| dc.description.abstract | This thesis focuses on analyzing different nonlinear algorithms to capture the emotional
information present in two bio-signals namely Electrocardiogram (ECG) and
Electromyogram (EMG). Emotion recognition is one of the emerging research areas in
human computer interaction (HCI) and has been widely used in applications such as elearning,
smart classrooms, medical applications for patients with autism, Parkinson’s
disease etc., Five basic emotional states (happiness, sadness, fear, surprise and disgust)
and neutral signal was induced in sixty subjects by means of audio-visual stimuli. The
emotion anger was omitted as a result of the pilot study conducted to identify the
emotional video clips that could elicit the target emotions in a better way. ECG and
EMG signals were pre-processed to eliminate noises that occur due to power line
interference and high frequency.QRS complex was then derived from the ECG signals
by using a derivative based algorithm. The EMG signals were smoothed and the trend
was removed. The emotional frequency was identified by validating the conventional
statistical features used for emotion recognition applications using analysis of variance
(ANOVA) at different frequency levels. The emotional features extracted from QRS
complex and EMG signals at the identified emotional frequency range was classified
using four classifiers (Regression Tree, Naïve Bayes, K-nearest neighbor (KNN) and
Fuzzy K-Nearest Neighbor (FKNN)). Statistical, Higher Order Statistical (HOS) and
non-linear features were obtained from the filtered signals and the signals processed by
Hilbert Huang Transform (HHT). A hybrid system replacing Hilbert Transform by
Discrete Fourier Transform (DFT) to the signal decomposed and reconstructed by
Empirical Mode Decomposition (EMD) was proposed. DFT based method performed
better in case of ECG whereas HHT performed better in case of EMG signals. The nonlinear
feature Hurst was extracted from the filtered signals using two methods namely
Rescaled Range Statistics (RRS) and Finite Variance Analysis (FVA). New methods
such as Skewness based RRS, Kurtosis based RRS, Skewness based FVA and Kurtosis
based FVA were proposed for computing Hurst by combining HOS with the traditional
methods. The features achieved in all the methods were found to be statistically
significant (p<0.01). Kurtosis based FVA method performed better for both ECG and
EMG signals with an accuracy of 78% and 62% respectively. A system that combines
both ECG and EMG signals was obtained by combining the features using principal
component analysis (PCA) and multi dimensional scaling (MDS). Principal component
analysis (PCA) applied on all the Hurst features derived using FVA for both the signals
resulted in an improved accuracy of 82.54%. | en_US |
