Deep learning and computer vision have achieved remarkable success in many areas of machine learning and medical diagnostics. However, there is still a remarkable gap between dermatologists' skin cancer diagnosis and reliable... more
Deep learning and computer vision have achieved remarkable success in many areas of machine learning and medical diagnostics. However, there is still a remarkable gap between dermatologists' skin cancer diagnosis and reliable computer-aided melanoma detection. There are several reasons behind this gap, and the availability of insufficient data for training deep learning networks is one of them. Data augmen-tation is a popular technique to increase training data manifolds to mitigate the lack of data. In this paper, a conditional generative adversarial network (CGAN) is proposed to produce high-resolution synthetic images to augment the training data and gain higher performance of skin cancer detection systems. The artificial generation of images resembling real images is a difficult task owing to unstable information present in the skin lesions such as irregular borders, diameter, shape, color, and texture. The generator module of CGAN is designed to aggregate the information from all feature layers and produce synthetic images. Additionally, the generator incorporates the auxiliary information along with image inputs to map latent feature components successfully. The network is trained on 10,015 skin cancer images taken from the International Skin Imaging Collaboration (ISIC 2018). It was concluded from the experiments that the proposed model obtained better classi-fication performance as compared to the imbalanced original dataset and other state-of-the-art methods.
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Advanced engineering and information technology combined with medical and clinical knowledge enable evolutionary solutions for remote, wireless and continuous monitoring of vital signs. Such approach facilitates implementation of enhanced... more
Advanced engineering and information technology combined with medical and clinical knowledge enable evolutionary solutions for remote, wireless and continuous monitoring of vital signs. Such approach facilitates implementation of enhanced monitoring systems everywhere: at home, hospital and outdoor (on the move). It is estimated that more than 50 billion devices will be connected to the Internet by 2020. Among them would be devices for medical surveillance and diagnostic purposes. Vital signs are often considered as critical information to assess initial health condition and underlying health issues. They are monitored and analysed by clinicians for planning appropriate health interventions. Moreover, vital signs can be processed by computerised decision support systems, diagnostic models or expert systems to assist medical professionals by presenting early warning/alert or highlighting any significant changes of patients’ health conditions. The proposed study deals with the web-based and wireless monitoring of vital signs using advanced wireless data collection devices. The proposed system aims to aid in the diagnosis of patients’ health conditions from the collected vital signs and assist clinicians with better healthcare delivery. Data collection is currently undertaken at North Shore Hospital and Waitakere Hospital (Auckland, New Zealand) under local and national ethics approvals. The system collects blood pressure, pulse rate, heart rate, oxygen saturation (SpO2), ear temperature and blood glucose from hospitalised patients and transfers to web-based software called VitelMed by Medtech Global Ltd for remote monitoring and possible diagnosis. Ultimately this system can potentially achieve a high level of agreement with physicians when assessing specific physical signs such as bradycardia, tachycardia, hypertension, hypotension, hypoxemia, fever and hypothermia, and will be able to generate early warnings.
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This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected... more
This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected medical devices for collecting vital signs. The medical device data collected by the app includes heart rate, oxygen saturation and electrocardiograph (ECG). The collated data is streamed/displayed on the smartphone in real-time. This application was designed by adopting six screens approach (6S) mobile development framework and focused on user-centered approach and considered clinicians-as-a-user. The clinical engagement, consultations, feedback and usability of the application in the everyday practices were considered critical from the initial phase of the design and development. Furthermore, the proposed application is capable to deliver rich clinical decision support in real-time using the integrated medical device data.
Research Interests: Computer Science, Usability, Medicine, Electrocardiography, Decision support system, and 13 moreLibrary and Information Studies, Humans, Bluetooth, Pubmed, Mobile Applications, Heart rate, Feedback, Vital Signs, Public health systems and services research, Smartphone, Oximetry, Android Operating System, and Clinical Decision Support Systems
This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data... more
This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact components from the original EMG signal. Components that correspond to the ECG artifact are then identified by an automated detection algorithm and are subsequently removed using a conventional high pass filter. Finally, the results of the proposed method are compared with wavelet transform, ICA, adaptive filter and empirical mode decomposition-ICA methods. The automated artifact removal method proposed in this study successfully removes the ECG artifacts from EMG signals with a signal to noise ratio value of 9.38 while keeping the distortion of original EMG to a minimum.
