An emotion analysis method and an electronic apparatus thereof are provided. The emotion analysis method is adapted to the electronic apparatus having a database or connected to the database in order to analyze an emotion of an examinee. The emotion analysis method includes: obtaining a heart rate signal of the examinee; defining a plurality of candidate emotions from the database; analyzing the heart rate signal to obtain a plurality of target emotion parameters; and analyzing the target emotion parameters to determine one of the candidate emotions corresponding to the heart rate signal by applying an emotion analysis model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An emotion analysis method, adapted to an electronic apparatus having a database or connecting to the database in order to analyze an emotion of an examinee, the emotion analysis method comprising: obtaining a heart rate signal of the examinee; defining a plurality of candidate emotions from the database; analyzing the heart rate signal to obtain a plurality of target emotion parameters; and analyzing the target emotion parameters to determine one of the candidate emotions corresponding to the heart rate signal by applying an emotion analysis model. 2. The emotion analysis method according to claim 1, wherein the step of analyzing the heart rate signal to obtain the target emotion parameters comprises: calculating a plurality of parameters of the heart rate signal in a time domain, a frequency domain, a statistical analysis and a Poincare plot to serve as a plurality of initial emotion parameters; and selecting at least part of the initial emotion parameters to serve as the target emotion parameters. 3. The emotion analysis method according to claim 2, wherein the step of selecting the at least part of the initial emotion parameters to serve as the target emotion parameters comprises: selecting the at least part of the initial emotion parameters to serve as the target emotion parameters by performing a principal components analysis (PCA). 4. The emotion analysis method according to claim 2, wherein the initial emotion parameters comprises an average value of a plurality of RR-intervals of the heart rate signal, a coefficient of variation of the RR-intervals of the heart rate signal, a standard deviation of the RR-intervals of the heart rate signal, a standard deviation of successive differences of the RR-intervals of the heart rate signal, a low frequency (LF) power of the heart rate signal, a high frequency (HF) power of the heart rate signal, a ratio of the LF power and the HF power of the heart rate signal, a kurtosis of the heart rate signal, a skewness of the heart rate signal, a first standard deviation of the heart rate signal in the Poincare plot, a second standard deviation of the heart rate signal in the Poincare plot and a ratio of the second standard deviation and the first standard deviation of the heart rate signal in the Poincare plot. 5. The emotion analysis method according to claim 1, wherein before analyzing the target emotion parameters to determine the one of the candidate emotions corresponding to the heart rate signal by applying the emotion analysis model, the emotion analysis method further comprises: obtaining a plurality of training heart rate signals in correspondence to the candidate emotions, respectively; obtaining a plurality of training emotion parameters from the training heart rate signals, respectively; and obtaining the emotion analysis model by training a classifier according to the training emotion parameters. 6. An electronic apparatus, for analyzing an emotion of an examinee, the electronic apparatus comprising: an information extraction device, obtaining an electrocardiogram signal from the examinee; and a processor, coupled to the information extraction device, wherein the processor obtains a heart rate signal of the examinee from the electrocardiogram signal and defines a plurality of candidate emotions from a database, wherein the processor analyzes the heart rate signal to obtain a plurality of target emotion parameters, and analyzes the target emotion parameters to determine one of the candidate emotions corresponding to the heart rate signal by applying an emotion analysis model. 7. The electronic apparatus according to claim 6, wherein the processor calculates a plurality of parameters of the heart rate signal in a time domain, a frequency domain, a statistical analysis and a Poincare plot to serve as a plurality of initial emotion parameters, and selects at least part of the initial emotion parameters to serve as the target emotion parameters. 8. The electronic apparatus according to claim 7, wherein the processor selects the at least part of the initial emotion parameters to serve as the target emotion parameters by performing a principal components analysis (PCA). 9. The electronic apparatus according to claim 7, wherein the initial emotion parameters comprises an average value of a plurality of RR-intervals of the heart rate signal, a coefficient of variation of the RR-intervals of the heart rate signal, a standard deviation of the RR-intervals of the heart rate signal, a standard deviation of successive differences of the RR-intervals of the heart rate signal, a low frequency (LF) power of the heart rate signal, a high frequency (HF) power of the heart rate signal, a ratio of the LF power and the HF power of the heart rate signal, a kurtosis of the heart rate signal, a skewness of the heart rate signal, a first standard deviation of the heart rate signal in the Poincare plot, a second standard deviation of the heart rate signal in the Poincare plot and a ratio of the second standard deviation and the first standard deviation of the heart rate signal in the Poincare plot. 10. The electronic apparatus according to claim 6, wherein before analyzing the target emotion parameters to determine the one of the candidate emotions corresponding to the heart rate signal by applying the emotion analysis model, the processor obtains a plurality of training heart rate signals in correspondence to the candidate emotions, respectively, obtains a plurality of training emotion parameters from the training heart rate signals, respectively, and obtains the emotion analysis model by training a classifier according to the training emotion parameters.