Dynamically recommending repositories for health data : a machine learning model
- Authors: Uddin, Md Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
- Full Text: false
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- Description: Recently, a wide range of digital health record repositories has emerged. These include Electronic Health record managed by the government, Electronic Medical Record (EMR) managed by healthcare providers, Personal Health Record (PHR) managed directly by the patient and new Blockchain-based systems mainly managed by technologies. Health record repositories differ from one another on the level of security, privacy, and quality of services (QoS) they provide. Health data stored in these repositories also varies from patient to patient in sensitivity, and significance depending on medical, personal preference, and other factors. Decisions regarding which digital record repository is most appropriate for the storage of each data item at every point in time are complex and nuanced. The challenges are exacerbated with health data continuously streamed from wearable sensors. In this paper, we propose a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The model maps health data to be stored in the repositories. The mapping between health data features and characteristics of each repository is learned using a machine learning-based classifier mediated through clinical rules. Evaluation results demonstrate the model's feasibility. © 2020 ACM.
- Description: E1
Towards smart online dispute resolution for medical disputes
- Authors: Bellucci, Emilia , Stranieri, Andrew , Venkatraman, Sitalakshmi
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2020); Melbourne, Australia; 3rd-7th February 2020. p. 1-5
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- Description: With the advancements in technologies, digitization of health records in the healthcare industry is undertaking a rapid revolution. This is further fueled with the entrance of Internet of Things (IoT), where mobile health devices have resulted in an explosion of health data and increased accessibility via wireless communications and sensor networks. With the introduction of an Electronic Health Record (EHR) system as an important venture for the general health and wellbeing of a country's citizens, privacy issues and medical disputes are expected to rise. In addition to critical health information being documented and shared electronically, integrating data from diverse smart medical IoT devices are leading towards increasingly more complex disputes that require immense time and effort to resolve. Online dispute resolution (ODR) programs have been successfully applied to cost-effectively help disputants resolve commercial, insurance and other legal disputes, but as yet have not been applied to healthcare. This paper takes a modest step in this direction, firstly to identify the drivers of medical disputes that include patient empowerment and technology advancements and trends. Secondly, we explore dispute resolution models and identify the status and limitations of current ODR systems.
- Description: This work was funded by the University of Ballarat Deakin University Collaborative Fund. 160134
A Decentralized Patient Agent Controlled Blockchain for Remote Patient Monitoring
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 15th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2019 Vol. 2019-October, p. 207-214
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- Description: Blockchain emerging for healthcare provides a secure, decentralized and patient driven record management system. However, the storage of data generated from IoT devices in remote patient management applications requires a fast consensus mechanism. In this paper, we propose a lightweight consensus mechanism and a decentralized patient software agent to control a remote patient monitoring (RPM) system. The decentralized RPM architecture includes devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) software replicated on the Smartphone, Fog and Cloud servers processes medical data to ensure reliable, secure and private communication. Performance analysis has been conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled remote patient monitoring system. © 2019 IEEE.
- Description: E1
An efficient selective miner consensus protocol in blockchain oriented iot smart monitoring
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne; Australia; 13th-15th February 2019 Vol. 2019-February, p. 1135-1142
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- Description: Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
Blockchain leveraged task migration in body area sensor networks
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th Asia-Pacific Conference on Communications, APCC 2019 p. 177-184
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- Description: Blockchain technologies emerging for healthcare support secure health data sharing with greater interoperability among different heterogeneous systems. However, the collection and storage of data generated from Body Area Sensor Net-works(BASN) for migration to high processing power computing services requires an efficient BASN architecture. We present a decentralized BASN architecture that involves devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) replicated on the Smartphone, Fog and Cloud servers processes medical data and execute a task offloading algorithm by leveraging a Blockchain. Performance analysis is conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled BASN. © 2019 IEEE.
