreto. Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables. Proceedings of the 28th IEEE EMBS Annual International Conference. New York, USA.2006,1355～1358.
[10].F Nasoz, C L Lisetti, K Alvarez, N Finelstein (2003), Emotional Recognition from Physiological Signals for User Modeling of Affect, In Proceedings of the 3rd Workshop on Affective and Attitude User Modeling, Pittsburgh, PA, USA, June 2003.
[11]. Wagner J, Kim J, Andre E. From Physiological Signals to Emotions:Implementing and Comparing Selected Methods for Feature Extraction and Classification. In IEEE International Conference on Multimedia & Expo (ICME 2005).2005,940-943.
[12]. Horlings. R. Emotion recognition using brain activity. [Ph.D. Dissertation].Delft University of Technology,2008.
[13]. Lan Li, Ji-hua Chen. Emotion Recognition Using Physiological Signals. Advances in Artificial Reality and Tele-Existence.,4282/2006,437-446
[14].陈继华,李岚,钱坤喜.基于多生理信号的情绪初步识别.生物医学工程研究2006(03).
[15]. Kim, J. and Andre, E. (2008). Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis Machine Intelligence,30(12):2067-2083.
[16].黄敏,杨瑰玉,赵智贤.临床心电图全解：病例分析与学习精要.科学出版社,2004.
[17]. Aaron Sloman and Monica Croucher. Why Robots Will Have Emotion. Proceedings IJCAI. Vancouver,1981.
[18]. Olivier Villon, Christine Lisetti, Toward Recognizing Individual's Subjective Emotion from Physiological Signals in Practical Application, Proceedings of 20th IEEE International Symposium on Computer-Based Medical Systems (CBMS2007), IEEE Computer Society Press, Maribor, Slovenia,2007,357-362
[19].程波,刘光远.基于小波变换与神经网络的表面肌电信号的情感识别.计算机应用2008,(28)2.
[20]. Sinha, R., Lovallo, W.R., and Parsons O.A. (1992) Cardiovascular Differentiation of Emotions. Psychosomatic Medicine,54(4):422-435.
[21]. Mi-hee Lee, Gyunghye Yang， Hyoung-Ki Lee and Seokwon Bang. Development Stress monitoring System based on Personal Digital Assistant (PDA). Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco. CA, USA,2004,2364-2367.
[22]. Kim K.H., Bang S.W., Kim S.R.. Emotion Recognition System using Short-term Monitoring of Physiological Signals. Medical Biology Engine Computer,2004,42:419-427.
[23]. http://emotion-research.net/.
[24].边肇祺,张学工.模式识别.第二版.北京：清华大学出版社,2005,198-205.
[25].谢波.普通话语音情感识别关键技术研究：[博士学位论文].浙江：浙江大学,2006.64-66.
[26]. Lain R. Murray, John L. Arnott. Towards the Simulation of Emotion in Synthetic Speech:A Review of the Literature on Human Vocal Emotion. Journal of the Acoustical Society of America. 1993,93(2):1097～1108.
[27].Leon E., Clarke G., Sepulveda F., Callaghan V. Optimised Attribute Selection for Emotion Classification Using Physiological Signals. In:Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Francisco, California, 2004.
[28]. Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of 1995 IEEE International Conference on Neural Networks.1995,4:1942～1948.
[29]. Kennedy J., Eberhart R. C. A discrete binary version of the particle swarm algorithm. Proc. of the 1997 Conf. on Systems, Man, and Cybernetics. Piscataway, IEEE Press.1997,4104～4108.
[30].侯建成，李国丽.基于LabVIEW的小波变换在心电信号分析中的应用研究：[硕士学位论文].合肥：合肥工业大学,2008.10-20.
[31].余祖龙,周旭欣,艾信友,王玉,周波.基于离散平稳小波变换的心电信号去噪方法.科技创新导报2008(03).
[32].田媛.现代心电图诊断技术与心电图图谱分析实用手册.当代中国音像出版社.2004.
[33].葛哲学,沙威.小波分析理论与MATLAB2007实现.第二版.北京：电子工业出版社,2007,33-39.
