Showing papers in "International Journal of E-health and Medical Communications in 2019"
TL;DR: A comparative analysis of performance evaluation of three trusted candidate encryption algorithms, namely AES, SPECK and SIMON, which are simulated and compared in details to distinguish who has the best behaviour to be nominated for a medical application.
Abstract: Short period monitoring and emergency notification of healthcare signals is becoming affordable with existence of internet of things (IoT) support. However, IoT does not prevent challenges that may hinder the appropriate safe spread of medical solutions. Confidentiality of data is vital, making a real fear requesting cryptography. The limitations in memory, computations processing, power consumptions, and small-size devices contradict the robust encryption process asking for help of low-weight-cryptography to handle practically. This article presents a comparative analysis of performance evaluation of three trusted candidate encryption algorithms, namely AES, SPECK and SIMON, which are simulated and compared in details to distinguish who has the best behaviour to be nominated for a medical application. These encryption algorithms are implemented and evaluated in regard to the execution time, power consumption, memory occupation and speed. The implementation is carried out using the Cooja simulator running on Contiki operating system showing interesting attractive results.
42 citations
TL;DR: An overview of HL7 FHIR, its concepts and literature review on its current status, usage, and adoption is provided to bridge interoperability gap between the growing number of disparate and variety of healthcare entities.
Abstract: The inception of EHR has shown a lot of potentials and virtually eliminated the drawbacks of paper-based medical notes. However, the transition has not been seamless due to various technical and po...
36 citations
TL;DR: The idea of integrating all the individual methods in to a multi-modal diagnostic system to enhance detection sensitivity is presented to present the potential to diagnose and predict ASD clinically, neurologically & objectively with high detection sensitivity.
Abstract: Autism spectrum disorder (ASD) is a complex heterogeneous neurological disorder that has led to a spectrum of diagnosis techniques. The screening instruments, medical and technological tools initia...
22 citations
TL;DR: A novel task scheduling algorithm is proposed to improve the QoS parameters which comprises of metrics such as response time, computation time, availability and cost to optimize the performance.
Abstract: The fast-growing internet services have led to the rapid development of storing, retrieving and processing health-related documents from a public cloud. In such a scenario, the performance of cloud services offered is not guaranteed, since it depends on efficient resource scheduling, network bandwidth, etc. The trade-off which lies between the cost and the QoS is that the cost should be variably low on achieving high QoS. This can be done by performance optimization. In order to optimize the performance, a novel task scheduling algorithm is proposed in this article. The main advantage of this proposed scheduling algorithm is to improve the QoS parameters which comprises of metrics such as response time, computation time, availability and cost. The proposed work is simulated in Aneka and shows better performance compared to existing paradigms.
22 citations
TL;DR: A SPIHT compression technique assisted YDbDr-Fuzzy c-means clustering considerably reduces the execution time, is simple to implement, saves memory, improves segmentation accuracy, and yields better results than the same without using SPIHT.
Abstract: The healing status of chronic wounds is important for monitoring the condition of the wounds. This article designs and discusses the implementation of smartphone-enabled compression technique under a tele-wound network (TWN) system. Nowadays, there is a huge demand for memory and bandwidth savings for clinical data processing. Wound images are captured using a smartphone through a metadata application page. Then, they are compressed and sent to the telemedical hub with a set partitioning in hierarchical tree (SPIHT) compression algorithm. The transmitted image can then be reduced, followed by an improvement in the segmentation accuracy and sensitivity. Better wound healing treatment depends on segmentation and classification accuracy. The proposed framework is evaluated in terms of rates (bits per pixel), compression ratio, peak signal to noise ratio, transmission time, mean square error and diagnostic quality under telemedicine framework. A SPIHT compression technique assisted YDbDr-Fuzzy c-means clustering considerably reduces the execution time (105s), is simple to implement, saves memory (18 KB), improves segmentation accuracy (98.39%), and yields better results than the same without using SPIHT. The results favor the possibility of developing a practical smartphone-enabled telemedicine system and show the potential for being implemented in the field of clinical evaluation and the management of chronic wounds in the future.