Research Interests: Computer Science, Algorithms, Artificial Intelligence, Signal Processing, Skeletal muscle biology, and 14 moreIndependent Component Analysis, Medicine, Wavelet, Electrocardiography, Wavelet Analysis, Library and Information Studies, Humans, Wavelet Transform, Pubmed, Electromyography, Artifacts, Public health systems and services research, Muscle contraction, and Signal to Noise Ratio
There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic... more
There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions such as obesity is increasing to the point that requires effective interventions and advancements to reduce the burden of the healthcare. This research focuses on the early risk assessment of overweight/obesity using wearable technology. We establish an individualised health profile that identifies the level of activity and current health status of an individual using real-time activity and vital signs. We developed an algorithm to assess the risk of obesity using the individual's current activity and calorie expenditure. The algorithm was deployed on a smartphone application to collect wearable device data, and user reported data. Based on the collected data, the proposed application assesses the risk of obesity/overweight, measures the current activity level and recommends an optimized calorie plan.
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Background: Amomi fructus (AF) (a dried fruit of Amomum villosum Lour.) has been used in the treatment of digestive diseases such as abdominal pain and dysentery and in the prevention of abortion. The active ingredient of AF is its... more
Background: Amomi fructus (AF) (a dried fruit of Amomum villosum Lour.) has been used in the treatment of digestive diseases such as abdominal pain and dysentery and in the prevention of abortion. The active ingredient of AF is its volatile oil. The volatile oil contains bornyl acetate and (1R,4R)-(+)-camphor, which are the primary active ingredients of AF that are analyzed for the quality assessment. Therefore, it is important to find an accurate and easy method to analyze the aforementioned volatile components of AF. Materials and Methods: In this study, 8 samples (A1–A4, B5 and B6, and C7 and C8) were collected and divided into Grades A, B, and C, respectively. The characteristics of volatile oils (the aroma) in these samples were analyzed using an electronic nose (E-nose) and a gas chromatography–mass spectrometry. In this study, we proposed a bionic olfactory system based on E-nose technology combined with a convolutional neural network algorithm for component identification. This system can qualitatively evaluate AF from different quality grades and quantitatively predict the contents of the two aforementioned primary chemical components. Results: The accuracy of qualitative identification was over 95% for Grade A samples and over 90% for Grade B and Grade C samples. Discussion: Based on our identification of the quality, Grade A samples were detected with an accuracy of 86.7%. However, Grade B and C samples were identified with lower accuracies (80% and 73.3%, respectively). Conclusion: The identification of quality of AF was successfully evaluated by two primary volatile components: bornyl acetate and (1R,4R)-(+)-camphor. The bionic olfactory system combined with an appropriate prediction model might be used as a potential quality control tool for Chinese herbal medicines.
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Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which increases the chance of early diagnosis and improve the rate of survival for people diagnosed with hypertension and Cardiovascular diseases... more
Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which increases the chance of early diagnosis and improve the rate of survival for people diagnosed with hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data are challenging. This research is aimed to address this challenge by proposing a deep learning technique, convolutional neural network (CNN), to estimate the systolic blood pressure (SBP) using electrocardiogram (ECG) and photoplethysmography (PPG) signals. Two different methods are investigated and compared in this research. In the first method, continuous wavelet transform (CWT) and CNN have been employed to estimate the SBP. For the second method, we used random sampling within the stochastic gradient descent (SGD) optimization of CNN and the raw ECG and PPG signals for training the network. The Medical Information Mart for Intensive Care (MIMIC III) database is used for both methods, which split to two parts, 70% for training our network and the remaining used for testing the performance of the network. Both methods are capable of learning how to extract relevant features from the signals. Therefore, there is no need for engineered feature extraction compared to previous works. Our experimental results show high accuracy for both CNN-based methods which make them promising and reliable architectures for SBP estimation.
