本文選題：心電情感數(shù)據(jù)庫 + 小波變換　； 參考：《燕山大學(xué)》2014年碩士論文

【摘要】：情感數(shù)據(jù)獲取的有效性與合理性是認(rèn)知情感計算研究中的關(guān)鍵問題，因此，建立性能良好的情感計算數(shù)據(jù)庫是情感計算研究的重要部分，也是該領(lǐng)域?qū)W者研究的熱點。心電情感信號是非常重要的生理信號，大量研究已證明其與人體情感狀態(tài)有極大相關(guān)性，并且，心電信號在臨床應(yīng)用廣泛，采集分析技術(shù)相對成熟。因此，本文通過情感誘發(fā)實驗，采集心電情感信號，建立心電信號情感數(shù)據(jù)庫。 研究發(fā)現(xiàn)影視片段比圖片和音樂等素材更能成功誘發(fā)人的情感，本文從大量的影視素材中剪輯出能夠誘發(fā)特定情感的電影片段進(jìn)行情感誘發(fā)。情感誘發(fā)實驗采集到身體健康、無心臟病史的25名學(xué)生，分別在高興、憤怒、悲傷、恐懼種情感狀態(tài)下的心電信號。從心電情感信號中截取出60秒有效數(shù)據(jù)作為樣本，通過小波變換進(jìn)行去除基線漂移等預(yù)處理，建立心電信號的情感數(shù)據(jù)庫。 情感特征提取直接影響情感分析與識別效果，心率變異性反映了人體內(nèi)外環(huán)境對心血管系統(tǒng)的擾動以及心血管系統(tǒng)通過自主神經(jīng)及體液調(diào)節(jié)對這種擾動的反應(yīng)，對不同生理狀態(tài)甚至情感狀態(tài)的變化都比較敏感，蘊含了有關(guān)情感的大量信息，，因此，能夠用來進(jìn)行情感識別。本文提出一種基于小波變換與獨立成分分析結(jié)合的算法來進(jìn)行心率變異性時頻分析，共獲得21個心率變異性特征。同時結(jié)合提取的心電信號時域特征79個、心電信號小波特征36個，常規(guī)心率變異時頻特征21個，共提取出157個心電情感特征。對情感特征集進(jìn)行心電情感識別分析，并進(jìn)一步采用Relief-F、最大相關(guān)最小冗余算法、主成分分析和獨立成分結(jié)合算法進(jìn)行特征選擇及優(yōu)化組合，并采用遺傳算法優(yōu)化后的支持向量機(jī)分別進(jìn)行單一情感識別，平均識別正確率均達(dá)到90%以上。結(jié)果表明，本文建立的心電信號情感數(shù)據(jù)庫具有較好性能，并且提取的情感特征具有較強(qiáng)的情感識別能力。與常規(guī)心率變異性特征、心電時域特征、心電小波特征進(jìn)行比較，該算法提取的心率變異性特征對情感識別同樣取得較顯著的識別能力。
[Abstract]:The validity and rationality of emotional data acquisition is a key issue in cognitive affective computing. Therefore, the establishment of an affective computing database with good performance is an important part of affective computing, and also a hot topic for researchers in this field. Electrocardiogram (ECG) emotional signal is a very important physiological signal. A large number of studies have proved that it has a great correlation with the emotional state of human body. Furthermore, ECG signal is widely used in clinical practice, and the technology of collection and analysis is relatively mature. Therefore, this paper collects ECG emotional signals and establishes ECG emotional database by affective induction experiment. The study found that film and television clips can induce human emotion more successfully than pictures and music. The affective induction experiment collected ECG signals from 25 healthy students who had no history of heart disease in the emotional states of joy anger sadness and fear. The effective data of 60 seconds was extracted from the ECG emotion signal as a sample, and the baseline drift was removed by wavelet transform to establish the emotion database of ECG signal. Emotion feature extraction directly affects the effect of emotion analysis and recognition. Heart rate variability reflects the disturbance of cardiovascular system in human body and the response of cardiovascular system to this disturbance through autonomic nerve and body fluid regulation. It is sensitive to the changes of different physiological states and even emotional states, and contains a lot of information about emotion, so it can be used for emotion recognition. In this paper, an algorithm based on wavelet transform and independent component analysis is proposed to analyze heart rate variability (HRV). A total of 21 HRV features are obtained. At the same time, 79 ECG temporal features, 36 ECG wavelet features and 21 time-frequency features of conventional heart rate variability were extracted. A total of 157 ECG emotion features were extracted. The emotion feature set is analyzed by ECG emotion recognition, and further, Relief-F, maximum correlation and minimum redundancy algorithm, principal component analysis and independent component combination algorithm are used to select and optimize the feature. The support vector machine (SVM), which is optimized by genetic algorithm, is used for single emotion recognition, and the average recognition accuracy is over 90%. The results show that the ECG emotion database established in this paper has a good performance and the extracted emotion features have a strong ability of emotion recognition. Compared with conventional heart rate variability (HRV), ECG time domain (ECG) and ECG wavelet (ECG wavelet), the HRV feature extracted by this algorithm can also achieve significant recognition ability for emotion recognition.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP311.13;TN911.7

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