Displaying publications 1 - 20 of 29 in total

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  1. Gan KB, Yahyavi ES, Ismail MS
    Technol Health Care, 2016 Sep 14;24(5):761-8.
    PMID: 27163300 DOI: 10.3233/THC-161161
    BACKGROUND: At the emergency triage center, assessment of the present of the danger signs and measurement of vital signs are measured according to the guidelines. The respiration rate is still posing a challenge to the doctor as it is impractical to use conventional devices. Attaching measurement devices to the patient will induce artificial measurements (self-awareness stress effects) besides being time-consuming. Currently, the medical officers visually count the number of times the chest movement in a minute, sometimes poses cultural challenges especially for female patients.

    OBJECTIVE: The main objective of this paper is to develop a robust algorithm to extract respiration rate using the contactless displacement sensor.

    METHODS: In this study, chest movements were used as an indicative of inspiration and expiration to measure respiratory rate using the contactless displacement sensor. The contactless optical signals were recorded from 32 healthy subjects in four different controlled breathing conditions: rest, coughing, talking and hand movement to obtain the motion artifacts that the patients may have in the emergency department. The Empirical mode decomposition (EMD) algorithm was used to derive continuous RR signal from the contactless optical signal.

    RESULTS: The analysis showed that there is a good correlation (0.9702) with RMSE of 0.33 breaths per minutes between the contact respiration rate and contactless respiration rate using empirical mode decomposition method.

    CONCLUSION: It can be concluded that the empirical mode decomposition method can extract the respiration rate of the contactless optical signal from chest movement.

  2. Ali MA, Sundaraj K, Ahmad RB, Ahamed NU, Islam MA, Sundaraj S
    Technol Health Care, 2014;22(4):617-25.
    PMID: 24990168 DOI: 10.3233/THC-140833
    Normally, surface electromyography electrodes are used to evaluate the activity of superficial muscles during various kinds of voluntary contractions of muscle fiber. The objective of the present study was to investigate the effect of repetitive isometric contractions on the three heads of the triceps brachii muscle during handgrip force exercise.
  3. Lua PL, Neni WS, Lee JK, Abd Aziz Z
    Technol Health Care, 2013;21(6):547-56.
    PMID: 24284547 DOI: 10.3233/THC-130758
    Being well-informed and knowledgeable about their illnesses would be a great advantage to children with epilepsy (CWE). Subsequently, an effective education programme which could secure interest and simultaneously improve their awareness, knowledge and attitudes (AKA) is essential in enhancing well-being and health outcomes.
  4. Ambusam S, Omar B, Joseph L, Meng SP, Padzil FA
    Technol Health Care, 2015;23(5):691-7.
    PMID: 26410131 DOI: 10.3233/THC-151015
    The reliability of a triaxial accelerometer in measuring the head excursion during typing task among occupational typists has not been reported so far.
  5. Samsudin WS, Sundaraj K, Ahmad A, Salleh H
    Technol Health Care, 2016 Mar 14;24(2):287-94.
    PMID: 26578273 DOI: 10.3233/THC-151103
    An initial assessment method that can classify as well as categorize the severity of paralysis into one of six levels according to the House-Brackmann (HB) system based on facial landmarks motion using an Optical Flow (OF) algorithm is proposed. The desired landmarks were obtained from the video recordings of 5 normal and 3 Bell's Palsy subjects and tracked using the Kanade-Lucas-Tomasi (KLT) method. A new scoring system based on the motion analysis using area measurement is proposed. This scoring system uses the individual scores from the facial exercises and grades the paralysis based on the HB system. The proposed method has obtained promising results and may play a pivotal role towards improved rehabilitation programs for patients.
  6. Azeez D, Gan KB, Mohd Ali MA, Ismail MS
    Technol Health Care, 2015;23(4):419-28.
    PMID: 25791174 DOI: 10.3233/THC-150907
    BACKGROUND: Triage of patients in the emergency department is a complex task based on several uncertainties and ambiguous information. Triage must be implemented within two to five minutes to avoid potential fatality and increased waiting time.
    OBJECTIVE: An intelligent triage system has been proposed for use in a triage environment to reduce human error.
    METHODS: This system was developed based on the objective primary triage scale (OPTS) that is currently used in the Universiti Kebangsaan Malaysia Medical Center. Both primary and secondary triage models are required to develop this system. The primary triage model has been reported previously; this work focused on secondary triage modelling using an ensemble random forest technique. The randomized resampling method was proposed to balance the data unbalance prior to model development.
    RESULTS: The results showed that the 300% resampling gave a low out-of-bag error of 0.02 compared to 0.37 without pre-processing. This model has a sensitivity and specificity of 0.98 and 0.89, respectively, for the unseen data.
    CONCLUSION: With this combination, the random forest reduces the variance, and the randomized resembling reduces the bias, leading to the reduced out-of-bag error.
    KEYWORDS: Decision support system; emergency department; random forest; randomized resampling
  7. Namazi H, Aghasian E, Ala TS
    Technol Health Care, 2020;28(1):57-66.
    PMID: 31104032 DOI: 10.3233/THC-181579
    Analysis of human brain activity is an important topic in human neuroscience. Human brain activity can be studied by analyzing the electroencephalography (EEG) signal. In this way, scientists have employed several techniques that investigate nonlinear dynamics of EEG signals. Fractal theory as a promising technique has shown its capabilities to analyze the nonlinear dynamics of time series. Since EEG signals have fractal patterns, in this research we analyze the variations of fractal dynamics of EEG signals between four datasets that were collected from healthy subjects with open-eyes and close-eyes conditions, patients with epilepsy who did and patients who did not face seizures. The obtained results showed that EEG signal during seizure has greatest complexity and the EEG signal during the seizure-free interval has lowest complexity. In order to verify the obtained results in case of fractal analysis, we employ approximate entropy, which indicates the randomness of time series. The obtained results in case of approximate entropy certified the fractal analysis results. The obtained results in this research show the effectiveness of fractal theory to investigate the nonlinear structure of EEG signal between different conditions.
  8. Pakniyat N, Babini MH, Kulish VV, Namazi H
    Technol Health Care, 2021 Aug 13.
    PMID: 34397441 DOI: 10.3233/THC-213136
    BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart's activity, a relationship should exist among their activities.

    OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis.

    METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18-22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked.

    RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566).

    CONCLUSION: We conclude that heart and brain activities are related.

  9. Kamal SM, Dawi NBM, Sim S, Tee R, Nathan V, Aghasian E, et al.
    Technol Health Care, 2020;28(6):675-684.
    PMID: 32200366 DOI: 10.3233/THC-192034
    BACKGROUND: Walking is one of the important actions of the human body. For this purpose, the human brain communicates with leg muscles through the nervous system. Based on the walking path, leg muscles act differently. Therefore, there should be a relation between the activity of leg muscles and the path of movement.

    OBJECTIVE: In order to address this issue, we analyzed how leg muscle activity is related to the variations of the path of movement.

    METHOD: Since the electromyography (EMG) signal is a feature of muscle activity and the movement path has complex structures, we used entropy analysis in order to link their structures. The Shannon entropy of EMG signal and walking path are computed to relate their information content.

    RESULTS: Based on the obtained results, walking on a path with greater information content causes greater information content in the EMG signal which is supported by statistical analysis results. This allowed us to analyze the relation between muscle activity and walking path.

    CONCLUSION: The method of analysis employed in this research can be applied to investigate the relation between brain or heart reactions and walking path.

  10. Kamal SM, Sim S, Tee R, Nathan V, Aghasian E, Namazi H
    Technol Health Care, 2020;28(4):381-390.
    PMID: 31796717 DOI: 10.3233/THC-191965
    BACKGROUND: The human brain controls all actions of the body. Walking is one of the most important actions that deals with the movement of the body. In fact, the brain controls and regulates human walking based on different conditions. One of the conditions that affects human walking is the complexity of path of movement. Therefore, the brain activity should change when a person walks on a path with different complexities.

    OBJECTIVE: In this research we benefit from fractal analysis to study the effect of complexity of path of movement on the complexity of human brain reaction.

    METHODS: For this purpose we calculate the fractal dimension of the electroencephalography (EEG) signal when subjects walk on different paths with different fractal dimensions (complexity).

    RESULTS: The results of the analysis show that the complexity of brain activity increases with the increment of complexity of path of movement.

    CONCLUSION: The method of analysis employed in this research can also be employed to analyse the reaction of the human heart and respiration when subjects move on paths with different complexities.

  11. Chia KP, Li OK, Yuong TS, Singh OP, Faudzi AABM, Sornambikai S, et al.
    Technol Health Care, 2021;29(4):829-836.
    PMID: 33492252 DOI: 10.3233/THC-202414
    BACKGROUND: Force Monitoring Devices (FMDs) reported in the literature to monitor applied force during Joint Mobilization Technique (JMT) possess complex design/bulky which alters the execution of treatment, has poor accuracy and is unable to feel the resistance provided by soft tissues limits its usage in the clinical settings.

