An accurate emotion recognition system using ECG and GSR signals and matching pursuit method

The potential of photoplethysmogram and galvanic skin response in emotion recognition using nonlinear features

Recently, developing an accurate automatic emotion recognition system using a minimum number of bio-signals has become a challenging issue in "affective computing." This study aimed to propose a reliable system by examining nonlinear dynamics of photoplethysmogram (PPG) and galvanic skin response (GSR). To address this goal, two strategies were adopted. First, the efficiency of each signal in valence/arousal based emotion categorization was examined. Then, the proficiency of a hybrid feature, by combining both GSR and PPG features was studied. Lyapunov exponents, lagged Poincare's measures, and approximate entropy were extracted to characterize the irregularity and chaotic behavior of the phase space. To discriminate two levels of arousal and two levels of the valence, a probabilistic neural network (PNN) with different sigma adjustment parameter was examined. The results showed that the phase space geometry and consequently, the signal dynamics are influenced by the emotional music video. Additionally, distinctive patterns of the phase space behavior were observed under the influence of different lags. For both signals, the most irregularity was observed during the high valence, and the least irregularity was seen during the low valence. Consequently, signals' irregularity is affected by the valence dimension. The results showed that the fusion has more potential for emotion recognition than that of using each signal separately. For sigma = 0.1, the highest recognition rate was 100% in a subject-dependent mode. In a subject-independent mode, the maximum accuracies of 88.57 and 86.8% were obtained for arousal and valence dimensions, respectively.

Human identification using a new matching Pursuit-based feature set of ECG

BACKGROUND AND OBJECTIVE

In recent years, many attempts have been made to design reliable systems for identifying individuals using biometrics. Electrocardiogram (ECG) biometric is one of the newest methods that not only offers unique characteristics of individuals for human identification, but also the possibility of counterfeiting it is negligible. In this paper, our objective was to develop an identification system using a non-fiducial one-lead ECG feature set based on a sparse algorithm.

METHODS

The ECG signals of 90 participants were decomposed using a matching pursuit (MP) and several statistical and nonlinear measures were extracted from the MP coefficients. Then, the performance of ECG characteristics delivered by MP analysis in human identification was evaluated by the probabilistic neural network (PNN) and k-nearest neighbor (kNN) with one vs. all strategy. The role of the feature set in classification rates was also tested in different modes, including linear attributes, nonlinear indices, all features, features selected by principal component analysis (PCA), and features selected by linear discriminant analysis (LDA).

RESULTS

Experimental results showed that (1) the highest recognition rate was 99.68%; (2) the performance of the PNN was superior to the kNN; and (3) selecting features with LDA resulted in higher identification rates.

CONCLUSIONS

The results are prominent from the performance perspective because it gives higher recognition rates over the group of 90 participants. The great performance of the proposed identification system advocates that it can be employed confidently in different smart systems.

Microbes are off the menu: Defective macrophage phagocytosis in COPD

In this issue of the Biomedical Journal, we learn about the pathophysiology of chronic obstructive pulmonary disease and how defective macrophage phagocytosis may lead to the build up of microbes and pollutants in inflamed lungs. We also focus on new findings that may take us a step closer to full automation in diagnostic bacteriology laboratories. Finally, we highlight the anti-tumor properties of microalgae and the application of algorithms to predict human emotion from electrocardiogram.