- Description: E1
Comparing Pixel N-grams and bag of visual word features for the classification of diabetic retinopathy
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Jelinek, Herbert
- Date: 2019
- Type: Text , Conference proceedings
- Relation: ACSW 2019: Australasian Computer Science Week 2019;Sydney NSW Australia; January 29 - 31, 2019; published in Proceedings of the Australasian Computer Science Week Multiconference p. 1-7
- Full Text: false
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- Description: The extraction of Bag of Visual Words (BoVW) features from retinal images for automated classification has been shown to be effective but computationally expensive. Histogram and co-variance matrix features do not generally result in models that have the same predictive accuracy as BoVW and are still computationally expensive. The discovery of features that result in accurate image classification on computationally constrained devices such as smartphones would enable new and promising applications for image classification. For example, smartphone retinal cameras can conceivably make diabetic retinopathy widely available and potentially reduce undiagnosed retinopathy if it could be achieved with computationally simple classification algorithms. A novel image feature extraction technique inspired by N-grams in text mining, called 'Pixel N-grams' is described that can serve this purpose. Results on mammogram and texture classification have shown high accuracy despite the reduced computational complexity. However retinal scan classification results using Pixel N-grams lag behind BoVW approaches. An explanation for the relative poor performance of Pixel N-grams with diabetic retinopathy that draws on concepts associated with the No Free Lunch theorem are presented.
Integrating biological heuristics and gene expression data for gene regulatory network inference
- Authors: Zarnegar, Armita , Jelinek, Herbert , Vamplew, Peter , Stranieri, Andrew
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 Australasian Computer Science Week Multiconference, ACSW 2019; Sydney, Australia; 29th-31st January 2019 p. 1-10
- Full Text: false
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- Description: Gene Regulatory Networks (GRNs) offer enhanced insight into the biological functions and biochemical pathways of cells associated with gene regulatory mechanisms. However, obtaining accurate GRNs that explain gene expressions and functional associations remains a difficult task. Only a few studies have incorporated heuristics into a GRN discovery process. Doing so has the potential to improve accuracy and reduce the search space and computational time. A technique for GRN discovery that integrates heuristic information into the discovery process is advanced. The approach incorporates three elements: 1) a novel 2D visualized coexpression function that measures the association between genes; 2) a post-processing step that improves detection of up, down and self-regulation and 3) the application of heuristics to generate a Hub network as the backbone of the GRN. Using available microarray and next generation sequencing data from Escherichia coli, six synthetic benchmark GRN datasets were generated with the neighborhood addition and cluster addition methods available in SynTReN. Results of the novel 2D-visualization co-expression function were compared with results obtained using Pearson's correlation and mutual information. The performance of the biological genetics-based heuristics consisting of the 2D-Visualized Co-expression function, post-processing and Hub network was then evaluated by comparing the performance to the GRNs obtained by ARACNe and CLR. The 2D-Visualized Co-expression function significantly improved gene-gene association matching compared to Pearson's correlation coefficient (t = 3.46, df = 5, p = 0.02) and Mutual Information (t = 4.42, df = 5, p = 0.007). The heuristics model gave a 60% improvement against ARACNe (p = 0.02) and CLR (p = 0.019). Analysis of Escherichia coli data suggests that the GRN discovery technique proposed is capable of identifying significant transcriptional regulatory interactions and the corresponding regulatory networks.
Patient-empowered electronic health records
- Authors: Sahama, Tony , Stranieri, Andrew , Butler-Henderson, Kerryn
- Date: 2019
- Type: Text , Conference proceedings
- Relation: MEDINFO 2019: Health and Wellbeing e-Networks for All Vol. 264, p. 1765
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- Description: Electronic Health Records (EHRs) constitute evidence of online health information management. Critical healthcare information technology (HIT) infrastructure facilitates health information exchange of 'modern' health systems. The growth and implementation of EHRs are progressing in many countries while the adoption rate is lagging and lacking momentum amidst privacy and security concerns. This paper uses an interrupted time series (ITS) analysis of OECD data related to EHRs from many countries to make predictions about EHR adoption. The ITS model can be used to explore the impact of various HIT on adoption. Assumptions about the impact of Information Accountability are entered into the model to generate projections if information accountability technologies are developed. In this way, the OECD data and ITS analysis can be used to perform simulations for improving EHR adoption.
Semi-invasive system for detecting and monitoring dementia patients
- Authors: Yamsanwar, Yash , Patankar, Amol , Kulkarni, Siddhivinayak , Stratton, David , Stranieri, Andrew
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 5th IEEE International Conference for Convergence in Technolog, I2CT 2019
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- Description: Dementia is one of the most prevalent conditions faced by the elderly caused by specific brain cell damage. Various effects of dementia include a loss of memory, reduction in problem solving ability, analytical skills, and decision making capability. Few systems have been developed for the early detection of dementia. Existing systems depend largely on hardware e.g. sensors, gateways. Factors like maintainability and sustainability compromise the efficiency of such systems. This paper presents a novel approach towards the early detection of dementia and aims at eliminating some of the challenges posed by these systems. It also provides a comparati ve study of the cognitive abilities of healthy old-age people and those afflicted by dementia. © 2019 IEEE.