[34].Mahmoodabadi S.Z., Ahmadian A., Abolhasani M.D., Eslam M., Bidgoli J. H. ECG Feature Extraction Based on Multiresolution Wavelet Transform. Proceedings of the 2005 IEEE Engineering Medicine and Biology 27th Aunnual Conference Shanghai, China,Septemberl-4,2005.
[35].Lin He, Wensheng Hou, Xiaolin Zhen, Chenglin Peng. Recognition of ECG Patterns Using Artificial Neural Network.Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications,2006.
[36].Rajesh G., Ghatol A.A.Performance Analysis of Feature Extraction Schemes for Artificial Neural Network Based ECG Classification.International Conference on Computational Intelligence and Multimedia Applications,2007.
[37].梁仲钢,严洪.心率变异性的分析方法及其在航天医学领域的应用.航天医学与医学工程.2003(12)16.
[38].高尚,杨静宇.群智能算法及其应用.北京：中国水利水电出版社,2006，6-16.
[39]. S. Ktata, K. Ouni, and N. Ellouze, "ECG signal maxima detection using wavelet transform," Proceedings of IEEE International Symposium on Industrial Electronics, IEEE Industrial Electronic Society,2006, pp.700-703.
[40].Xie Xiaofeng, Zhang Wenjun, Yang Zhilian. Incorporating Knowledge in Genetic Glgorithms for Device Synthesis.6th Int Confon Solid-state and Integrated Circuit Technology.Shanghai.2001, 1143～1146.
[41]. Shi Y., Eberhart R C.A modified swarm optimizer.IEEE International Conference of Evolutionary Computation. Anchorage, Alaska:IEEE Press,1998.
[42]. Shi Y. and Eberhart R. C. Parameter selection in particle swarm optimization.Evolutionary Programming Ⅶ:Proceedings of the Seventh Annual Conference on Evolutionary Programming.New York,1998,591～600.
[43].M.Clerc, J.Kennedy. The Particle Swarm Explosion, Stability and Convergence in a Multidimensional Complex Space. IEEE Trans. On Evolutionary Computation.2002,6(1):58-63.
[44].J.Salerno. Using the Particle Swarm Optimization Technique to Train a Recurrent Neural Model.Proceedings of Ninth IEEE International Conference on Tools with Artificial Intelligence.1997, (11):45～49.
[45]. Cheng D. F., Liu, G. Y., Qiu Y. H.. Applications of particle swarm optimization and K-Nearest neighbors to emotion recognition from physiological signals.Proceeding of 2008 International Conference on Computational Intelligence and Security, Suzhou, China,2008,52-56.
[46].刘君君,王傅亮.赤潮生物图像分类识别技术研究：[硕士学位论文].浙江：厦门大学,2009,06.
[47].杨淑莹.图像模式识别.北京：清华大学出版社,2006，95-101.
[48].R.C.Eberhart, Hu Xiaohui. Human Tremor Analysis using Particle Swarm Optimization. Proc. of the Congress on Evolutionary Computation [C]. Washington.1999,1927～1930.
[49].冯林,颜世鹏,孙焘.邻域搜索的粒子群优化算法及其性能分析.计算机工程与科学,28(2006)12,72-73.
[50].Xu Ya, Liu Guangyuan, Hao Min, Wen Wanhui, Huang Xiting. Analysis of Affective ECG Signals toward Emotion Recognition.Journal of Electronics (China).2010,27(01), pp.8-14.
[51].李聪.基于小生境正交遗传算法的地区电网实时无功优化.广东输电与变电技术,11(2009)2,8-12.
[52].乔立岩,彭喜元,彭宇.基于微粒群算法和支持向量机的特征子集选择方.电子学报.2006,34：496～498.
[2]. Picard, R. W. Affective Computing, MIT Press, London, England,1997.
[3].胡包钢,谭铁牛,王珏.情感计算-计算机科技发展的新课题,科学时报,2000，03.
[4]. Picard R. W., Healey J. Affective Wearable. In Proceedings of the First International Symposium on Wearable Computers. Cambridge, MA, Oct 1997,1:231～240.
[5].阎克乐,张文彩,张月娟,封文波,袁立壮,王兰爽,唐一源.心率变异性在心身疾病和情绪障碍研究中的应用.心理科学进展.2006，14(2)：261-265.
