E. Zarei; N. Barimani; G. Nazari Golpayegani
Abstract
Cardiac Arrhythmias are known as one of the most dangerous cardiac diseases. Applying intelligent algorithms in this area, leads into the reduction of the ECG signal processing time by the physician as well as reducing the probable mistakes caused by fatigue of the specialist. The purpose of this study ...
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Cardiac Arrhythmias are known as one of the most dangerous cardiac diseases. Applying intelligent algorithms in this area, leads into the reduction of the ECG signal processing time by the physician as well as reducing the probable mistakes caused by fatigue of the specialist. The purpose of this study is to introduce an intelligent algorithm for the separation of three cardiac arrhythmias by using chaos features of ECG signal and combining three types of the most common classifiers in these signal’s processing area. First, ECG signals related to three cardiac arrhythmias of Atrial Fibrillation, Ventricular Tachycardia and Post Supra Ventricular Tachycardia along with the normal cardiac signal from the arrhythmia database of MIT-BIH were gathered. Then, chaos features describing non-linear dynamic of ECG signal were extracted by calculating the Lyapunov exponent values and signal’s fractal dimension. finally, the compound classifier was used by combining of multilayer perceptron neural network, support vector machine network and K-Nearest Neighbor. Obtained results were compared to the classifying method based on features of time-domain and time-frequency domain, as a proof for the efficacy of the chaos features of the ECG signal. Likewise, to evaluate the efficacy of the compound classifier, each network was used both as separately and also as combined and the results were compared. The obtained results showed that Using the chaos features of ECG signal and the compound classifier, can classify cardiac arrhythmias with 99.1% ± 0.2 accuracy and 99.6% ± 0.1 sensitivity and specificity rate of 99.3 % ± 0.1
H.3.15.2. Computational neuroscience
A. Goshvarpour; A. Abbasi; A. Goshvarpour
Abstract
Emotion, as a psychophysiological state, plays an important role in human communications and daily life. Emotion studies related to the physiological signals are recently the subject of many researches. In This study a hybrid feature based approach was proposed to examine affective states. To this effect, ...
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Emotion, as a psychophysiological state, plays an important role in human communications and daily life. Emotion studies related to the physiological signals are recently the subject of many researches. In This study a hybrid feature based approach was proposed to examine affective states. To this effect, Electrocardiogram (ECG) signals of 47 students were recorded using pictorial emotion elicitation paradigm. Affective pictures were selected from the International Affective Picture System and assigned into four different emotion classes. After extracting approximate and detail coefficients of Wavelet Transform (WT / Daubechies 4 at level 8), two measures of the second-order difference plot (CTM and D) were calculated for each wavelet coefficient. Subsequently, Least Squares Support Vector Machine (LS-SVM) was applied to discriminate between affective states and the rest. The statistical analysis indicated that the density of CTM in the rest is distinctive from the emotional categories. In addition, the second-order difference plot measurements at the last level of WT coefficients showed significant differences between the rest and emotion categories. Applying LS-SVM, the maximum classification rate of 80.24 % was reached for discrimination between rest and fear. The results of this study indicate the usefulness of the WT in combination with nonlinear technique in characterizing emotional states.