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Abstract The fragrance retention grades (FRGs) of monomer flavors contribute significantly to the perfumer technology development. In silico prediction of FRGs of monomer flavors are required to reduce costs, time, and manual testing.... more
Abstract The fragrance retention grades (FRGs) of monomer flavors contribute significantly to the perfumer technology development. In silico prediction of FRGs of monomer flavors are required to reduce costs, time, and manual testing. Quantitative structure-property relationships (QSPR) were established employing a database of monomer flavors, including 1552 odorants and corresponding FRGs. Molecular structure physicochemical information of the odorant molecules was acquired using a molecular calculation software (Dragon 7.0). To obviate the challenge of high dimensionality, we employed five feature extractors, including principal component analysis, lasso, recursive feature elimination, autoencoder, and boruta algorithm. Moreover, three machine learning algorithms were applied and compared to develop QSPR models for the estimation of the FRGs of monomer flavors. The selected machine learning algorithms were random forest, support vector machine, and deep neural network. We developed a weighted scoring formula for calculating the correlation score and association analysis between functional groups and FRGs. The results demonstrated that SH (thiols), ArOR (ethers), and ArCOOR (esters) functional groups have significant impact on the FRGs. In addition, we defined the applicability domains (AD) to limit the scope of application of the test dataset and used external data to validate the model reliability. Finally, we performed a comparative analysis using recursive feature elimination to extract the 80-dimensional molecular descriptors (MDs). It was concluded that the random forest algorithm performed better, with an accuracy of 77.81%, precision of 77.83%, recall of 77.99%, and F1-score of 77.88%. The proposed in silico predictive QSPR model is likely to be considered reliable for evaluating the FRGs of monomer flavors and being promoted to the perfumer industry.
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This work investigates a set of ECG data compression schemes to compare their performances in compressing and preparing ECG signals for automatic cardiac arrhythmia classification. These schemes are based on transform methods such as fast... more
This work investigates a set of ECG data compression schemes to compare their performances in compressing and preparing ECG signals for automatic cardiac arrhythmia classification. These schemes are based on transform methods such as fast Fourier transform (FFT), discrete cosine transform (DCT), wavelet transform (WT), and their combinations. Each specific transform is applied to a pre-selected data segment from the MIT-BIH database and then compression is performed in the new domain. These transformation methods are known as an important class of ECG compression techniques. The WT has been shown as the most efficient method for further improvement. A compression ratio of 7.98 to 1 has been achieved with a percent of root mean square difference (PRD) of 0.25%, indicating that the wavelet compression technique offers the best performance over the other evaluated methods.
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Background: Amomi fructus (AF Lour.) has been used to treat digestive diseases in the context of Traditional Chinese Medicine. Its aroma characteristics have been attracted attention and are considered to be effective markers for... more
Background: Amomi fructus (AF Lour.) has been used to treat digestive diseases in the context of Traditional Chinese Medicine. Its aroma characteristics have been attracted attention and are considered to be effective markers for determining AF from different habitats. Materials and Methods: In this article, the odor characteristics of AF from three different habitats were investigated and analyzed using gas chromatography-mass spectrometry (GC-MS) and an electronic nose (E-nose). Results: It was found that the E-nose in conjunction with principal component analysis as an analytic tool, showed good performance and achieved a total variance of 93.90% with the first two principal components. A total of 65 aroma constituents among three groups of AF were separated, identified, and calculated using GC-MS. It was observed that the components and the contents were clearly different among the three groups. To confirm the interrelation between aroma constituents and sensors, the contents of 12 aroma ingredients and the response values of six sensors were selected to be trained and tested using the partial least squares. A satisfied quantitative prediction was presented that the contents of selected constituents were accurately predicted by corresponding E-nose sensors with the most determination coefficient of calibration and determination coefficient of prediction of >90%. Conclusion: It was revealed that the E-nose is capable of discriminating AF from different habitats, presenting an accurate, easy-operating, and nondestructive reference approach.