13 citations
TL;DR: A model for preserving privacy in data storage used in health care systems is outlined by validating access to the data through IP based detection and geographical location-based security techniques and the concept of using constant key length encryption technique to secure data on cloud storage irrespective of the type of user is provided.
Abstract: Cloud-based platforms are helping organizations like health care systems to improve conditions of patients and saving their lives. Medical professionals are making use of cloud technology to collect information regarding patients more than before and exchange it over different geographical regions. However, the exchange of patient data and information is taking place via complex systems with huge vulnerabilities and risks. In this article, the authors have outlined a model for preserving privacy in data storage used in health care systems by validating access to the data through IP based detection and geographical location-based security techniques. Later, the privacy is enabled by using k-mean clustering technique for validating the user access and avail subscriptions whenever consumer want to use the organization services. The authors also provide the concept of using constant key length encryption technique to secure data on cloud storage irrespective of the type of user.
12 citations
TL;DR: In this research, a discrete wavelet-based watermarking scheme has been proposed for the authentication of medical images and the experimental results obtained have shown that the proposed scheme is a secure one and robust against various common image processing attacks.
Abstract: The growth and emergence of telemedicine has resulted into considerable increase in the transfer of medical images between various health centers over computer networks for various purposes, such as diagnosis, clinical interpretation, and archives. The security of these images is a serious concern since these images can be manipulated during their transmission over the network. Moreover, as the authentication of the medical images is required for various legal purposes; the information security requirement specifications specified by various legislative rules must be adhered by the concerned stakeholders. In this research, a discrete wavelet-based watermarking scheme has been proposed for the authentication of medical images. The experimental results obtained have shown that the proposed scheme is a secure one and robust against various common image processing attacks.
12 citations
TL;DR: A data-driven multi-layer architecture for pervasively remote patient monitoring that incorporates issues like, wearability, adaptability, interoperability, integration, security, and network efficiency is proposed.
Abstract: In the last decade, significant advancements in telecommunications and informatics have seen which incredibly boost mobile communications, wireless networks, and pervasive computing. It enables hea...
9 citations
TL;DR: The conclusion is that mHealth interventions have positive effects on various health related outcomes, but further research is required to allow their incorporation in the clinical practice.
Abstract: The study reported in this article aimed to identify: i) the most relevant application domains of mHealth to support older adults in their domiciles; ii) the most relevant chronic conditions of older adults, whose management is being supported by mHealth; iii) the characteristics, outcomes and impacts of mHealth tools that might support older adults in their domiciles. The method of a systematic review of reviews and meta-analyses was performed based on a search of the literature. The result of a total of 66 reviews and meta-analyses across several chronic diseases were retrieved. These studies compare mHealth interventions with usual care. The conclusion is that mHealth interventions have positive effects on various health related outcomes, but further research is required to allow their incorporation in the clinical practice.
8 citations
TL;DR: This article proposes a system that can be used to transmit patient vitals like pulse rate, oxygen saturation, and perfusion index readings to a doctor in a remote area, while a patient is in transit, using a smartphone application, a pulse oximeter, and the real-time data transferring capabilities of Firebase.
Abstract: Most developing countries are currently unable to provide adequate, let alone advanced healthcare support to rural areas. Telemedicine combines the capability of information technology and dedicate...
5 citations
TL;DR: The purpose of this article is to simplify the integration of OpenEHR, by introducing a stepwise methodology of the migration from legacy SQL-based EHR to an interoperable OpenE HR based NoSQL oriented document model.
Abstract: Nowadays, having an electronic health record properly adopted by medical bodies is no longer a challenge. In fact, the critical issue for health practitioners is related to the exchange of health d...
TL;DR: The objective is to predict the stage of breast cancer by using diverse input parameters and the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched.
