196,281 research outputs found
Electromyogram signal based human emotion classification using KNN and LDA
No full textProceeding of The IEEE International Conference on System Engineering and Technology, (ICSET 2011) at Shah Alam, Malaysia on 27 June 2011 through 28 June 2011. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jspIn this paper, we presents Electromyogram (EMG) signal based human emotion classification using K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA). Five most dominating emotions such as: happy, disgust, fear, sad and neutral are considered and these emotions are induced through Audio-visual stimuli (video clips). EMG signals are obtained by using 3 electrodes over 10 trials per emotion and preprocessed by using Butterworth 6th order filter to remove noises and external interferences. EMG signals on decomposed into four different frequency ranges ((8 Hz-16 Hz), (16 Hz-31 Hz) and (16 Hz-63 Hz)) using Discrete Wavelet Transform (DWT). The ststistical features extracted from the above frequency bands are mapped into five different emotions using two simple classifiers such as KNN and LDA. The value of K in KNN is varied randomly, and maximum classification rate is achieved at K=3. KNN classifier gives the highest classification rate on four emotions (disgust, happy, fear and neutral) different emotions and LDA on sad emotion. The maximum classification rate of disgust, happy, fear neutral, and sad are 90.83%, 100%, 94.17%, and 90.28% and 43.89%, respectively are achieved using KNN and LDA. The results from the proposed methodology are promising and female are easily evoked by different emotional stimuli compared to male
ASPECCT: panitumumab versus cetuximab for colorectal cancer – authors' reply
No full textCorrespondenceTimothy J Price, Marc Peeters, Paul Ruff, Swaminathan Murugappan, Roger Sidh
Wireless EEG signals based neuromarketing system using Fast Fourier Transform (FFT)
No full textProceeding of The Proceeding of the 10th International Colloquium on Signal Processing & Its Applications (CSPA 2014) at Kuala Lumpur, Malaysia on 7 March 2014 until 9 March 2014. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsphis work aims to identify the most preferred brand on automotive in Malaysia through wireless EEG signals. A group of four major vehicle brand advertisements such as Toyota, Audi, Proton and Suzuki is considered on this work. An advertisement (video) of above said brands were used to simulate the subjects (9 male and 3 female with age range of 22–24 years) and the brain signal responses for the stimuli were collected using 14 channel wireless Emotiv headset with a sampling frequency of 128 Hz. The acquired signals are preprocessed using 4th order Butterworth band pass filter with a cut off frequency of 0.5 Hz–60 Hz and smoothed using Surface Laplacian filter. The alpha frequency band (8 Hz–13 Hz) of EEG signal information has been extracted using the Butterworth 4th order filter. The frequency spectrum of Alpha band is obtained through Fast Fourier Transform (FFT) to extract three statistical features such as power spectral density (PSD), spectral energy (SE) and spectral centroid (SC) from the EEG signals. Extracted features on all the subjects over four different advertisement stimuli are used to develop the feature vector. This feature vector is further given to a two non-linear classifiers namely K Nearest Neighbor (KNN) and Probabilistic Neural Network (PNN) for classifying the subject intention on advertisements. This present experimental results indicate that, the subjects are mostly inspired on Toyota brand vehicles compared to other brands. The maximum mean classification rate of 96.62% is achieved using PSD feature and PNN classifier
Frequency band analysis of electrocardiogram (ECG) signals for human emotional state classification using discrete wavelet transform (DWT)
No full textLink to publisher's homepage at https://www.jstage.jst.go.jp/[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coifet5 (coif5). The k-nearest neighbor (KNN) and linear discriminate analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness - 50.28%; happiness - 79.03%; fear - 77.78%; disgust - 88.6%; and neutral - 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems
A study on Mental Arithmetic Task based human stress level classification using Discrete Wavelet Transform
No full textProceeding of The 3rd IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT 2012) at Kuala Lumpur, Malaysia on 6 October 2012 through 9 October 2012. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jspSeveral studies examined human stress identification using Mental Arithmetic Task (MAT). The identification and prediction of stress levels using existing data processing methodologies are incompetent to predict the stress levels either in real time or laboratory based experiments. The main objectives of the present work is to classify the stress levels using mental arithmetic task and appropriate signal processing methodology, (ii) to analyze the characteristics of Electrocardiogram (ECG) signal for different stress levels, and (iii) to derive the optimum features from a set of statistical features over different frequency bands. Ten healthy female subjects (20 to 25) years voluntarily participated and ECG signal was acquired. In this work, High Frequency (HF) and Low Frequency (LF) frequency band of ECG signal is directly analyzed similar frequency ranges of Heart Rate Viability (HRV) signals. Discrete Wavelet Transform (DWT) have employed for identifying the stress relevant effect of ANS activity during different stress levels. Statistical features derived using DWT are mapped into four different states including three stress levels (normal, low stress, medium stress, and high stress) using K-Nearest Neighbor (KNN) classifier. Covariance feature gives the maximum mean classification rate of 96.3%, and 75.9% in LF and HF bands, respectively. In addition, the maximum average classification accuracy of 65.5% is achieved using mean feature in LF/HF+LF and HF/HF+LF ratios
