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Habib MA, Ibrahim F, Mohktar MS, Kamaruzzaman SB, Lim KS. Recursive independent component analysis (ICA)-decomposition of ictal EEG to select the best ictal component for EEG source imaging. Clin Neurophysiol 2020; 131:642-654. [DOI: 10.1016/j.clinph.2019.11.058] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 11/25/2019] [Accepted: 11/30/2019] [Indexed: 11/28/2022]
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Subermaniam K, Welfred R, Subramanian P, Chinna K, Ibrahim F, Mohktar MS, Tan MP. The Effectiveness of a Wireless Modular Bed Absence Sensor Device for Fall Prevention among Older Inpatients. Front Public Health 2017; 4:292. [PMID: 28119908 PMCID: PMC5220104 DOI: 10.3389/fpubh.2016.00292] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 12/21/2016] [Indexed: 11/17/2022] Open
Abstract
Background Falls and fall-related injuries are increasingly serious issues among elderly inpatients due to population aging. The bed-exit alarm has only previously been evaluated in a handful of studies with mixed results. Therefore, we evaluated the effectiveness of a modular bed absence sensor device (M-BAS) in detecting bed exits among older inpatients in a middle income nation in East Asia. Methods Patients aged ≥65 years on an acute geriatric ward who were able to mobilize with or without walking aids and physical assistance were recruited to the study. The total number of alarms and the numbers of true and false alarms were recorded by ward nurses. The M-BAS device is placed across the mattress of all consenting participants. Nurses’ workload was assessed using the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) score, while nurses’ perceptions were surveyed. Results The sensitivity of the M-BAS was 100% with a positive predictive value of 68% and a nuisance alarm rate of 31%. There was a significant reduction in total NASA-TLX workload score (mean difference = 14.34 ± 13.96 SD, p < 0.001) at the end of the intervention period. 83% of the nurses found the device useful for falls prevention, 97% found it user friendly, and 87% would use it in future. Conclusion The M-BAS was able to accurately detect bed absence episodes among geriatric inpatients and alert nurses accordingly. The use of the device significantly reduced the total workload score, while the acceptability of the device was high among our nurses. A larger, cluster randomized study to measure actual falls outcome associated with the use of the device is now indicated.
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Affiliation(s)
- Kogilavani Subermaniam
- Anatomy and Physiology Unit, Allied Health Science College, Ministry of Health Malaysia , Sungai Buloh , Malaysia
| | - Ridgwan Welfred
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Pathmawathi Subramanian
- Department of Nursing Science of Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Ageing and Age-Associated Disorders Research Group, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Karuthan Chinna
- Social and Preventive Medicine of Faculty of Medicine, University of Malaya , Kuala Lumpur , Malaysia
| | - Fatimah Ibrahim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Mas S Mohktar
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Maw Pin Tan
- Ageing and Age-Associated Disorders Research Group, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Division of Geriatric Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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Khalil SF, Mohktar MS, Ibrahim F. Bioimpedance Vector Analysis in Diagnosing Severe and Non-Severe Dengue Patients. Sensors (Basel) 2016; 16:s16060911. [PMID: 27322285 PMCID: PMC4934337 DOI: 10.3390/s16060911] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Revised: 05/17/2016] [Accepted: 05/18/2016] [Indexed: 12/31/2022]
Abstract
Real-time monitoring and precise diagnosis of the severity of Dengue infection is needed for better decisions in disease management. The aim of this study is to use the Bioimpedance Vector Analysis (BIVA) method to differentiate between healthy subjects and severe and non-severe Dengue-infected patients. Bioimpedance was measured using a 50 KHz single-frequency bioimpedance analyzer. Data from 299 healthy subjects (124 males and 175 females) and 205 serologically confirmed Dengue patients (123 males and 82 females) were analyzed in this study. The obtained results show that the BIVA method was able to assess and classify the body fluid and cell mass condition between the healthy subjects and the Dengue-infected patients. The bioimpedance mean vectors (95% confidence ellipse) for healthy subjects, severe and non-severe Dengue-infected patients were illustrated. The vector is significantly shortened from healthy subjects to Dengue patients; for both genders the p-value is less than 0.0001. The mean vector of severe Dengue patients is significantly shortened compare to non-severe patients with a p-value of 0.0037 and 0.0023 for males and females, respectively. This study confirms that the BIVA method is a valid method in differentiating the healthy, severe and non-severe Dengue-infected subjects. All tests performed had a significance level with a p-value less than 0.05.
