Browsing M. Murugappan, Dr. by Issue Date

Now showing items 41-60 of 67

    • Classification of driver drowsiness level using wireless EEG 

      Mousa Kadhim, Wali; Murugappan, Muthusamy, Dr.; R. Badlishah, Ahmad, Prof. Dr. (Przegląd Elektrotechniczny, 2013)
      In this work, wireless Electroencephalogram (EEG) signals are used to classify the driver drowsiness levels (neutral, drowsy, high drowsy and sleep stage1) based on Discrete Wavelet Packet Transform (DWPT). Two statistical ...

    • Sudden cardiac death prediction using ECG signal derivative (heart rate variability): a review 

      Murukesan, Loganathan; Murugappan, M.; Mohammad Iqbal, Omar@Ye Htut, Assoc. Prof. Dr. (IEEE Conference Publications, 2013)
      Sudden cardiac death (SCD) prediction using Electrocardiogram (ECG) signal is a popular area of research because of the seriousness of the matter. There are tons of papers published in this research which available online. ...

    • Drowsiness detection during different times of day using multiple features 

      Sahayadhas, Arun; Sundaraj, Kenneth, Prof. Dr.; Murugappan, M (Springer Netherlands, 2013)
      Driver drowsiness has been one of the major causes of road accidents that lead to severe trauma, such as physical injury, death, and economic loss, which highlights the need to develop a system that can alert drivers of ...

    • Emotion processing in Parkinson's disease: an EEG spectral power study 

      Yuvaraj, Rajamanickam; Murugappan, M; Mohd Iqbal, Omar@Ye Htut, Assoc. Prof. Dr.; Norlinah, Mohamed Ibrahim; Sundaraj, Kenneth, Prof. Dr.; Khairiyah, Mohamad; Satiyan, M (Informa Healthcare, 2013)
      Objective: Although an emotional deficit is a common finding in Parkinson's disease (PD), its neurobiological mechanism on emotion recognition is still unknown. This study examined the emotion processing deficits in PD ...

    • Multiple physiological signal-based human stress identification using non-linear classifiers 

      Karthikeyan, Palanisamy; Murugappan, Muthusamy, Dr.; Sazali, Yaacob, Prof. Dr. (2013)
      This paper describes the human stress identification using multiple physiological signals. The Electrocardiogram (ECG), Electromyogram (EMG), Heart Rate Variability (HRV), Galvanic Skin Response (GSR), and Skin Temperature ...

    • Development of EEG data acquisition device by using single board computer 

      Wali, Mousa Kadhim; Murugappan, M., Dr.; R. Badlishah, Ahmad, Prof. Dr. (Inderscience Enterprises Ltd., 2013)
      Electroencephalogram (EEG) plays a vital role in several medical diagnosis (brain tumour detection, Alzheimer disease, epilepsy, etc.), engineering applications (emotion detection, drowsiness detection, stress assessment, ...

    • Subtractive fuzzy classifier based driver distraction levels classification using EEG 

      Wali, Mousa Kadhim; Murugappan, M., Dr.; R. Badlishah, Ahmad, Prof. Dr. (Society of Physical Therapy Science, 2013)
      [Purpose] In earlier studies of driver distraction, researchers classified distraction into two levels (not distracted, and distracted). This study classified four levels of distraction (neutral, low, medium, high). [Subjects ...

    • Wavelet packet transform based driver distraction level classification using EEG 

      Wali, Mousa Kadhim; Murugappan, M., Dr.; R. Badlishah, Ahmad, Prof. Dr. (Mathematical Problems in Engineering, 2013)
      We classify the driver distraction level (neutral, low, medium, and high) based on different wavelets and classifiers using wireless electroencephalogram (EEG) signals. 50 subjects were used for data collection using 14 ...

    • Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT) 

      Murugappan, Muthusamy, Dr.; Murugappan, Subbulakshmi (Institute of Electrical and Electronics Engineers (IEEE), 2013-03)
      Human emotion recognition plays a vital role in psychology, psycho-physiology and human machine interface (HMI) design. Electroencephalogram (EEG) reflects the internal emotional state changes of the subject compared to ...