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High blood pressure (BP) is one of the common risk factors for heart disease, stroke, congestive heart failure, and kidney disease. An accurate, continuous and cuffless BP monitoring technique could help clinicians improve the rate of... more
High blood pressure (BP) is one of the common risk factors for heart disease, stroke, congestive heart failure, and kidney disease. An accurate, continuous and cuffless BP monitoring technique could help clinicians improve the rate of prevention, detection, and treatment of hypertension and related diseases. Pulse transit time (PTT) has attracted interest as an index of BP changes for cuffless BP measurement techniques. Currently, PPT-based BP measurement approaches have improved and are able to relieve the discomfort associated with an inflated cuff such as that used in auscultatory and oscillometric BP measurement techniques. However, PTT can only track the BP variation in high frequency (HF) which limits the true representation of BP changes. This paper presents a continuous and cuffless BP monitoring method based on multi-parameter fusion. We used photoplethysmogram (PPG) and a two-lead electrocardiogram (ECG) and employed an algorithm based on PTT and the PPG intensity ratio (PIR) to continuously track BP in both high and low frequencies and estimate systolic and diastolic BP.
Research Interests: Mathematics, Computer Science, Cardiology, Statistics, Medicine, and 12 moreElectrocardiography, Library and Information Studies, Humans, Internal Medicine, Hypertension, Blood Pressure, Pubmed, Public health systems and services research, Photoplethysmogram, Photoplethysmography, Arrival Time, and Pulse wave analysis
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Quantitative analysis of Chinese Herbal Medicines (CHMs) is important for quality consistency evaluation. However, due to various harvesting factors and plant processing it is a challenging task. This paper presents experimental-based... more
Quantitative analysis of Chinese Herbal Medicines (CHMs) is important for quality consistency evaluation. However, due to various harvesting factors and plant processing it is a challenging task. This paper presents experimental-based evaluation of five kinds of herbs using an electronic tongue and support vector machine (SVM). It was found that the performance of SVM in classifying of these herbs was superior to other selected methods. The SVM was implemented by selecting a radial basis kernel function for data mapping and conducting a parameter optimization with the K-fold cross validation. The average accuracy of the final classification can reach to 96.67% and therefore, it is feasible to employ electronic tongue and SVM method for species identification of CHMs.
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Research Interests: Computer Science, Materials Science, Biosensors, Graphene, Nanotechnology, and 15 moreMolecular Biophysics, Medicine, Adsorption, Graphite, Gold, Biopolymers, Biosensor, Biomolecule, Immunoassay, BioSensors, Equipment Design, Equipment Failure Analysis, Biosensing Techniques, Plasmon, and Localized Surface Plasmon Resonance
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The mobile healthcare (mHealth) applications are becoming increasingly important in monitoring and delivery of healthcare interventions. They are often considered as pocket computers, due to their advanced computing features and diverse... more
The mobile healthcare (mHealth) applications are becoming increasingly important in monitoring and delivery of healthcare interventions. They are often considered as pocket computers, due to their advanced computing features and diverse capabilities. Their sophisticated sensors and advanced software applications make mHealth based applications more feasible and innovative. Advanced engineering, communication and information technologies combined with medical and clinical knowledge enable the possibility of remote, wireless, continuous monitoring of physiological parameters. These technologies facilitate the implementation of mHealth based patient monitoring and diagnostic systems virtually anywhere: home, hospital and outdoors (on the move). The proposed mHealth vital sign monitoring system in this chapter is aimed to help clinicians by illustrating the trace of critical physiological parameters, generating early warning/alerts and indicating any significant changes to the data. The system was validated with different set of collected data from 20 hospitalised older adults and achieved an accuracy of 95.83%, sensitivity of 100%, specificity of 93.15%, and predictability of 90.38% in compare with a clinician assessment for tachycardia, hypertension, hypotension, hypoxemia and hypothermia. Another important aspect of this chapter is to investigate challenges and critical issues related to the use of such applications in healthcare including reliability, efficiency, mobile phone platform variability, cost effectiveness, energy usage, user interface, quality of medical data, and security and privacy.
