Key result
Neural network-driven Matching Pursuit algorithm achieves 100% emotion recognition accuracy from ECG signals.
Observational (n=11)
No
An emotion recognition system using the Matching Pursuit algorithm and wavelet dictionaries on ECG and GSR signals achieved up to 100% recognition accuracy in a small cohort of healthy students.
High accuracy in tiny healthy cohort merits larger validation trials; leaves open clinical utility of ECG-based emotion detection.
BACKGROUND: The purpose of the current study was to examine the effectiveness of Matching Pursuit (MP) algorithm in emotion recognition. METHODS: Electrocardiogram (ECG) and galvanic skin responses (GSR) of 11 healthy students were collected while subjects were listening to emotional music clips. Applying three dictionaries, including two wavelet packet dictionaries (Coiflet, and Daubechies) and discrete cosine transform, MP coefficients were extracted from ECG and GSR signals. Next, some statistical indices were calculated from the MP coefficients. Then, three dimensionality reduction methods, including Principal Component Analysis (PCA), Linear Discriminant Analysis, and Kernel PCA were applied. The dimensionality reduced features were fed into the Probabilistic Neural Network in subject-dependent and subject-independent modes. Emotion classes were described by a two-dimensional emotion space, including four quadrants of valence and arousal plane, valence based, and arousal based emotional states. RESULTS: Using PCA, the highest recognition rate of 100% was achieved for sigma = 0.01 in all classification schemes. In addition, the classification performance of ECG features was evidently better than that of GSR features. Similar results were obtained for subject-dependent emotion classification mode. CONCLUSIONS: An accurate emotion recognition system was proposed using MP algorithm and wavelet dictionaries.
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Goshvarpour et al. (2017) conducted an observational in Healthy (Emotion Recognition) (n=11). Matching Pursuit (MP) algorithm with PCA and Probabilistic Neural Network on ECG signals vs. GSR signals and other feature selection methods (LDA, K-PCA) was evaluated on Emotion recognition accuracy rate. Using Principal Component Analysis and a Probabilistic Neural Network, the Matching Pursuit algorithm achieved a 100% emotion recognition rate from ECG signals.
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