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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
K. A. Sidek
International Islamic University Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Distinctive features for normal and crackles respiratory sounds using cepstral coefficients N. H. Mohd Johari; Noreha Abdul Malik; K. A. Sidek
Bulletin of Electrical Engineering and Informatics Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.387 KB) | DOI: 10.11591/eei.v8i3.1517

Abstract

Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.
Distinctive features for normal and crackles respiratory sounds using cepstral coefficients N. H. Mohd Johari; Noreha Abdul Malik; K. A. Sidek
Bulletin of Electrical Engineering and Informatics Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.387 KB) | DOI: 10.11591/eei.v8i3.1517

Abstract

Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.
Distinctive features for normal and crackles respiratory sounds using cepstral coefficients N. H. Mohd Johari; Noreha Abdul Malik; K. A. Sidek
Bulletin of Electrical Engineering and Informatics Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.387 KB) | DOI: 10.11591/eei.v8i3.1517

Abstract

Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.
Electrocardiogram (ECG) based stress recognition integrated with different classification of age and gender N. S. Nor Shahrudin; K. A. Sidek; A. Z. Jusoh
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 1: July 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i1.pp199-210

Abstract

Good mental health is important in our daily life. A person commonly finds stress as a barrier to enhance an individual’s performance. Be reminded that not all people have the same level of stress because different people have dissimilar problems in their life. In addition, different level of age and gender will affect unequal amount of stress. Electrocardiogram (ECG) signal is an electrical indicator of the heart that can detect changes of human response which relates to our emotions and reactions. Thus, this research proposed a non-intrusive detector to identify stress level for both gender and different classification of age using the ECG. A total of 30 healthy subjects were involved during the data acquisition stage. Data acquisition which initialize ECG data were divided into two conditions; which are normal and stress states. ECG data for normal state only need the participant to breath in and out normally. In other hand, the participants also need to undergo Stroop Colour word test as a stress inducer to represent ECG in stress state. Then, Sgolay filter was selected in the pre-processing stage to remove artifacts in the signal. The process was followed by feature extraction of the ECG signal and finally classified using RR Interval (RRI), different amplitudes of R peaks and Cardioid graph were used to evaluate the performance of the proposed technique. As a result, Class 5 (age range between 50-59 years old) marks the highest changes of stress level rather than other classes, while women are more affected by stress rather than men by showing tremendous percentage changes between normal and stress level over the proposed classifiers. The result proves that ECG signals can be used as an alternative mechanism to recognize stress more efficiently with the integration of gender and age variabilities.
Portable health monitoring kit using photolethysmogram signal N. F. A. Jamal; K. A. Sidek
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp638-649

Abstract

This study investigates the feasibility of photoplethysmogram (PPG) signals in monitoring health condition and designing a portable health monitoring kit which is suitable for home use. The aim of this study is to ease people in monitoring their health continuously without the need to go to the hospital which can save a lot of time. The focus of this study is to find heart rate and blood pressure recording. The type of PPG sensor used in this project is a non-invasive PPG which measures the blood volume changes in any part of the body. A total of 16 subjects consisting of male and female with age range of 20 to 60 years old were involved in this research. The heart rate and blood pressure for each subject were acquired and analyzed. Based on the result, it shows that higher heart rate reading is associated with female and younger age groups. Meanwhile, for blood pressure value, male subjects tend to have higher blood pressure as compared to female subjects. However, there is no specific pattern for blood pressure in terms of the age group. In the case of HRV analysis based on Kubios software, the SDNN value is higher for male and older age subjects. Meanwhile, the RMSSD value is lower for male and older age subjects. Therefore, PPG signal has the capability to monitor the health status of an individual, which acts as an alternative biological signal for the existing health monitoring systems.
Co-Authors A. Z. Jusoh N. F. A. Jamal N. H. Mohd Johari N. S. Nor Shahrudin Noreha Abdul Malik