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Affiliation(s)
- Sami F Khalil
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Department of Biomedical Engineering, College of Engineering, Sudan University of Science and Technology, 407 Khartoum, Sudan.
| | - Mas S Mohktar
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Fatimah Ibrahim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
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Al-Faqheri W, Ibrahim F, Thio THG, Joseph K, Mohktar MS, Madou M. Liquid density effect on burst frequency in centrifugal microfluidic platforms. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:3221-4. [PMID: 26736978 DOI: 10.1109/embc.2015.7319078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Centrifugal microfluidic platforms are widely used in various advanced processes such as biomedical diagnostics, chemical analysis and drug screening. This paper investigates the effect of liquid density on the burst frequency of the centrifugal microfluidic platform. This effect is experimentally investigated and compared to theoretical values. It is found that increasing the liquid density results in lower burst frequency and it is in agreement with theoretical calculations. Moreover, in this study we proposed the use of the microfluidic CD platform as an inexpensive and simple sensor for liquid density measurements. The proposed liquid sensor requires much less liquid volume (in the range of microliters) compared to conventional density meters. This study presents fundamental work which allows for future advance studies with the aim of designing and fabricating centrifugal microfluidic platforms for more complex tasks such as blood analysis.
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Habib MA, Ibrahim F, Mohktar MS, Kamaruzzaman SB, Rahmat K, Lim KS. Ictal EEG Source Imaging for Presurgical Evaluation of Refractory Focal Epilepsy. World Neurosurg 2015; 88:576-585. [PMID: 26548833 DOI: 10.1016/j.wneu.2015.10.096] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Revised: 10/25/2015] [Accepted: 10/26/2015] [Indexed: 11/25/2022]
Abstract
BACKGROUND Electroencephalography source imaging (ESI) is a promising tool for localizing the cortical sources of both ictal and interictal epileptic activities. Many studies have shown the clinical usefulness of interictal ESI, but very few have investigated the utility of ictal ESI. The aim of this article is to examine the clinical usefulness of ictal ESI for epileptic focus localization in patients with refractory focal epilepsy, especially extratemporal lobe epilepsy. METHODS Both ictal and interictal ESI were performed by the use of patient-specific realistic forward models and 3 different linear distributed inverse models. Lateralization as well as concordance between ESI-estimated focuses and single-photon emission computed tomography (SPECT) focuses were assessed. RESULTS All the ESI focuses (both ictal and interictal) were found lateralized to the same hemisphere as ictal SPECT focuses. Lateralization results also were in agreement with the lesion sides as visualized on magnetic resonance imaging. Ictal ESI results, obtained from the best-performing inverse model, were fully concordant with the same cortical lobe as SPECT focuses, whereas the corresponding concordance rate is 87.50% in case of interictal ESI. CONCLUSIONS Our findings show that ictal ESI gives fully lateralized and highly concordant results with ictal SPECT and may provide a cost-effective substitute for ictal SPECT.