- Description: E1
A patient agent to manage blockchains for remote patient monitoring
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 7th International Conference on Global Telehealth, GT 2018; Colombo, Sri Lanka; 10th-11th October 2018; published in Studies in Health Technology and Informatics Vol. 254, p. 105-115
- Full Text: false
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- Description: Continuous monitoring of patient's physiological signs has the potential to augment traditional medical practice, particularly in developing countries that have a shortage of healthcare professionals. However, continuously streamed data presents additional security, storage and retrieval challenges and further inhibits initiatives to integrate data to form electronic health record systems. Blockchain technologies enable data to be stored securely and inexpensively without recourse to a trusted authority. Blockchain technologies also promise to provide architectures for electronic health records that do not require huge government expenditure that challenge developing nations. However, Blockchain deployment, particularly with streamed data challenges existing Blockchain algorithms that take too long to place data in a block, and have no mechanism to determine whether every data point in every stream should be stored in such a secure way. This article presents an architecture that involves a Patient Agent, coordinating the insertion of continuous data streams into Blockchains to form an electronic health record.
- Description: Studies in Health Technology and Informatics
Comparison of pixel N-Grams with histogram, Haralick's features and bag-of-visual-words for texture image classification
- Authors: Kulkarni, Pradnya , Stranieri, Andrew
- Date: 2018
- Type: Text , Conference proceedings
- Relation: IEEE 3rd International Conference on Convergence in Technology: Pune, India ; April 6th-8th, 2018 p. 1-4
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- Description: Texture image classification is very useful in many domains. It has been tried using statistical, spectral and structural approaches. A novel Pixel N-grams technique has emerged for image feature extraction recently. The aim of this paper is to analyse the efficacy of Pixel N-grams technique for texture image classification in comparison with the traditional techniques namely Intensity histogram, Haralick’s features based on co-occurrence matrix and state-of-the-art Bag-of-Visual-Words (BoVW). The experiments were carried out on the benchmark UIUC texture dataset using SVM classifier. The classification performance was compared using Fscore, Recall and Precision. The classification results using Pixel N-gram were significantly better than that using Intensity histogram and Haralick features whereas, they were comparable with the BoVW approach.
Data analytics to select markers and cut-off values for clinical scoring
- Authors: Stranieri, Andrew , Yatsko, Andrew , Venkatraman, Sitalakshmi , Jelinek, Herbert
- Date: 2018
- Type: Text , Conference proceedings
- Relation: ACSW '18: Proceedings of the Australasian Computer Science Week Multiconference; Brisbane; 29th January -2nd February 2018 p. 1-6
- Full Text: false
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- Description: Scoring systems such as the Glasgow-Coma scale used to assess consciousness AusDrisk to assess the risk of diabetes, are prevalent in clinical practice. Scoring systems typically include relevant variables with ordinal values where each value is assigned a weight. Weights for selected values are summed and compared to thresholds for health care professionals to rapidly generate a score. Scoring systems are prevalent in clinical practice because they are easy and quick to use. However, most scoring systems comprise many variables and require some time to calculate an final score. Further, expensive population-wide studies are required to validate a scoring system. In this article, we present a new approach for the generation of a scoring system. The approach uses a search procedure invoking iterative decision tree induction to identify a suite of scoring rules, each of which requires values on only two variables. Twelve scoring rules were discovered using the approach, from an Australian screening program for the assessment of Type 2 Diabetes risk. However, classifications from the 12 rules can conflict. In this paper we argue that a simple rule preference relation is sufficient for the resolution of rule conflicts.
A rule based inference model to establish strategy-process relationship
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 30th International Business Information Management Association Conference - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, IBIMA 2017; Madrid, Spain; 8th-9th November 2017 Vol. 2017-January, p. 4544-4556
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- Description: An effective relationship between business processes and their relevant strategies helps enterprises achieve their goals. As a business organisation changes quickly, business processes implement their relevant business operations for efficiency. It is important to know which business process achieves which business strategies dynamically. To the best of our knowledge, there exists a framework which aims to automatically determine the strategy-process relationship (Morrison et al. 2011). However, this framework can only work when the effect of the business process is known, but it is difficult to determine such effect accurately. Moreover, by optimising business processes to satisfy business strategies, higher efficiency may be achieved but there is a high chance of losing discriminative information. It therefore creates certain level of uncertainty in achieving accurate strategy-process relationship. To reduce this uncertainty and determine the relationship accurately between business processes and their relevant strategies as defined by business domain experts, in this paper, we introduce a rule-based inference model. This model not only helps business organisations realize which business processes need to be involved for the organisation to achieve their goals when strategies are made, but also reduces the possibility of losing important details from business process optimisation. We have developed a business case to validate our proposed model and the results show that our model can infer the relation accurately for each rule defined for the related business case.
