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Electroencephalogram (EEG) signal is non-stationary signal that have low frequency component and amplitude compared to stationary signal. Therefore, present of unwanted substance (nicotine) in Tobacco smoking will alter the brain electrical activity. This paper is proposed to investigate the changes of EEG signal with the present of nicotine and identify the difference brain signal between smoker and non-smoker. There are 20 males (10 smokers, 10 non-smokers) are selected. The subjects are chosen based on inclusion criteria (abstained from smoking within 6 hours before experiment, and do not take any medication and caffeine). The recorded EEG signal contain a lot of noise such as head moving, muscle movement, power line, eyes blinks and interference with other device. Butterworth filter are implemented to remove the unwanted noise present in the original signal. Bandpass filter is used to decompose the EEG signal into alpha, theta, delta and beta frequency. Then, eight features (mean, median, maximum, minimum, variance, standard deviation, energy and power) have been extracted by using Fast Fourier Transform (FFT) and Power Spectral Density (PSD) method. Then, four different type of kernel function (‘Linear’, ‘BoxConstraint’, ‘Polynomial’ and ‘RBF’) of SVM classifier are used to identify the best accuracy. As a result, PSD (97.50%) have higher performance accuracy than FFT (97.33%) by using Radial Basis Function (RBF) of Support Vector Machine (SVM). Smoking activity caused slightly increase theta and delta frequency. Smoking is activated of five electrode channels (Fp1, Fp2, F8, F3 and C3) and caused additional emotion such as deep rest, stress releasing and losing attention. The attention of smokers can be measure by using stroop test. After smoking activity, smokers become more energetic and increase the time response (1.77 s) of stroop test compared to non-smokers (2.96 s). The result is calculated by using statistical analysis (t-test). The p-value is 0.037 which is less than 0.05. Thus, the null hypothesis is rejected and conclude there is significant different between smokers and non-smoker performance before and after smoking task.
Electroencephalogram (EEG) signal is non-stationary signal that have low frequency component and amplitude compared to stationary signal. Therefore, present of unwanted substance (nicotine) in Tobacco smoking will alter the brain electrical activity. This paper is proposed to investigate the changes of EEG signal with the present of nicotine and identify the difference brain signal between smoker and non-smoker. There are 20 males (10 smokers, 10 non-smokers) are selected. The subjects are chosen based on inclusion criteria (abstained from smoking within 6 hours before experiment, and do not take any medication and caffeine). The recorded EEG signal contain a lot of noise such as head moving, muscle movement, power line, eyes blinks and interference with other device. Butterworth filter are implemented to remove the unwanted noise present in the original signal. Bandpass filter is used to decompose the EEG signal into alpha, theta, delta and beta frequency. Then, eight features (mean, median, maximum, minimum, variance, standard deviation, energy and power) have been extracted by using Fast Fourier Transform (FFT) and Power Spectral Density (PSD) method. Then, four different type of kernel function (‘Linear’, ‘BoxConstraint’, ‘Polynomial’ and ‘RBF’) of SVM classifier are used to identify the best accuracy. As a result, PSD (97.50%) have higher performance accuracy than FFT (97.33%) by using Radial Basis Function (RBF) of Support Vector Machine (SVM). Smoking activity caused slightly increase theta and delta frequency. Smoking is activated of five electrode channels (Fp1, Fp2, F8, F3 and C3) and caused additional emotion such as deep rest, stress releasing and losing attention. The attention of smokers can be measure by using stroop test. After smoking activity, smokers become more energetic and increase the time response (1.77 s) of stroop test compared to non-smokers (2.96 s). The result is calculated by using statistical analysis (t-test). The p-value is 0.037 which is less than 0.05. Thus, the null hypothesis is rejected and conclude there is significant different between smokers and non-smoker performance before and after smoking task.
There scientific and therapeutic advances in perinatology and neonatology have improved the survival prospects of preterm and extremely low birth weight infants. Infants' cries are a valuable noninvasive tool for monitoring their neurologic health, especially if they are premature. An automatic acoustic analysis and data mining are employed in this study to determine the discriminative features of preterm and full-term infant cries. The use of machine learning for recognizing sounds in a newborn's cry language has received less attention than previous methods for analyzing the sounds. Moreover, to extract appropriate features from infant cries, adequate knowledge and appropriate signal descriptors are required. Accordingly, to analyze infant cry language, we propose an approach that uses fractal descriptor to extract discriminant features from spectrograms of windowed signals, followed by iterative neighborhood component analysis (iNCA) to select appropriate features. Additionally, the improved deep support vector machine (deepSVM) is used to classify the infants' crying types and their meanings. The proposed method is verified using a newborn sound dataset. According to the classification of five types of crying perception based on various characteristics, 98.34% of all crying perceptions have been recognized. Although there are many classes examined, the feature extraction method based on the fractal method and our optimal classification have a much higher diagnostic accuracy compared to similar methods for analyzing baby crying language. The proposed method can overcome many problems associated with analyzing babies' crying sounds and understanding their language, like uncertainty and unusual errors in classification.
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