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Worldwide spending on long-term and chronic care conditions is increasing to a point that requires immediate interventions and advancements to reduce the burden of the healthcare cost. This research is focused on early detection of... more
Worldwide spending on long-term and chronic care conditions is increasing to a point that requires immediate interventions and advancements to reduce the burden of the healthcare cost. This research is focused on early detection of prediabetes and type 2 diabetes mellitus (T2DM) using wearable technology. An artificial intelligence model was developed based on adaptive-neuro fuzzy interference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence and calories). The data was collected using an advanced wearable body vest. The real-time data was combined with manual recordings of blood glucose, height, weight, age and sex. The model analyzed the data alongside a clinical knowledge-base. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines and protocols. The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.
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In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from... more
In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97dB and 0.02 respectively and a significantly higher correlation coefficient (p<0.05).
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High blood pressure (BP) or hypertension is the single most crucial adjustable risk factor for cardiovascular diseases (CVDs) and monitoring the arterial blood pressure (ABP) is an efficient way to detect and control the prevalence of the... more
High blood pressure (BP) or hypertension is the single most crucial adjustable risk factor for cardiovascular diseases (CVDs) and monitoring the arterial blood pressure (ABP) is an efficient way to detect and control the prevalence of the cardiovascular health of patients. Therefore, monitoring the regulation of BP during patients’ daily life plays a critical role in the ambulatory setting and the latest mobile health technology. In recent years, many studies have been conducted to explore the feasibility and performance of such techniques in the health care system. The ultimate aim of these studies is to find and develop an alternative to conventional BP monitoring by using cuff-less, easy-to-use, fast, and cost-effective devices for controlling and lowering the physical harm of CVDs to the human body. However, most of the current studies are at the prototype phase and face a range of issues and challenges to meet clinical standards. This review focuses on the description and analysis of the latest continuous and cuff-less methods along with their key challenges and barriers. Particularly, most advanced and standard technologies including pulse transit time (PTT), ultrasound, pulse arrival time (PAT), and machine learning are investigated. The accuracy, portability, and comfort of use of these technologies, and the ability to integrate to the wearable healthcare system are discussed. Finally, the future directions for further study are suggested.
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This review provides an overview of the literature on a broad range of tablet-based healthcare applications in hospital care settings. Most of the systems described are either in their development stage or simulation stage but not... more
This review provides an overview of the literature on a broad range of tablet-based healthcare applications in hospital care settings. Most of the systems described are either in their development stage or simulation stage but not actually implemented in the hospital care (exceptions to systems conducted clinical trials). Further focused research is required in such applications with respect to their clinical implementation, end-user acceptability, evaluation by medical professionals and security and privacy.
Research Interests: Health Care and Medicine
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Computer-based and wireless patient monitoring systems are emerging as a low cost, reliable and accurate way of healthcare delivery. Advanced and secure solutions such as electronic records, mobile systems and cloud computing have been... more
Computer-based and wireless patient monitoring systems are emerging as a low cost, reliable and accurate way of healthcare delivery. Advanced and secure solutions such as electronic records, mobile systems and cloud computing have been developed for healthcare. Most tele-health solutions send data or video remotely to healthcare providers but very few systems are in place for both vital signs and video connectivity in real-time. We proposed an advanced and efficient telehealth solution focusing on video conferencing (consultation) between patients and medical professionals in addition to wireless vital signs transmission. The selected vital signs include; blood pressure (systolic and diastolic), heart rate, respiratory rate, oxygen saturation, body temperature, spirometry (lung volumes) and blood glucose level.
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Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in... more
Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and detecting melanoma at an early stage. We aim to develop a novel handheld medical scanning device dedicated to early detection of melanoma at the primary healthcare with low cost and high performance. However, developing this particular device is very challenging due to the complicated computations required by the embedded diagnosis system. In this paper, we propose a hardware-friendly design for implementing an embedded system by exploiting the recent hardware advances in reconfigurable computing. The developed embedded system achieved optimized implementation results for the hardware resource utilization, power consumption, detection speed and processing time with high classification accuracy rate using real data for melanoma detection. Consequently, the proposed embedded diagnosis system meets the critical embedded systems constraints, which is capable for integration towards a cost- and energy-efficient medical device for early detection of melanoma.
