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Nazrul Anuar Nayan
Universiti Kebangsaan Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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The Effects of Nitrogen and Oxygen Atmosphere on the Photoconductivity of Trimethyl Phenyl Diamine Thin Films Nazrul Anuar Nayan; Khairul Anuar A. Rahman
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (617.473 KB) | DOI: 10.11591/eei.v7i2.950

Abstract

Organic materials were previously used as insulators in electrical technology. These materials, however, are currently used as conductors once their photoconductivity is confirmed and studied. From the literature, it has shown that the photoconductivity of trimethyl phenyl diamine (TPD) increases in the air and decreased in the atmosphere of the vacuum. To the best of our knowledge, there is no detailed study of the effects of gas in the air that affect TPD photoconductivity. In this study we investigate the effects of nitrogen (N2) and oxygen (O2) gases on photoconductivity, degradation and residual decay of photoconductivity for thin film TPD. The results of the study show that in the atmosphere of O2, TPD produces about seven times higher photoconductivity compared to N2 conditions. It also shows that, N2 and O2 provide more effective response time during photoconductivity residual decay. Photoconductivity degradation occurs in all conditions and its recovery takes more than 65 hours.
Evaluation of patient electrocardiogram datasets using signal quality indexing Nazrul Anuar Nayan; Hafifah Ab Hamid
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (838.106 KB) | DOI: 10.11591/eei.v8i2.1289

Abstract

Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved.
The Effects of Nitrogen and Oxygen Atmosphere on the Photoconductivity of Trimethyl Phenyl Diamine Thin Films Nazrul Anuar Nayan; Khairul Anuar A. Rahman
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (617.473 KB) | DOI: 10.11591/eei.v7i2.950

Abstract

Organic materials were previously used as insulators in electrical technology. These materials, however, are currently used as conductors once their photoconductivity is confirmed and studied. From the literature, it has shown that the photoconductivity of trimethyl phenyl diamine (TPD) increases in the air and decreased in the atmosphere of the vacuum. To the best of our knowledge, there is no detailed study of the effects of gas in the air that affect TPD photoconductivity. In this study we investigate the effects of nitrogen (N2) and oxygen (O2) gases on photoconductivity, degradation and residual decay of photoconductivity for thin film TPD. The results of the study show that in the atmosphere of O2, TPD produces about seven times higher photoconductivity compared to N2 conditions. It also shows that, N2 and O2 provide more effective response time during photoconductivity residual decay. Photoconductivity degradation occurs in all conditions and its recovery takes more than 65 hours.
Development of Respiratory Rate Estimation Technique Using Electrocardiogram and Photoplethysmogram for Continuous Health Monitoring Nazrul Anuar Nayan; Rosmina Jaafar; Nur Sabrina Risman
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (522.018 KB) | DOI: 10.11591/eei.v7i3.1244

Abstract

Abnormal vital signs often predict a serious condition of acutely ill hospital patients in 24 hours. The notable fluctuations of respiratory rate (RR) are highly predictive of deteriorations among the vital signs measured. Traditional methods of detecting RR are performed by directly measuring the air flow in or out of the lungs or indirectly measuring the changes of the chest volume. These methods require the use of cumbersome devices, which may interfere with natural breathing, are uncomfortable, have frequently moving artifacts, and are extremely expensive. This study aims to estimate the RR from electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which consist of passive and non-invasive acquisition modules. Algorithms have been validated by using PhysioNet’s Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II)’s patient datasets. RR estimation provides the value of mean absolute error (MAE) for ECG as 1.25 bpm (MIMIC-II) and 1.05 bpm for the acquired data. MAE for PPG is 1.15 bpm (MIMIC-II) and 0.90 bpm for the acquired data. By using 1-minute windows, this method reveals that the filtering method efficiently extracted respiratory information from the ECG and PPG signals. Smaller MAE for PPG signals results from fewer artifacts due to easy sensor attachment for the PPG because PPG recording requires only one-finger pulse oximeter sensor placement. However, ECG recording requires at least three electrode placements at three positions on the subject’s body surface for a single lead (lead II), thereby increasing the artifacts. A reliable technique has been proposed for RR estimation.
Evaluation of patient electrocardiogram datasets using signal quality indexing Nazrul Anuar Nayan; Hafifah Ab Hamid
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (838.106 KB) | DOI: 10.11591/eei.v8i2.1289

Abstract

Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved.
Development of Respiratory Rate Estimation Technique Using Electrocardiogram and Photoplethysmogram for Continuous Health Monitoring Nazrul Anuar Nayan; Rosmina Jaafar; Nur Sabrina Risman
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (522.018 KB) | DOI: 10.11591/eei.v7i3.1244