[6]. Ekman P. An Argument for Basic Emotion. Cognition and Emotion.1992,6:169～200.
[7]. Haag A., Goronzy S., Schaich P., Williams J. Emotion Recognition Using Bio-Sensors:First Step Towards an Automatic System. Affective Dialogue Systems. Kloster Irsee, Germany.2004, 3068:36～48.
[8]. Mi-hee Lee, Gyunghye Yang, Hyoung-Ki Lee and Seokwon Bang. Development Stress monitoring System based on Personal Digital Assistant (PDA). Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco. CA, USA,2004,2364～2367.
[9]. Jing Zhai and Armando Barreto. Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables. Proceedings of the 28th IEEE EMBS Annual International Conference. New York, USA.2006,1355～1358.
[10].F Nasoz, C L Lisetti, K Alvarez, N Finelstein (2003), Emotional Recognition from Physiological Signals for User Modeling of Affect, In Proceedings of the 3rd Workshop on Affective and Attitude User Modeling, Pittsburgh, PA, USA, June 2003.
[11]. Wagner J, Kim J, Andre E. From Physiological Signals to Emotions:Implementing and Comparing Selected Methods for Feature Extraction and Classification. In IEEE International Conference on Multimedia & Expo (ICME 2005).2005,940-943.
[12]. Horlings. R. Emotion recognition using brain activity. [Ph.D. Dissertation].Delft University of Technology,2008.
[13]. Lan Li, Ji-hua Chen. Emotion Recognition Using Physiological Signals. Advances in Artificial Reality and Tele-Existence.,4282/2006,437-446
[14].陈继华,李岚,钱坤喜.基于多生理信号的情绪初步识别.生物医学工程研究2006(03).
[15]. Kim, J. and Andre, E. (2008). Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis Machine Intelligence,30(12):2067-2083.
[16].黄敏,杨瑰玉,赵智贤.临床心电图全解：病例分析与学习精要.科学出版社,2004.
[17]. Aaron Sloman and Monica Croucher. Why Robots Will Have Emotion. Proceedings IJCAI. Vancouver,1981.
[18]. Olivier Villon, Christine Lisetti, Toward Recognizing Individual's Subjective Emotion from Physiological Signals in Practical Application, Proceedings of 20th IEEE International Symposium on Computer-Based Medical Systems (CBMS2007), IEEE Computer Society Press, Maribor, Slovenia,2007,357-362
[19].程波,刘光远.基于小波变换与神经网络的表面肌电信号的情感识别.计算机应用2008,(28)2.
[20]. Sinha, R., Lovallo, W.R., and Parsons O.A. (1992) Cardiovascular Differentiation of Emotions. Psychosomatic Medicine,54(4):422-435.
[21]. Mi-hee Lee, Gyunghye Yang， Hyoung-Ki Lee and Seokwon Bang. Development Stress monitoring System based on Personal Digital Assistant (PDA). Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco. CA, USA,2004,2364-2367.
[22]. Kim K.H., Bang S.W., Kim S.R.. Emotion Recognition System using Short-term Monitoring of Physiological Signals. Medical Biology Engine Computer,2004,42:419-427.
[23]. http://emotion-research.net/.
[24].边肇祺,张学工.模式识别.第二版.北京：清华大学出版社,2005,198-205.
[25].谢波.普通话语音情感识别关键技术研究：[博士学位论文].浙江：浙江大学,2006.64-66.
[26]. Lain R. Murray, John L. Arnott. Towards the Simulation of Emotion in Synthetic Speech:A Review of the Literature on Human Vocal Emotion. Journal of the Acoustical Society of America. 1993,93(2):1097～1108.
[27].Leon E., Clarke G., Sepulveda F., Callaghan V. Optimised Attribute Selection for Emotion Classification Using Physiological Signals. In:Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Francisco, California, 2004.
[28]. Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of 1995 IEEE International Conference on Neural Networks.1995,4:1942～1948.
[29]. Kennedy J., Eberhart R. C. A discrete binary version of the particle swarm algorithm. Proc. of the 1997 Conf. on Systems, Man, and Cybernetics. Piscataway, IEEE Press.1997,4104～4108.