An Agile group aware process beyond CRISP-DM: A hospital data mining case study
- Authors: Sharma, Vishakha , Stranieri, Andrew , Ugon, Julien , Martin, Laura
- Date: 2017
- Type: Text , Conference proceedings
- Relation: ICCDA '17: Proceedings of the International Conference on Computer and Data Analysis May 2017 p. 109-113
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- Description: The CRISP-DM methodology is commonly used in data analytics exercises within an organisation to provide system and structure to data mining processes. However, in providing a rigorous framework, CRISP-DM overlooks two facets of data analytics in organisational contexts; data mining exercises are far more agile and subject to change than presumed in CRISP-DM and central decisions regarding the interpretation of patterns discovered and the direction of analytics exercises are typically not made by individuals but by committees or groups within an organisation. The current study provides a case study of data mining in a hospital setting and suggests how the agile nature of an analytics exercise and the group reasoning inherent in key decisions can be accommodated within a CRISP-DM methodology.
Atrial fibrillation analysis for real time patient monitoring
- Authors: Allami, Ragheed , Stranieri, Andrew , Marzbanrad, Faezeh , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 44th Computing in Cardiology Conference, CinC 2017 Vol. 44, p. 1-4
- Full Text: false
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- Description: Atrial Fibrillation (AF) can lead to life-threatening conditions such as stroke and heart failure. The instant recognition of life-threatening cardiac arrhythmias based on a 3-lead ECG to record a Lead II configuration for a few seconds is a challenging problem of clinical significance. Five consecutive ECG beats that were identified by a cardiologist to characterise an AF episode and five consecutive heartbeat intervals representing an irregular RR intervals episode were analysed. The detection and analysis of P waves as the morphological features of AF was executed based on two template matching methods. An AF detector was developed by combining the correlation coefficients based on the template matching methods and the standard deviation of the RR intervals. The AF detector was then applied to classify 5 consecutive beats as AF or non-AF based on thresholding the calculated irregularity. The proposed algorithm was tested on the MIT-BIH Atrial Fibrillation and the Challenge 2017 databases. The proposed method resulted in an improved sensitivity, specificity and accuracy of 97.60%, 98.20% and 99% respectively compared to recent published methods. In addition, the proposed method is suitable for real-time patient monitoring as it is computationally simple and requires only a few seconds of ECG recording to detect an AF rhythm. © 2017 IEEE Computer Society. All rights reserved.
Pixel N-grams for mammographic lesion classification
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Ugon, Julien , Mittal, Manish , Kulkarni, Siddhivinayak
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 2nd International Conference on Communication Systems, Computing and IT Applications, CSCITA , Mumbai; 7th-8th April, 2017; published in CSCITA 2017 - Proceedings p. 107-111
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- Description: Automated classification algorithms have been applied to breast cancer diagnosis in order to improve the diagnostic accuracy and turnover time. However, classification accuracy, sensitivity and specificity could still be improved further. Moreover, reducing computational cost is another challenge as the number of images to be analyzed is typically large. In this paper, a novel Pixel N-gram approach inspired from character N-grams in the text retrieval context has been applied for mammographic lesion classification. The experiments on real world database demonstrate that the Pixel N-grams outperform the existing histogram as well as Haralick features with respect to classification accuracy as well as sensitivity. Effect of varying N and using various classifiers is also analyzed in this paper. Results show that optimum value of N is equal to 3 and MLP classifier performs better than SVM and KNN classifier using 3-gram features.