Abstract: Quality analysis of the treatment of cancer has been an objective of e-health services for quite some time. The objective is to predict the stage of breast cancer by using diverse input parameters. Breast cancer is one of the main causes of death in women when compared to other tumors. The classification of breast cancer information can be profitable to anticipate diseases or track the hereditary of tumors. For classification, an artificial neural network (ANN) structure was carried out. In the structure, nine training algorithms are used and the proposed is the Levenberg-Marquardt algorithm. For optimizing the hidden layer and neuron, three optimization techniques are used. In the result, the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched. The correlation execution diagram for an accuracy of 95%, a sensitivity of 98%, and a specificity of 89% of a social spider optimization (SSO) algorithm are shown.
TL;DR: A security framework is proposed where-in smart devices encrypt sensed physiological data with Light-Weight Encryption Algorithm and Advanced Encryption Standard cryptographic algorithms and the designed protocol provides better security and are energy efficient as presented in the evaluation section.
Abstract: Internet of Things (IoT) is the emerging technology finding applications in a wide range of fields that include smart homes, intelligent transportation, e-health, supply chain management. Among IoT...
TL;DR: The Patient 3.0 Profile is used to explore to the patient engagement strategies of early adopter baby boomers' in three domains: 1) patient relationships, 2) health information use and 3) consumer health technology (CHT) use.
Abstract: The Patient 3.0 Profile is used to explore to the patient engagement strategies of early adopter baby boomers' in three domains: 1) patient relationships, 2) health information use and 3) consumer health technology (CHT) use. Findings from six focus groups with early adopter boomers challenge prior notions about older adults' passive approach to patient engagement. Baby boomers want to make final healthcare decisions with input from providers. While adept at finding and critically assessing online health information for self-education and self-management, boomers want providers to curate relevant and trustworthy information. Boomers embrace CHTs offered through providers (i.e., patient portals, email and text messaging) and sponsored by wellness programs (i.e., diet and activity devices and apps). However, there is no indication they add information to their online medical records or use CHT for diagnosis, treatment or disease management. Additional resources are needed to encourage widespread adoption, support patient effectiveness, and confirm cost-benefit.
TL;DR: This work carries out an extended experimental evaluation of a secure architecture based on ubiquitous computing for medical records retrieval and maintenance to demonstrate its feasibility through of experiments in laboratory and hospital scenarios.
Abstract: Electronic health record (EHR) systems play a relevant role in hospital daily activities. They support the access of patient data, such as medication delivery and history of hospitalizations. Currently, it is essential to provide easy and secure access to medical records by hospital staff. Pervasive and ubiquitous computing can lead to a better way to manage EHR systems. In this scenario, one of the greatest research challenges is to ensure device authentication avoiding its impersonation. The main contribution of this work is to carry out an extended experimental evaluation of a secure architecture based on ubiquitous computing for medical records retrieval and maintenance to demonstrate its feasibility through of experiments in laboratory and hospital scenarios. For evaluation, this article implements a prototype that performs the medication delivery procedure. Experimental results indicate the system functionality, usability with a decreasing user learning curve, and no significant overhead in terms of communication delay.
TL;DR: The objective of this research article is to provide the complete statistical performance estimates of the four classifiers to the authenticated cloud users and the proposed comparative analysis framework can be used to analyze the performances of the classifiers with respect to any input dataset.
Abstract: Several classifiers are prevalent which act as a major drive for almost all supervised machine learning applications. These classifiers, though their objective working looks similar, they vary drastically in their performances. Some of the important factors that cause such variations are the scalability of the dataset, dataset nature, training time estimation, classifying time for the test data, prediction accuracy and the error rate computation. This paper focuses mainly on analyzing the performance of the existing four main classifiers: IF-THEN rule, C4.5 decision trees, naive Bayes, and SVM classifier. The objective of this research article is to provide the complete statistical performance estimates of the four classifiers to the authenticated cloud users. These users have the access facility in obtaining the essential statistical information about the classifiers in study from the cloud server. Such statistical information might be helpful in choosing the best classifier for their personal or organizational benefits. The classifiers follow the traditional underlying algorithms for classification that is performed in the cloud server. These classifiers are tested on three different datasets namely PIMA, breast-cancer and liver-disorders dataset for performance analysis. The performance analysis indicators used in this research article to summarize the working of the various classifiers are training time, testing time, prediction accuracy and error rate computation. The proposed comparative analysis framework can be used to analyze the performances of the classifiers with respect to any input dataset.