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Affiliation(s)
- Mohammad Ashfak Habib
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; Department of Computer Science & Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
| | - Fatimah Ibrahim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
| | - Mas S Mohktar
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Shahrul Bahyah Kamaruzzaman
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kartini Rahmat
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kheng Seang Lim
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia; Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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Mohktar MS, Sukor JA, Redmond SJ, Basilakis J, Lovell NH. Effect of Home Telehealth Data Quality on Decision Support System Performance. Procedia Computer Science 2015; 64:352-359. [DOI: 10.1016/j.procs.2015.08.499] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Mohktar MS, Redmond SJ, Antoniades NC, Rochford PD, Pretto JJ, Basilakis J, Lovell NH, McDonald CF. Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data. Artif Intell Med 2014; 63:51-9. [PMID: 25704112 DOI: 10.1016/j.artmed.2014.12.003] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2011] [Revised: 12/02/2014] [Accepted: 12/04/2014] [Indexed: 10/24/2022]
Abstract
BACKGROUND The use of telehealth technologies to remotely monitor patients suffering chronic diseases may enable preemptive treatment of worsening health conditions before a significant deterioration in the subject's health status occurs, requiring hospital admission. OBJECTIVE The objective of this study was to develop and validate a classification algorithm for the early identification of patients, with a background of chronic obstructive pulmonary disease (COPD), who appear to be at high risk of an imminent exacerbation event. The algorithm attempts to predict the patient's condition one day in advance, based on a comparison of their current physiological measurements against the distribution of their measurements over the previous month. METHOD The proposed algorithm, which uses a classification and regression tree (CART), has been validated using telehealth measurement data recorded from patients with moderate/severe COPD living at home. The data were collected from February 2007 to January 2008, using a telehealth home monitoring unit. RESULTS The CART algorithm can classify home telehealth measurement data into either a 'low risk' or 'high risk' category with 71.8% accuracy, 80.4% specificity and 61.1% sensitivity. The algorithm was able to detect a 'high risk' condition one day prior to patients actually being observed as having a worsening in their COPD condition, as defined by symptom and medication records. CONCLUSION The CART analyses have shown that features extracted from three types of physiological measurements; forced expiratory volume in 1s (FEV1), arterial oxygen saturation (SPO2) and weight have the most predictive power in stratifying the patients condition. This CART algorithm for early detection could trigger the initiation of timely treatment, thereby potentially reducing exacerbation severity and recovery time and improving the patient's health. This study highlights the potential usefulness of automated analysis of home telehealth data in the early detection of exacerbation events among COPD patients.
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Affiliation(s)
- Mas S Mohktar
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.
| | - Stephen J Redmond
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
| | - Nick C Antoniades
- Institute for Breathing and Sleep, Department of Respiratory Medicine, Austin Health, Heidelberg, Victoria 3081, Australia.
| | - Peter D Rochford
- Institute for Breathing and Sleep, Department of Respiratory Medicine, Austin Health, Heidelberg, Victoria 3081, Australia.
| | - Jeffrey J Pretto
- Department of Respiratory Medicine, John Hunter Hospital, Newcastle 2305, Australia.
| | - Jim Basilakis
- School of Computing and Mathematics, University of Western Sydney, Sydney, NSW 2751, Australia.
| | - Nigel H Lovell
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
| | - Christine F McDonald
- Institute for Breathing and Sleep, Department of Respiratory Medicine, Austin Health, Heidelberg, Victoria 3081, Australia.