    OBJECTIVES: This study aims to develop a highly accurate, portable FMD and to demonstrate real-time monitoring of force applied by health professionals during JMT without altering its execution.

    METHODS: The FMD was constructed using the FlexiForce sensor, potential divider, ATmega 328 microcontroller, custom-written software, and liquid crystal display. The calibration, accuracy, and cyclic repeatability of the FMD were tested from 0 to 90 N applied load with a gold standard universal testing machine. For practical demonstration, the FMD was tested for monitoring applied force by a physiotherapist while performing Maitland's grade I to IV over the 6th cervical vertebra among 30 healthy subjects.

    RESULTS: The obtained Bland-Altman plot limits agreement for accuracy, and cyclic repeatability was -1.57 N to 1.22 N, and -1.26 N to 1.26 N, respectively with standard deviation and standard error of the mean values of 3.77% and 0.73% and 2.15% and 0.23%, respectively. The test-retest reliability of the FMD tested by the same researcher at an interval of one week showed an excellent intra-class correlation coefficient of r= 1.00. The obtained force readings for grade I to IV among 30 subjects ranged from 10.33 N to 45.24 N.

    CONCLUSIONS: Appreciable performance of the developed FMD suggested that it may be useful to monitor force applied by clinicians during JMT among neck pain subjects and is a useful educational tool for academicians to teach mobilization skills.

  12. El-Badawy IM, Singh OP, Omar Z
    Technol Health Care, 2021;29(1):59-72.
    PMID: 32716337 DOI: 10.3233/THC-202198
    BACKGROUND: The quantitative features of a capnogram signal are important clinical metrics in assessing pulmonary function. However, these features should be quantified from the regular (artefact-free) segments of the capnogram waveform.

    OBJECTIVE: This paper presents a machine learning-based approach for the automatic classification of regular and irregular capnogram segments.

    METHODS: Herein, we proposed four time- and two frequency-domain features experimented with the support vector machine classifier through ten-fold cross-validation. MATLAB simulation was conducted on 100 regular and 100 irregular 15 s capnogram segments. Analysis of variance was performed to investigate the significance of the proposed features. Pearson's correlation was utilized to select the relatively most substantial ones, namely variance and the area under normalized magnitude spectrum. Classification performance, using these features, was evaluated against two feature sets in which either time- or frequency-domain features only were employed.

    RESULTS: Results showed a classification accuracy of 86.5%, which outperformed the other cases by an average of 5.5%. The achieved specificity, sensitivity, and precision were 84%, 89% and 86.51%, respectively. The average execution time for feature extraction and classification per segment is only 36 ms.

    CONCLUSION: The proposed approach can be integrated with capnography devices for real-time capnogram-based respiratory assessment. However, further research is recommended to enhance the classification performance.

  13. Soundirarajan M, Pakniyat N, Sim S, Nathan V, Namazi H
    Technol Health Care, 2021;29(1):99-109.
    PMID: 32568131 DOI: 10.3233/THC-192085
    BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles.

    OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain.

    METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content).

    RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals.

    CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.

  14. Pakniyat N, Namazi H
    Technol Health Care, 2021 Sep 17.
    PMID: 34542048 DOI: 10.3233/THC-213052
    BACKGROUND: The analysis of brain activity in different conditions is an important research area in neuroscience.

    OBJECTIVE: This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals.

    METHODS: We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents.

    RESULTS: The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions.

    CONCLUSION: This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.

  15. Ahmad HAB, El-Badawy IM, Singh OP, Hisham RB, Malarvili MB
    Technol Health Care, 2018;26(4):573-579.
    PMID: 29758955 DOI: 10.3233/THC-171067
    BACKGROUND: Fetal heart rate (FHR) monitoring device is highly demanded to assess the fetus health condition in home environments. Conventional standard devices such as ultrasonography and cardiotocography are expensive, bulky and uncomfortable and consequently not suitable for long-term monitoring. Herein, we report a device that can be used to measure fetal heart rate in clinical and home environments.

    METHODS: The proposed device measures and displays the FHR on a screen liquid crystal display (LCD). The device consists of hardware that comprises condenser microphone sensor, signal conditioning, microcontroller and LCD, and software that involves the algorithm used for processing the conditioned fetal heart signal prior to FHR display. The device's performance is validated based on analysis of variance (ANOVA) test.