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ABSTRACT Aim: The aim of this study was to investigate the effectiveness of current rapid response applications available in acute care settings for escalation of patient deterioration. Current challenges and barriers, as well as key... more
ABSTRACT Aim: The aim of this study was to investigate the effectiveness of current rapid response applications available in acute care settings for escalation of patient deterioration. Current challenges and barriers, as well as key recommendations, were also discussed. Methods: We adopted PRISMA review methodology and screened a total of 559 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2015 and 2019. The selection criteria were based on the inclusion of studies that report on the advancement made to the current practice for providing rapid response to the patient deterioration in acute care settings. Results: We found that current rapid response applications are complicated and time-consuming for detecting inpatient deterioration. Existing applications are either siloed or challenging to use, where clinicians are required to move between two or three different applications to complete an end-to-end patient escalation workflow – from vital signs collection to escalation of deteriorating patients. We found significant differences in escalation and responses when using an electronic tool compared to the manual approach. Moreover, encouraging results were reported in extensive documentation of vital signs and timely alerts for patient deterioration. Conclusion: The electronic vital signs monitoring applications are proved to be efficient and clinically suitable if they are user-friendly and interoperable. As an outcome, several key recommendations and features were identified that would be crucial to the successful implementation of any rapid response system in all clinical settings.
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Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier... more
Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International S...
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Background: Among skin cancers, melanoma is the most dangerous and aggressive form, exhibiting a high mortality rate worldwide. Biopsy and histopatholog-ical analysis are common procedures for skin cancer detection and prevention in... more
Background: Among skin cancers, melanoma is the most dangerous and aggressive form, exhibiting a high mortality rate worldwide. Biopsy and histopatholog-ical analysis are common procedures for skin cancer detection and prevention in clinical settings. A significant step involved in the diagnosis process is the deep understanding of patterns, size, color, and structure of lesions based on images obtained through dermatoscopes for the infected area. However, the manual seg-mentation of the lesion region is time-consuming because the lesion evolves and changes its shape over time which makes its prediction challenging. Moreover, at the initial stage, it is difficult to predict melanoma as it closely resembles other skin cancer types that are not malignant as melanoma, thus automatic segmentation techniques are required to design a computer-aided system for accurate and timely detection. Methods: As deep learning approaches have gained high attention in recent years due to their remarka...
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This research is aimed to develop a 10-year risk prediction model and identify key contributing Cardiovascular Disease (CVD) risk factors. A Cox proportional hazard regression method was adopted to design and develop the risk model. We... more
This research is aimed to develop a 10-year risk prediction model and identify key contributing Cardiovascular Disease (CVD) risk factors. A Cox proportional hazard regression method was adopted to design and develop the risk model. We used Framingham Original Cohort dataset of 5079 men and women aged 30 62 years, who had no overt symptoms of CVD at the baseline. Out of them, 3189 (62.78%) had an actual CVD event. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure, cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel contributing risk factors. We validated the model via statistical and empirical validation methods. The proposed model achieved an acceptable discrimination and calibration with C-index (receiver operating characteristic (ROC)) being 0.71 from the validation dataset.
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The aim of this review is to investigate barriers and challenges of Wearable Sensors (WS) and Internet-of-Things (IoT) solutions in healthcare. This work specifically focuses on falls and Activity of Daily Life (ADLs) for ageing... more
The aim of this review is to investigate barriers and challenges of Wearable Sensors (WS) and Internet-of-Things (IoT) solutions in healthcare. This work specifically focuses on falls and Activity of Daily Life (ADLs) for ageing population and independent living for older adults. The majority of the studies focussed on the system aspects of WS and IoT solutions including advanced sensors, wireless data collection, communication platforms and usability. The current studies are focused on a single use-case/health area using non-scalable and ‘silo’ solutions. Moderate to low usability/ userfriendly approach is reported in most of the current studies. Other issues found were, inaccurate sensors, battery/power issues, restricting the users within the monitoring area/space and lack of interoperability. The advancement of wearable technology and possibilities of using advanced technology to support ageing population is a concept that has been investigated by many studies. We believe, WS an...