[30].侯建成，李国丽.基于LabVIEW的小波变换在心电信号分析中的应用研究：[硕士学位论文].合肥：合肥工业大学,2008.10-20.
[31].余祖龙,周旭欣,艾信友,王玉,周波.基于离散平稳小波变换的心电信号去噪方法.科技创新导报2008(03).
[32].田媛.现代心电图诊断技术与心电图图谱分析实用手册.当代中国音像出版社.2004.
[33].葛哲学,沙威.小波分析理论与MATLAB2007实现.第二版.北京：电子工业出版社,2007,33-39.
[34].Mahmoodabadi S.Z., Ahmadian A., Abolhasani M.D., Eslam M., Bidgoli J. H. ECG Feature Extraction Based on Multiresolution Wavelet Transform. Proceedings of the 2005 IEEE Engineering Medicine and Biology 27th Aunnual Conference Shanghai, China,Septemberl-4,2005.
[35].Lin He, Wensheng Hou, Xiaolin Zhen, Chenglin Peng. Recognition of ECG Patterns Using Artificial Neural Network.Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications,2006.
[36].Rajesh G., Ghatol A.A.Performance Analysis of Feature Extraction Schemes for Artificial Neural Network Based ECG Classification.International Conference on Computational Intelligence and Multimedia Applications,2007.
[37].梁仲钢,严洪.心率变异性的分析方法及其在航天医学领域的应用.航天医学与医学工程.2003(12)16.
[38].高尚,杨静宇.群智能算法及其应用.北京：中国水利水电出版社,2006，6-16.
[39]. S. Ktata, K. Ouni, and N. Ellouze, "ECG signal maxima detection using wavelet transform," Proceedings of IEEE International Symposium on Industrial Electronics, IEEE Industrial Electronic Society,2006, pp.700-703.
[40].Xie Xiaofeng, Zhang Wenjun, Yang Zhilian. Incorporating Knowledge in Genetic Glgorithms for Device Synthesis.6th Int Confon Solid-state and Integrated Circuit Technology.Shanghai.2001, 1143～1146.
[41]. Shi Y., Eberhart R C.A modified swarm optimizer.IEEE International Conference of Evolutionary Computation. Anchorage, Alaska:IEEE Press,1998.
[42]. Shi Y. and Eberhart R. C. Parameter selection in particle swarm optimization.Evolutionary Programming Ⅶ:Proceedings of the Seventh Annual Conference on Evolutionary Programming.New York,1998,591～600.
[43].M.Clerc, J.Kennedy. The Particle Swarm Explosion, Stability and Convergence in a Multidimensional Complex Space. IEEE Trans. On Evolutionary Computation.2002,6(1):58-63.
[44].J.Salerno. Using the Particle Swarm Optimization Technique to Train a Recurrent Neural Model.Proceedings of Ninth IEEE International Conference on Tools with Artificial Intelligence.1997, (11):45～49.
[45]. Cheng D. F., Liu, G. Y., Qiu Y. H.. Applications of particle swarm optimization and K-Nearest neighbors to emotion recognition from physiological signals.Proceeding of 2008 International Conference on Computational Intelligence and Security, Suzhou, China,2008,52-56.
[46].刘君君,王傅亮.赤潮生物图像分类识别技术研究：[硕士学位论文].浙江：厦门大学,2009,06.
[47].杨淑莹.图像模式识别.北京：清华大学出版社,2006，95-101.
[48].R.C.Eberhart, Hu Xiaohui. Human Tremor Analysis using Particle Swarm Optimization. Proc. of the Congress on Evolutionary Computation [C]. Washington.1999,1927～1930.
[49].冯林,颜世鹏,孙焘.邻域搜索的粒子群优化算法及其性能分析.计算机工程与科学,28(2006)12,72-73.
[50].Xu Ya, Liu Guangyuan, Hao Min, Wen Wanhui, Huang Xiting. Analysis of Affective ECG Signals toward Emotion Recognition.Journal of Electronics (China).2010,27(01), pp.8-14.
[51].李聪.基于小生境正交遗传算法的地区电网实时无功优化.广东输电与变电技术,11(2009)2,8-12.
[52].乔立岩,彭喜元,彭宇.基于微粒群算法和支持向量机的特征子集选择方.电子学报.2006,34：496～498.