TL;DR: An experimental study to determine how to use the stored Digital Imaging and Communication in Medicine (DICOM) metadata to perform population studies shows the relevance of the aggregation and analyses of DICOM metadata stored in heterogeneous PACS facilities.
Abstract: This article reports an experimental study to determine how to use the stored Digital Imaging and Communication in Medicine DICOM metadata to perform population studies. As a case study, it was con...
TL;DR: This proposed method for retinal imagesdetection is used to diagnose various diseases and state-of-art methods.
Abstract: Retinal images are commonly used to diagnose various diseases, such as diabetic retinopathy, glaucoma, and hypertension. An important step in the analysis of such images is the detection of blood vessels, which is usually done manually and is time consuming. The main proposal in this work is a fast method for retinal blood vessel detection using Extreme Learning Machine (ELM). ELM requires only one iteration to complete its training and it is a robust and fast network in all aspects. The proposal is a compact and efficient representation of retinal images in which the authors achieved a reduction up to 39% of the initial data volume, while still keeping representativeness. To achieve such a reduction whilst maintaining the representativeness, three features (local tophat, local average, and local variance) were used. According to the simulations carried out, this proposal achieved an accuracy of about 95% for most results, outperforming most of the state-of-art methods. Furthermore, this proposal has greater sensitivity, meaning that more vessel pixels are detected correctly.
TL;DR: A generic conceptual process framework for effective HIM that provides cost-effective, portable, easy to use solution that incorporates GIS, Mobile technology, information management concepts, ICD-10 codes, WHO and mHealth standards is defined.
Abstract: For effective decision making in public health information management(HIM) system, health information availability, accessibility, prompt exchange, GIS linkage, spatiotemporal analysis of diseases is crucial. Lack of cost-effective technical support and information gaps are the main obstacles in HIM. This article defines a generic conceptual process framework for effective HIM that provides cost-effective, portable, easy to use solution. The solution incorporates GIS, Mobile technology, information management concepts, ICD-10 codes, WHO and mHealth standards. The current research is implemented as an android application that facilitates: 1) Patient disease data collection, geospatial mapping of disease data and accumulate a centralized server 2) LETL that supports bulk disease data upload 3) Addresses syntactic and semantic heterogeneity in health data 4) A strong multi-criteria query engine, visualization and spatiotemporal analysis of diseases are designed with a global perspective to be used across the globe.
TL;DR: An automatic identity concealment system for pictures is presented, designed in the GetBetter tele-medicine system developed under a WHO/TDR grant, and shown to be not only effective in concealing the identity of the patient, but also in preserving the exact distribution of pixel values in the image.
Abstract: Tele-medicine systems run the risk of unauthorized access to medical records, and there is greater possibility for the unlawful sharing of sensitive patient information, including children, and possibly showing their private parts. Aside from violating their right to privacy, such practices discourage patients from subjecting themselves to tele-medicine. The authors thus present an automatic identity concealment system for pictures, the way it is designed in the GetBetter tele-medicine system developed under a WHO/TDR grant. Based on open-source face- and eye-detection algorithms, identity concealment is executed by blurring the eye region of a detected face using pixel shuffling. This method is shown to be not only effective in concealing the identity of the patient, but also in preserving the exact distribution of pixel values in the image. This is useful when subsequent image processing techniques are employed, such as when identifying the type of lesions based on images of the skin.
TL;DR: An algorithm to detect exudates, which can be considered as one of the many abnormalities, to identify diabetic retinopathy from fundus images is presented, which is novel and adaptive, that is highly accurate for detection of exudate.
Abstract: This article presents an algorithm to detect exudates, which can be considered as one of the many abnormalities, to identify diabetic retinopathy from fundus images. The algorithm is invariant to illumination and works well on poor contrast images with high reflection noise. The artefacts are correctly rejected despite their colour, intensity and contrast being almost similar to that of exudates. Optic disc is localized and segmented using average filter of specially determined size which is an important step in the rejection of false positives. Exudates are located by generating candidate regions using variance and median filters followed by morphological reconstruction. The strategic selection of local properties to decide the threshold, makes this approach novel and adaptive, that is highly accurate for detection of exudates. The proposed method was tested on two publicly available labelled databases (DIARETDB1 and MESSIDOR) and a database from a local hospital and achieved a sensitivity of 96.765% and a positive predictive value of 93.514%.