ECG signals based mental stress assessment using wavelet transform
No full textProceeding of The IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2011) at Penang, Malaysia on 25 November 2011 through 27 November 2011. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jspThis paper describes the mental stress assessment using Electrocardiography (ECG) signal. Stress reflects the changes in heart rates under stressful situation. In this work, Heart Rate Variability (HRV) from ECG signal is used to study the activity of Autonomic Nervous System (ANS) under stress states. The Stroop colour word test is used to induce stress and ECG signal was simultaneously acquired from the 10 female subjects in the age range of (20 - 25) years in non invasive manner. An acquired ECG signals are preprocessed using 4 th order elliptic band pass filter. The High Frequency (HF) and Low Frequency (LF) bands of ECG signals were considered to extract the stress related features through Discrete Wavelet Transform (DWT) using db4 wavelet function. The extracted features are mapped into two states such as stress and relax using a K Nearest Neighbour (KNN). The experimental results show the maximum average classification accuracy of 96.41% on classifying the stress and relax states from the ECG signals
EMG Signal Based Human Stress Level Classification Using Wavelet Packet Transform
No full textLink to publisher's homepage at http://link.springer.com/Recent days, Electromyogram (EMG) signal acquired from muscles can be useful to measure the human stress levels. The aim of this present work to investigate the relationship between the changes in human stress levels to muscular tensions through Electromyography (EMG) in a stimulated stress-inducement environment. The stroop colour word test protocol is used to induce the stress and EMG signal is acquired from left trapezius muscle of 10 female subjects using three surface electrodes. The acquired signals were preprocessed through wavelet denoising method and statistical features were extracted using Wavelet Packet Transform (WPT). EMG signals are decomposed to four levels using db5 mother wavelet function. Frequency band information's of third and fourth levels are considered for descriptive analysis. Totally, seven statistical features were computed and analyzed to find the appropriate frequency band and feature for stress level assessment. A simple non-linear classifier (K Nearest Neighbor (KNN)) is used for classifying the stress levels. Statistical features derived from the frequency range of (0-31.5) Hz gives a maximum average classification accuracy of 90.70% on distinguishing the stress levels in minimum feature
Subtractive fuzzy classifier based driver drowsiness levels classification using EEG
No full textProceeding of The 2nd International Conference on Communication and Signal Processing (ICCSP 2013) at Melmaruvathur, Tamilnadu, India on 3 April 2013 through 5 April 2013. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jspDriver drowsiness is one of the major causes for several road accidents over the world. In this study, Electroencephalogram (EEG) signals were acquired using 14 electrodes from 50 subjects. All the electrodes are placed on the driver scalp based on International 10/20 standard and Butterworth 4 th order filter was used to remove the noise and artifact. Four EEG frequency bands (delta, theta, alpha, and beta) were analyzed on this work and extracted using Discrete Wavelet Packet Transform (DWPT). Fast Fourier Transform (FFT) was used to extract two statistical features such as spectral centroid and power spectral density (PSD) from the above frequency bands. Subtractive fuzzy classifier was used to map the extracted features into four different driver drowsiness levels namely, awake, drowsy, high drowsy and sleep stage1. As a result of this study points out the best average accuracy achieved by subtractive fuzzy inference classifier is 84.41% based on power spectral density feature extracted by 'db4' wavelet function
Development of cost effective ECG data acquisition system for clinical applications using LabVIEW
No full textProceeding of The Proceeding of the 10th International Colloquium on Signal Processing & Its Applications (CSPA 2014) at Kuala Lumpur, Malaysia on 7 March 2014 until 9 March 2014. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jspThe main objective of this work is to develop a portable and cost effective data acquisition (DAQ) system for clinical applications. This DAQ consists of several modules such as power supply, analog to digital converter (ADC), amplifiers, isolators, filters and interfacing circuits. The complete data acquisition circuit has been developed using This system mainly aims to collect the ECG signals of frequency between 0.05 Hz and 113 Hz with a gain of 3113. This frequency information from the ECG signal is highly useful clinical applications such as SCA prediction, cardiovascular disease (CVD) detection, etc. ECG signals will be collected from the subjects using 3 leads system and given to DAQ for recording the ECG signal. The acquired signal through this DAQ will then be transferred to the Notebook through NI6008 data acquisition card. This DAQ interface is used to convert the input analog signal to digital signal output and to save the ECG data in the notebook using Labview software. This acquired signal from Labview software is used for further clinical investigation. We also developed a Graphical User Interface (GUI) in LabVIEW software to continuously monitor the ECG signal traces and to record the ECG data with higher precision. The morphology of the acquired ECG signal in the system is highly precise and useful for clinical diagnosis. Furthermore, this proposed system is used for developing sudden cardiac arrest (SCA) prediction in our university
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