Abstract

Abnormal vital signs often predict a serious condition of acutely ill hospital patients in 24 hours. The notable fluctuations of respiratory rate (RR) are highly predictive of deteriorations among the vital signs measured. Traditional methods of detecting RR are performed by directly measuring the air flow in or out of the lungs or indirectly measuring the changes of the chest volume. These methods require the use of cumbersome devices, which may interfere with natural breathing, are uncomfortable, have frequently moving artifacts, and are extremely expensive. This study aims to estimate the RR from electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which consist of passive and non-invasive acquisition modules. Algorithms have been validated by using PhysioNet’s Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II)’s patient datasets. RR estimation provides the value of mean absolute error (MAE) for ECG as 1.25 bpm (MIMIC-II) and 1.05 bpm for the acquired data. MAE for PPG is 1.15 bpm (MIMIC-II) and 0.90 bpm for the acquired data. By using 1-minute windows, this method reveals that the filtering method efficiently extracted respiratory information from the ECG and PPG signals. Smaller MAE for PPG signals results from fewer artifacts due to easy sensor attachment for the PPG because PPG recording requires only one-finger pulse oximeter sensor placement. However, ECG recording requires at least three electrode placements at three positions on the subject’s body surface for a single lead (lead II), thereby increasing the artifacts. A reliable technique has been proposed for RR estimation.
The Effects of Nitrogen and Oxygen Atmosphere on the Photoconductivity of Trimethyl Phenyl Diamine Thin Films Nazrul Anuar Nayan; Khairul Anuar A. Rahman
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (617.473 KB) | DOI: 10.11591/eei.v7i2.950

Abstract

Organic materials were previously used as insulators in electrical technology. These materials, however, are currently used as conductors once their photoconductivity is confirmed and studied. From the literature, it has shown that the photoconductivity of trimethyl phenyl diamine (TPD) increases in the air and decreased in the atmosphere of the vacuum. To the best of our knowledge, there is no detailed study of the effects of gas in the air that affect TPD photoconductivity. In this study we investigate the effects of nitrogen (N2) and oxygen (O2) gases on photoconductivity, degradation and residual decay of photoconductivity for thin film TPD. The results of the study show that in the atmosphere of O2, TPD produces about seven times higher photoconductivity compared to N2 conditions. It also shows that, N2 and O2 provide more effective response time during photoconductivity residual decay. Photoconductivity degradation occurs in all conditions and its recovery takes more than 65 hours.
Development of Respiratory Rate Estimation Technique Using Electrocardiogram and Photoplethysmogram for Continuous Health Monitoring Nazrul Anuar Nayan; Rosmina Jaafar; Nur Sabrina Risman
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (522.018 KB) | DOI: 10.11591/eei.v7i3.1244

Abstract

Abnormal vital signs often predict a serious condition of acutely ill hospital patients in 24 hours. The notable fluctuations of respiratory rate (RR) are highly predictive of deteriorations among the vital signs measured. Traditional methods of detecting RR are performed by directly measuring the air flow in or out of the lungs or indirectly measuring the changes of the chest volume. These methods require the use of cumbersome devices, which may interfere with natural breathing, are uncomfortable, have frequently moving artifacts, and are extremely expensive. This study aims to estimate the RR from electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which consist of passive and non-invasive acquisition modules. Algorithms have been validated by using PhysioNet’s Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II)’s patient datasets. RR estimation provides the value of mean absolute error (MAE) for ECG as 1.25 bpm (MIMIC-II) and 1.05 bpm for the acquired data. MAE for PPG is 1.15 bpm (MIMIC-II) and 0.90 bpm for the acquired data. By using 1-minute windows, this method reveals that the filtering method efficiently extracted respiratory information from the ECG and PPG signals. Smaller MAE for PPG signals results from fewer artifacts due to easy sensor attachment for the PPG because PPG recording requires only one-finger pulse oximeter sensor placement. However, ECG recording requires at least three electrode placements at three positions on the subject’s body surface for a single lead (lead II), thereby increasing the artifacts. A reliable technique has been proposed for RR estimation.
Evaluation of patient electrocardiogram datasets using signal quality indexing Nazrul Anuar Nayan; Hafifah Ab Hamid
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (838.106 KB) | DOI: 10.11591/eei.v8i2.1289

Abstract

Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved.
Co-Authors Hafifah Ab Hamid Khairul Anuar A. Rahman Nur Sabrina Risman Rosmina Jaafar