    • Detection of human stress using short-term ECG and HRV signals 

      Karthikeyan, Palanisamy; Murugappan, Muthusamy, Dr.; Sazali, Yaacob, Prof. Dr. (World Scientific Publishing Company, 2013-04)
      This paper introduces a method for resolving the problem of human stress detection through short-term (less than 5 min) electrocardiogram (ECG) and heart rate variability (HRV) signals. The explored methodology helps to ...

    • FCM clustering of emotional stress using ECG features 

      Zheng, Bong Siao; Murugappan, M., Dr.; Sazali, Yaacob, Prof. Dr. (IEEE Conference Publications, 2013-04)
      Emotional stress refers to the inducement of stress due to the consequence of a continuous experience of negative emotions (sad, anger, fear and disgust). This work aims to investigate the effect of negative emotions in ...

    • Subtractive fuzzy classifier based driver drowsiness levels classification using EEG 

      Murugappan, M., Dr.; Wali, Mousa Kadhim; R. Badlishah, Ahmad, Prof. Dr.; Murugappan, Subbulakshmi (IEEE Conference Publications, 2013-04)
      Driver 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 ...

    • Classification of emotional States from electrocardiogram signals: a non-linear approach based on hurst 

      Selvaraj, Jerritta; Murugappan, M., Dr.; Wan Khairunizam, Wan Ahmad, Dr.; Sazali, Yaacob, Prof. Dr. (BioMed Central, 2013-05)
      Background: Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) ...

    • PNN based driver drowsiness level classification using EEG 

      Mousa Kadhim, Wali; Murugappan, Muthusamy, Dr.; R. Badlishah, Ahmad, Prof. Dr. (JATIT & LLS. All rights reserved, 2013-06)
      In this work, we classify the driver drowsiness level (awake, drowsy, high drowsy and sleep stage1) based on different wavelets and probabilistic neural network classifier using wireless EEG signals. Deriving the amplitude ...

    • Frequency band analysis of electrocardiogram (ECG) signals for human emotional state classification using discrete wavelet transform (DWT) 

      Murugappan, Muthusamy, Dr.; Murugappan, Subbulakshmi; Bong, Siao Zheng (Society of Physical Therapy Science, 2013-06)
      [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 ...

    • Frequency band analysis of electrocardiogram (ECG) signals for human emotional state classification using discrete wavelet transform (DWT) 

      Murugappan, M., Dr.; Murugappan, Subbulakshmi; Zheng, Bong Siao (Society of Physical Therapy Science, 2013-06)
      [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 ...

    • Emotion detection from QRS complex of ECG signals using hurst exponent for different age groups 

      Selvaraj, Jerritta; Murugappan, M., Dr.; Wan Khairunizam, Wan Ahmad, Dr.; Sazali, Yaacob, Prof. Dr. (IEEE Conference Publications, 2013-09)
      Emotion recognition using physiological signals is one of the key research areas in Human Computer Interaction (HCI). In this work, we identify the six basic emotional states (Happiness, sadness, fear, surprise, disgust ...

    • Hospital nurse following robot: hardware development and sensor integration 

      Bukhari, Ilias; Ramachandran, Nagarajan, Prof. Dr.; Murugappan, M., Dr.; Khaled, Helmy, Dr.; Awang Sabri, Awang Omar; Muhammad Asyraf, Abdul Rahman (Inderscience Enterprises Ltd., 2014)
      Hospital nurse regularly bring her instrument to the patient using cart. They need to push or pull the cart to the patient bed and bring it back many times in a day. This can be tiresome for nurses because they need to ...

    • Frequency study of facial electromyography signals with respect to emotion recognition 

      Selvaraj, Jerritta; Murugappan, Muthusamy, Dr.; Wan Khairunizam, Wan Ahmad, Dr.; Sazali, Yaacob, Prof. Dr. (Walter de Gruyter GmbH, 2014-01)
      Emotional intelligence is one of the key research areas in human-computer interaction. This paper reports the development of an emotion recognition system using facial electromyogram (EMG) signals focusing the ambiguity ...

    • Wireless EEG signals based neuromarketing system using Fast Fourier Transform (FFT) 

      Murugappan, M., Dr.; Murugappan, Subbulakshmi; Balaganapathy; Gerard, Celestin (IEEE Conference Publications, 2014-03)
      his 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 ...