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Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in... more
Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and detecting melanoma at an early stage. We aim to develop a novel handheld medical scanning device dedicated to early detection of melanoma at the primary healthcare with low cost and high performance. However, developing this particular device is very challenging due to the complicated computations required by the embedded diagnosis system. In this paper, we propose a hardware-friendly design for implementing an embedded system by exploiting the recent hardware advances in reconfigurable computing. The developed embedded system achieved optimized implementation results for the hardware resource utilization, power consumption, detection speed and processing time with high classification accuracy rate using real data for melanoma detection. Con...
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Currently, remote health monitoring is one of the emerging areas of healthcare directed towards a better healthcare delivery. Worldwide acceptance of such solutions is due to the use of internet and its related services; being ‘online’ at... more
Currently, remote health monitoring is one of the emerging areas of healthcare directed towards a better healthcare delivery. Worldwide acceptance of such solutions is due to the use of internet and its related services; being ‘online’ at all times and ease of communication anywhere and everywhere. Remote patient monitoring systems can be considered as low cost, reliable and accurate way of advanced healthcare delivery. Our main focus is to evaluate a remote vital sign monitoring system consists of a computer based software application and wireless medical devices and compare with other existing systems in terms of users’ acceptability, comfort, mobility and usability. We have successfully tested and evaluated the adopted system with 30 individuals for mobility, usability, comfort, and overall acceptability of the latest wireless medical devices. It is found that not all medical devices are acceptable from the users’ perspective; either they lack user centeredness or clinical useful...
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Cuff-less and continuous blood pressure (BP) measurement has recently become an active research area in the field of remote healthcare monitoring. There is a growing demand for automated BP estimation and monitoring for various long-term... more
Cuff-less and continuous blood pressure (BP) measurement has recently become an active research area in the field of remote healthcare monitoring. There is a growing demand for automated BP estimation and monitoring for various long-term and chronic conditions. Automated BP monitoring can produce a good amount of rich health data, which increases the chance of early diagnosis and treatments that are critical for a long-term condition such as hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data is challenging, which is aimed to address in this research. We employed a continuous wavelet transform (CWT) and a deep convolutional neural network (CNN) to estimate the BP. The electrocardiogram (ECG), photoplethysmography (PPG) and arterial blood pressure (ABP) signals were extracted from the online Medical Information Mart for Intensive Care (MIMIC III) database. The scalogram of each signal was created and used for training and testing o...
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This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected... more
This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected medical devices for collecting vital signs. The medical device data collected by the app includes heart rate, oxygen saturation and electrocardiograph (ECG). The collated data is streamed/displayed on the smartphone in real-time. This application was designed by adopting six screens approach (6S) mobile development framework and focused on user-centered approach and considered clinicians-as-a-user. The clinical engagement, consultations, feedback and usability of the application in the everyday practices were considered critical from the initial phase of the design and development. Furthermore, the proposed application is capable to deliver rich clinical decision support in real-time using the integrated medical device data.
Research Interests: Computer Science, Usability, Medicine, Electrocardiography, Decision support system, and 13 moreLibrary and Information Studies, Humans, Bluetooth, Pubmed, Mobile Applications, Heart rate, Feedback, Vital Signs, Public health systems and services research, Smartphone, Oximetry, Android Operating System, and Clinical Decision Support Systems
Background: Digital continuous blood pressure (BP) monitoring is increasingly being used in clinical and remote settings. Although it could significantly help clinicians in vital signs monitoring, the analyzing of such amount of BP data... more
Background: Digital continuous blood pressure (BP) monitoring is increasingly being used in clinical and remote settings. Although it could significantly help clinicians in vital signs monitoring, the analyzing of such amount of BP data is challenging. Objective: This study is aimed to investigate the feasibility of applying deep convolutional neural network (CNN) to the estimation of the systolic blood pressure (SBP) using electrocardiogram (ECG) and photo plethysmography (PPG) signals. Method: A total of 62500 ECG and PPG signals, sampled at 125 Hz, with 250 corresponding SBP, sampled at 1 Hz, were selected from Medical Information Mart for Intensive Care (MIMIC-III) Waveform Database. The collected signals from 22 subjects were divided into training (80%) and testing (20%) datasets. A CNN-based model was designed with five convolutional layers, one fully connected layer, and one regression layer to predict the SBP. Two different methods of applying data to the input of the CNN mo...