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Abd Sukor J, Mohktar MS, Redmond SJ, Lovell NH. Signal quality measures on pulse oximetry and blood pressure signals acquired from self-measurement in a home environment. IEEE J Biomed Health Inform 2014; 19:102-8. [PMID: 25312963 DOI: 10.1109/jbhi.2014.2361654] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Recently, decision support system (DSSs) have become more widely accepted as a support tool for use with telehealth systems, helping clinicians to summarize and digest what would otherwise be an unmanageable volume of data. One of the pillars of a home telehealth system is the performance of unsupervised physiological self-measurement by patients in their own homes. Such measurements are prone to error and noise artifact, often due to poor measurement technique and ignorance of the measurement and transduction principles at work. These errors can degrade the quality of the recorded signals and ultimately degrade the performance of the DSS system, which is aiding the clinician in their management of the patient. Developed algorithms for automated quality assessment for pulse oximetry and blood pressure (BP) signals were tested retrospectively with data acquired from a trial that recorded signals in a home environment. The trial involved four aged subjects who performed pulse oximetry and BP measurements by themselves at their home for ten days, three times per day. This trial was set up to mimic the unsupervised physiological self-measurement as in a telehealth system. A manually annotated "gold standard" (GS) was used as the reference against which the developed algorithms were evaluated after analyzing the recordings. The assessment of pulse oximetry signals shows 95% of good sections and 67% of noisy sections were correctly detected by the developed algorithm, and a Cohen's Kappa coefficient (κ) of 0.58 was obtained in 120 pooled signals. The BP measurement evaluation demonstrates that 75% of the actual noisy sections were correctly classified in 120 pooled signals, with 97% and 91% of the signals correctly identified as worthy of attempting systolic and/or diastolic pressure estimation, respectively, with a mean error and standard deviation of 2.53±4.20 mmHg and 1.46±5.29 mmHg when compared to a manually annotated GS. These results demonstrate the feasibility, and highlight the potential benefit, of incorporating automated signal quality assessment algorithms for pulse oximetry and BP recording within a DSS for telehealth patient management.
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Khalil SF, Mohktar MS, Ibrahim F. The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases. Sensors (Basel) 2014; 14:10895-928. [PMID: 24949644 PMCID: PMC4118362 DOI: 10.3390/s140610895] [Citation(s) in RCA: 276] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 06/03/2014] [Accepted: 06/04/2014] [Indexed: 12/13/2022]
Abstract
Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance in body composition estimation and evaluation of clinical status. This paper reviews the main concepts of bioimpedance measurement techniques including the frequency based, the allocation based, bioimpedance vector analysis and the real time bioimpedance analysis systems. Commonly used prediction equations for body composition assessment and influence of anthropometric measurements, gender, ethnic groups, postures, measurements protocols and electrode artifacts in estimated values are also discussed. In addition, this paper also contributes to the deliberations of bioimpedance analysis assessment of abnormal loss in lean body mass and unbalanced shift in body fluids and to the summary of diagnostic usage in different kinds of conditions such as cardiac, pulmonary, renal, and neural and infection diseases.
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Affiliation(s)
- Sami F Khalil
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Mas S Mohktar
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Fatimah Ibrahim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
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Habib MA, Mohktar MS, Kamaruzzaman SB, Lim KS, Pin TM, Ibrahim F. Smartphone-based solutions for fall detection and prevention: challenges and open issues. Sensors (Basel) 2014; 14:7181-208. [PMID: 24759116 PMCID: PMC4029687 DOI: 10.3390/s140407181] [Citation(s) in RCA: 153] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Revised: 04/10/2014] [Accepted: 04/11/2014] [Indexed: 11/16/2022]
Abstract
This paper presents a state-of-the-art survey of smartphone (SP)-based solutions for fall detection and prevention. Falls are considered as major health hazards for both the elderly and people with neurodegenerative diseases. To mitigate the adverse consequences of falling, a great deal of research has been conducted, mainly focused on two different approaches, namely, fall detection and fall prevention. Required hardware for both fall detection and prevention are also available in SPs. Consequently, researchers' interest in finding SP-based solutions has increased dramatically over recent years. To the best of our knowledge, there has been no published review on SP-based fall detection and prevention. Thus in this paper, we present the taxonomy for SP-based fall detection and prevention solutions and systematic comparisons of existing studies. We have also identified three challenges and three open issues for future research, after reviewing the existing articles. Our time series analysis demonstrates a trend towards the integration of external sensing units with SPs for improvement in usability of the systems.