Significance level of a query for enterprise data
- Authors: Thi Ngoc Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew , Das, Rajkumar
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 30th International Business Information Management Association Conference - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, IBIMA 2017; Madrid, Spain; 8th-9th November 2017 Vol. 2017-January, p. 4494-4504
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- Description: To operate enterprise activities, a large number of queries need to be processed every day through an enterprise system. Consequently, such a system frequently faces hugely overloaded information and incurs high delay in producing query responses for big data. This is because, traditional queries are normally treated with equal importance. With the advent of big data and its use in enterprise systems and the growth of process complexity, the traditional approach of query processing is no more suitable as it does not consider semantic information and captures all data irrespective of their relevance to a business organization, which eventually increases the computational time in both big data collection and analysis. The significance level of a query can make a trade-off between query response delay and the extent of data collection and analysis. This motivates us to concentrate on determining the significance level of a query considering its importance to an enterprise system. To our knowledge, no such approach is available in the literature. To bridge this research gap, this paper, for the first time, proposes an approach to determine the significance level of a query to prioritize them with the relevance to a business organization. As business processes play key roles in any enterprise system and all business processes are not equally important, this is done by determining the semantic similarity between a query and the processes of a business organization and the importance of a business process to that organization. With a case study on an enterprise system of a retail company, the results produced by our proposed approach have shown that significance level is higher for more important queries compared to the less important ones.
A heuristic gene regulatory networks model for cardiac function and pathology
- Authors: Zarnegar, Armita , Vamplew, Peter , Stranieri, Andrew , Jelinek, Herbert
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 Computing in Cardiology Conference (CinC); Vancouver; 11-14th Sept, 2016
- Full Text: false
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- Description: Genome-wide association studies (GWAS) and next-generation sequencing (NGS) has led to an increase in information about the human genome and cardiovascular disease. Understanding the role of genes in cardiac function and pathology requires modeling gene interactions and identification of regulatory genes as part of a gene regulatory network (GRN). Feature selection and data reduction not sufficient and require domain knowledge to deal with large data. We propose three novel innovations in constructing a GRN based on heuristics. A 2D Visualised Co-regulation function. Post-processing to identify gene-gene interactions. Finally a threshold algorithm is applied to identify the hub genes that provide the backbone of the GRN. The 2D Visualized Co-regulation function performed significantly better compared to the Pearson's correlation for measuring pairwise associations (t=3.46, df=5, p=0.018). The F-measure, improved from 0.11 to 0.12. The hub network provided a 60% improvement to that reported in the literature. The performance of the hub network was then also compared against ARACNe and performed significantly better (p=0.024). We conclude that a heuristics approach in developing GRNs has potential to improve our understanding of gene regulation and interaction in diverse biological function and disease.
A model for the introduction of Ayurvedic and Allopathic Electronic Health Records in Sri Lanka
- Authors: Stranieri, Andrew , Sahama, Tony , Butler-Henderson, Kerryn , Perera, Kamal
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 IEEE International Symposium on Technology and Society; Trivandrum, Kerala, India; 20th-22nd October 2016 p. 56-61
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- Description: Fully integrated electronic health records (EHR) provide healthcare providers and patients access to records across a health care system and promise efficient and effective provision of health care. However, fully integrated records have proven to be very expensive and difficult to establish. Currently. EHR's have been developed largely to accommodate Western medicine events. These barriers impact on the introduction of EHR's in Sri Lanka, where health budgets are already stretched and Ayurvedic medicine is routinely practiced alongside Allopathic medicine. This article identifies requirements for EHR in the Sri Lankan context and advances a model for the introduction of EHR's that suits that context. The model is justified by drawing on insights and experiences with EHR in Western nations.
ECG reduction for wearable sensor
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS); Naples, Italy; 28th November-1st December 2016 p. 520-525
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- Description: The transmission, storage and analysis of electrocardiogram (ECG) data in real-time is essential for remote patient monitoring with wearable ECG devices and mobile ECG contexts. However, this remains a challenge to achieve within the processing power and the storage capacity of mobile devices. ECG reduction algorithms have an important role to play in reducing the processing requirements for mobile devices, however many existing ECG reduction and compression algorithms are computationally expensive to execute in mobile devices and have not been designed for real-time computation and incremental data arrival. In this paper, we describe a computationally naive, yet effective, algorithm that achieves high ECG reduction rates while maintaining key diagnostic features including PR, QRS, ST, QT and RR intervals. While reduction does not enable ECG waves to be reproduced, the ability to transmit key indicators (diagnostic features) using minimal computational resources, is particularly useful in mobile health contexts involving power constrained sensors and devices. Results of the proposed reduction algorithm indicate that the proposed algorithm outperforms other ECG reduction algorithms at a reduction/compression ratio (CR) of 5:1. If power or processing capacity is low, the algorithm can readily switch to a compression ratio of up to 10: 1 while still maintaining an error rate below 10%.