TL;DR: This is an original study in the realm of healthcare management, which reveals that the technology related factors and privacy concerns have considerable impact on the successful implementation of personalized medicare system on a large scale.
Abstract: Personalized medicare systems is an emerging field of research, which bears the potential to significantly reduce healthcare expenditures and treatment errors and thereby to revolutionize the entire treatment procedure. In this novel approach, genomic variation in different individuals is duly taken into consideration. However, there exist several serious issues (e.g. privacy concerns) that provide hindrance to large-scale adoption of this medicare system. The main objective of this study has been to identify the privacy issues and to evaluate their impact on successful implementation of this novel medical treatment. The methodology used is empirical and is based on a survey-based post facto procedure. The data collected from the survey are analyzed by using the method of structural modelling analysis. This is an original study in the realm of healthcare management, which reveals that the technology related factors and privacy concerns have considerable impact on the successful implementation of personalized medicare system on a large scale. But the privacy concerns have no significant moderating effect on the impact of technology related factors, so far, the success of implementation of personalized medicine is concerned.
TL;DR: This model will help the doctor to know what course of preventive actions for a patient with high risk and what advice to give to those with low and moderate risk so that the occurrences of the diseases can be prevented altogether and thereby reducing the number of people dying from Type 2 Diabetes Mellitus diseases worldwide.
Abstract: This article presents a predictive model that can be used for the early detection of Type 2 Diabetes Mellitus using fuzzy logic. In order to formulate the model, risk factors associated with the risk of T2DM were elicited. The predictive model was formulated using fuzzy triangular membership functions following which the rules needed for the inference engine was elicited from experts. The model was simulated using the MATLAB Fuzzy logic Toolbox. The results of the study showed that the sensitivity of 11.67% and 100% precision for the low risk was recorded for both cases, specificity of 41.67% compared to 48.33% for the moderate risk, while there was 0% and 13.33% for the high risk. In conclusion, this model will help the doctor to know what course of preventive actions for a patient with high risk and what advice to give to those with low and moderate risk so that the occurrences of the diseases can be prevented altogether and thereby reducing the number of people dying from Type 2 Diabetes Mellitus diseases worldwide.
TL;DR: This article offers an instinctive approach for identification of blood vessels in ophthalmoscope images that is fast, time efficient, and gives consistent accuracy for all retinal images.
Abstract: In the present time, the identification of blood vessels is a basic task for diagnosis of various eye abnormalities. So, this article offers an instinctive approach for identification of blood vessels in ophthalmoscope images. This approach includes three different phases: pre-processing, vessel extraction and post-processing for getting a final vessel segmentation outcome. In the presented method, formerly log transformation and contrast limited adaptive histogram equalization are used for the enhancement of retinal images. The enhanced image is then filtered using a morphological opening operation and subsequently the optic disk is removed. The second phase includes the application of the improved Otsu method on the pre-processed image for the identification of blood vessels. Lastly, the resultant vessel-segmented image is obtained by using the morphological cleaning operation. The proposed method is fast, time efficient, and gives consistent accuracy for all retinal images. It is more robust and easier to implement compared to other traditional methods. The performance of the presented method is evaluated using ten different mathematical measures. It achieves average sensitivity, specificity and accuracy of 0.710, 0.982 and 0.956 for the digital retinal images for vessel extraction (DRIVE) database, 0.738, 0.982 and 0.954 for the structure analysis of the retina (STARE) database and 0.737, 0.964 and 0.949 for the child heart and health study in England (CHASE_DB1) database. The presented method also performs better in segmenting thin vessels by giving average accuracies of 0.964, 0.954 and 0.965 for DRIVE, STARE and CHASE_DB1 databases respectively.