    RESULTS: FHR data was recorded from 22 pregnant women during the 17th to 37th week of gestation using the developed device and two standard devices; AngelSounds and Electronic Stethoscope. The results show that F-value (1.5) is less than F𝑐𝑟𝑖𝑡, (3.1) and p-value (p> 0.05). Accordingly, there is no significant difference between the mean readings of the developed and existing devices. Hence, the developed device can be used for monitoring FHR in clinical and home environments.

  16. Ahmad RF, Malik AS, Kamel N, Reza F, Amin HU, Hussain M
    Technol Health Care, 2017;25(3):471-485.
    PMID: 27935575 DOI: 10.3233/THC-161286
    BACKGROUND: Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful.

    METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes.

    RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature.

    CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

  17. Singh OP, Ahmed IB, Malarvili MB
    Technol Health Care, 2018;26(5):785-794.
    PMID: 30124456 DOI: 10.3233/THC-181288
    BACKGROUND: Assessment of asthma outside of the hospital using a patient independent device is highly in demand due to the limitation of existing devices, which are manual and unreliable if patients are not cooperative.

    OBJECTIVE: The study aims to verify the use of newly developed human respiration, carbon dioxide (CO2) measurement device for the management of asthma outside of the hospital.

    METHOD: The data were collected from 60 subjects aged between 18-35 years via convenience sampling method reported in UTM Health Center using the device. Furthermore, the data were normalized and analyzed using descriptive statistics, t-test, and area (Az) under receiver operating characteristic curve (ROC).

    RESULT: Findings revealed that the normalized mean values of end-tidal carbon dioxide (EtCO2), Hjorth Activity (HA), and respiratory rate (RR) were lower in asthmatic compared with healthy subjects with minimum deviation from the mean. In addition, each parameter was found to significantly differ statistically for asthma and non-asthma with p< 0.05. Furthermore, the Az shows the strong association for the screening of asthma and non-asthma with an average of 0.71 (95% CI: 0.57-0.83), 0.77 (95% CI: 0.64-0.90), and 0.83 (95% CI: 0.73-0.94) for RR, EtCO2, and HA, respectively.

    CONCLUSIONS: This study demonstrates that the newly developed handheld human respiration CO2 measurement device may possibly be used as an effort-independent asthma management method outside of the hospital.
  18. Namazi H, Aghasian E, Ala TS
    Technol Health Care, 2019;27(3):233-241.
    PMID: 30829625 DOI: 10.3233/THC-181497
    Brain activity analysis is an important research area in the field of human neuroscience. Moreover, a subcategory in this field is the classification of brain activity in terms of different brain disorders. Since the Electroencephalography (EEG) signal is, in fact, a non-linear time series, employing techniques to investigate its non-linear structure is rather crucial. In this study, we evaluate the non-linear structure of the EEG signal between healthy and schizophrenic adolescents using fractal theory. The results of our analysis revealed that in terms of all recording channels, the EEG signal of healthy subjects is more complex compared to the ones suffering from schizophrenia. The statistical analysis also indicated that there is a significant difference in the complex structure of the EEG signal between these two groups of subjects. We also utilized approximate entropy in our analysis in order to verify the obtained results of the fractal analysis. The result of the entropy analysis suggested that EEG signal for healthy subjects is less random compared to the EEG signal in schizophrenic individuals. In addition, the employed methodology in this research can be further investigated in order to classify the brain activity in terms of other brain disorders, where one can explore how the complex structure of the EEG signal alters between them.
  19. Ahamed MRA, Babini MH, Namazi H
    Technol Health Care, 2020 Mar 13.
    PMID: 32200368 DOI: 10.3233/THC-192105
    BACKGROUND: The human voice is the main feature of human communication. It is known that the brain controls the human voice. Therefore, there should be a relation between the characteristics of voice and brain activity.

    OBJECTIVE: In this research, electroencephalography (EEG) as the feature of brain activity and voice signals were simultaneously analyzed.

    METHOD: For this purpose, we changed the activity of the human brain by applying different odours and simultaneously recorded their voices and EEG signals while they read a text. For the analysis, we used the fractal theory that deals with the complexity of objects. The fractal dimension of EEG signal versus voice signal in different levels of brain activity were computed and analyzed.

    RESULTS: The results indicate that the activity of human voice is related to brain activity, where the variations of the complexity of EEG signal are linked to the variations of the complexity of voice signal. In addition, the EEG and voice signal complexities are related to the molecular complexity of applied odours.

    CONCLUSION: The employed method of analysis in this research can be widely applied to other physiological signals in order to relate the activities of different organs of human such as the heart to the activity of his brain.

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