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Affiliation(s)
- Mohammad Ashfak Habib
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Mas S Mohktar
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Shahrul Bahyah Kamaruzzaman
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Kheng Seang Lim
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Tan Maw Pin
- Centre for Innovation in Medical Engineering (CIME), Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Fatimah Ibrahim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
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Mohktar MS, Lin K, Redmond SJ, Basilakis J, Lovell NH. Design of a Decision Support System for a Home Telehealth Application. International Journal of E-Health and Medical Communications 2013. [DOI: 10.4018/jehmc.2013070105] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A decision support system (DSS) that has been designed to manage patients using a home telehealth system is presented. The DSS has been developed to assist home telehealth clinical support staff with their workload, and to provide more effective communication between multiple home telehealth users. The three-tier system architecture that consists of a data layer; a business logic layer; and a front-end layer employs business processes and uses a rule engine for its logic and knowledge base. This paper discusses the design considerations involved in the construction of a DSS for the purpose of home telehealth, and illustrates how it may be developed using entirely open source software.
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Affiliation(s)
- Mas S. Mohktar
- GSBME, University of New South Wales, Sydney, NSW, Australia & Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Kezhang Lin
- GSBME, University of New South Wales, Sydney, NSW, Australia
| | | | - Jim Basilakis
- School of Computing and Mathematics, University of Western Sydney, Sydney, NSW, Australia
| | - Nigel H. Lovell
- GSBME, University of New South Wales, Sydney, NSW, Australia
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Xie Y, Redmond SJ, Mohktar MS, Shany T, Basilakis J, Hession M, Lovell NH. Prediction of chronic obstructive pulmonary disease exacerbation using physiological time series patterns. Annu Int Conf IEEE Eng Med Biol Soc 2013; 2013:6784-6787. [PMID: 24111301 DOI: 10.1109/embc.2013.6611114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is responsible for significant morbidity and mortality worldwide. Recent clinical research has indicated a strong association between physiological homeostasis and the onset of COPD exacerbation. Thus the analysis of these variables may yield a means of predicting a COPD exacerbation in the near future. However, the accuracy of existing prediction methods based on statistical analysis of periodic snapshots of physiological variables is still far from satisfactory, due to lack of integration of long-term and interactive effects of the physiological variables. Therefore, developing a relatively accurate method for predicting COPD exacerbation is an outstanding challenge. In this paper, a regression-based machine learning technique was developed, using trend pattern variables extracted from COPD patients' longitudinal physiological records, to classify subjects into "low-risk" and "high-risk" categories, indicating their risk of suffering a COPD exacerbation event. Experimental results from cross validation assessment of the classifier model show an average accuracy of 79.27% using this method.
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Mohktar MS, Basilakis J, Redmond SJ, Lovell NH. A guideline-based decision support system for generating referral recommendations from routinely recorded home telehealth measurement data. Annu Int Conf IEEE Eng Med Biol Soc 2011; 2010:6166-9. [PMID: 21097150 DOI: 10.1109/iembs.2010.5627766] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The objectives of this paper are to present a guideline-based decision support system (GBDSS) design for supporting patient telehealth management of chronic disease and to test its performance in correctly making referral recommendations using routinely recorded measurement data from home telehealth recordings. The GBDSS has been developed to manage lung disease patients in a home telehealth environment. The system operates by checking the availability of home telehealth measurement data on a daily basis, interprets these data using a rule-based decision tree classification, and ultimately generates referral recommendations based on these measured data. The system has demonstrated discriminative power when applied in the analysis of retrospective telehealth data, as a surrogate for realtime referral generation. To this end a telehealth dataset comprising 16 chronic obstructive pulmonary disease (COPD) patients monitored over a 12 month period was used. It was shown that GBDSS referral recommendations could help reduce the number of cases that required a carer's urgent attention by 72.1%, with 81.9% accuracy, 80.8% specificity and 90.4% sensitivity.
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Affiliation(s)
- Mas S Mohktar
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, 2052, Australia
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