Platform independent web-based telecardiology for connected heart care

Most of the commercial telecardiology systems are platform-dependent and operating system (OS)-dependent. This causes inconvenience to medical officer for retrieving data from database and hence reduce the work efficiency. In this paper, a platformindependent and OS-independent web-based telecardiology system, named VirtualDave System, is proposed based on client-server model and developed in Hypertext Markup Language 5 (HTML5), Active Server Pages (ASP) scripting and C# languages. This system allows users to log on and access the patient medical data from any technology devices that equipped with web browser and internet access. Besides, it also allows targeted users to communicate and get remote medical consultation without long distance traveling and long-time queuing. Verification result shows that this proposed system could be executed in any platform regardless the OS. This web-based telecardiology could significantly help to improve the health care services especially in rural area

Efficient QRS complex detection algorithm implementation on SOC-based embedded system

This paper studies two different Electrocardiography ( ECG ) preprocessing algorithms , namely Pan and Tompkins (PT) and Derivative Based (DB) algorithm, which is crucial of QRS complex detection in cardiovascular disease detection . Both algorithms are compared in terms of QRS detection accuracy and computation timing performance , with implementation on System - on - C hip (SoC) based embedded system that prototype on Altera DE2 - 115 Field Programmable Gate Array (FPGA) platform as embedded software . Both algorithm s are tested with 30 minutes ECG data from each of 48 different patient records obtain from MIT - BIH arrhythmia database. Results show that PT algorithm achieve 98.15% accuracy with 56. 33 seconds computation while DB algorithm achieve 96.74% with only 22. 14 seconds processing time. Based on the study, an optimized PT algorithm with improvement on Moving Windows Integrator (MWI) has been proposed to accelerate its computation. Result show s that the proposed optimized Moving Windows Integrator algorithm achieve s 9.5 times speed up than original MWI while retaining its QRS detection accuracy

ARRHYTHMIA DETECTION BASED ON HERMITE POLYNOMIAL EXPANSION AND MULTILAYER PERCEPTRON ON SYSTEM-ON-CHIP IMPLEMENTATION

ABSTRACT As the number of health issues caused by heart problems is on the rise worldwide, the need for an efficient and portable device for detecting heart arrhythmia is needed. This work proposes a Premature Ventricular Contraction detection system, which is one of the most common arrhythmia, based on Hermite Polynomial Expansion and Artificial Neural Network Algorithm. The algorithm is implemented as a System-On-Chip on Altera DE2-115 FPGA board to form a portable, lightweight and cost effective biomedical embedded system to serve for arrhythmia screening and monitoring purposes. The complete Premature Ventricular Contraction classification computation includes pre-processing, segmentation, morphological information extraction based on Hermite Polynomial Expansion and classification based on artificial Neural Network algorithm. The MIT-BIH Database containing 48 patients' ECG records was used for training and testing purposes and Multilayer Perceptron training is performed using back propagation algorithm. Results show that the algorithm can detect the PVC arrhythmia for 48 different patients with 92.1% accuracy

Energy-Aware Network-on-Chip Application Mapping Based on Domain Knowledge Genetic Algorithm

This paper addresses energy-aware application mapping for large-scale Network-on-chip (NoC). The increasing number of intellectual property (IP) cores in multi-processor system-on-chips (MPSoCs) makes NoC application mapping more challenging to find optimum core-to-topology mapping. This paper proposes an application mapping technique that incorporates domain knowledge into genetic algorithm (GA) to minimize the energy consumption of NoC communication. The GA is initialized with knowledge on network partition whereas the genetic crossover operator is guided with inter-core communication demands. NoC energy estimation is based on analytical energy model and cycle-accurate Noxim simulation. For large-scale NoC, application mapping using knowledge-based genetic operator saves up to 28% energy compared to the one on conventional GA. Adding knowledge-based initial mapping speeds up convergence by 81% and further saves energy by 5% compared to only knowledge-based crossover GA. Furthermore, cycle-accurate simulations of applications with traffic dependency show the effectiveness of the proposed application mapping for large-scale NoC

Configurable Version Management Hardware Transactional Memory for Multi-processor Platform

Programming on a shared memory multi-processor platforms in an efficient way is difficult as locked based synchronization limits the efficiency. Transactional memory (TM) is a promising approach in creating an abstraction layer for multi-threaded programming. However, the performance of TM is application-specific. In general, the configuration of a TM is divided into version management and conflict management. Each scheme has its strengths and weaknesses depending on executing application. Previous TM implementations for embedded system were built on fixed version management configuration which results in significant performance loss when transaction behaviour changes. In this paper, we propose a hardware transactional memory (HTM) with interchangeable version management. Random requests at different contention levels are used to verify the performance of the proposed TM. The proposed architecture is targeted for embedded applications and is area-efficient compared to current implementations that apply cache coherence protocols

A vertical handover management for mobile telemedicine system using heterogeneous wireless networks

Application of existing mobile telemedicine system is restricted by the imperfection of network coverage, network capacity, and mobility. In this paper, a novel telemedicine based handover decision making (THODM) algorithm is proposed for mobile telemedicine system using heterogeneous wireless networks. The proposed algorithm select the best network based on the services requirement to ensure the connected or targeted network candidate has sufficient capacity for supporting the telemedicine services. The simulation results show that the proposed algorithm minimizes the number of unnecessary handover to WLAN in high speed environment. The throughput achieved by the proposed algorithm is up to 75% and 205% higher than Cellular and RSS based schemes, respectively. Moreover, the average data transmission cost of THODM algorithm is 24% and 69.2% lower than the Cellular and RSS schemes. The proposed algorithm minimizes the average transmission cost while maintaining the telemedicine service quality at the highest level in high speed environment

A novel and reliable framework of patient deterioration prediction in Intensive Care Unit based on long short-term memory-recurrent neural network

The clinical investigation explored that early recognition and intervention are crucial for preventing clinical deterioration in patients in Intensive Care units (ICUs). Deterioration of patients is predictable and can be preventable if early risk factors are recognized and developed in the clinical setting. Timely detection of deterioration in ICU patients may also lead to better health management. In this paper, a new model was proposed based on Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) to predict deterioration of ICU patients. An optimisation model based on a modified genetic algorithm (GA) has also been proposed in this study to optimize the observation window, prediction window, and the number of neurons in hidden layers to increase accuracy, AUROC, and minimize test loss. The experimental results demonstrate that the prediction model proposed in this study acquired a significantly better classification performance compared with many other studies that used deep learning models in their works. Our proposed model was evaluated for two tasks: mortality and sudden transfer of patients to ICU. Our results show that the proposed model could predict deterioration before one hour of onset and outperforms other models. In this study, the proposed predictive model is implemented using the state-of-the-art graphical processing unit (GPU) virtual machine provided by Google Colaboratory. Moreover, the study uses a novel time-series approach, which is minute-by-minute. This novel approach enables the proposed model to obtain highly accurate results (i.e., an AUROC of 0.933 and an accuracy of 0.921). This study utilizes the individual and combined effectiveness of different types of variables (i.e., vital signs, laboratory measurements, GCS, and demographic data). In this study, data was extracted from MIMIC-III database. The ad-hoc frameworks proposed by previous studies can be improved by the novel and reliable prediction framework proposed in this research, which will result in predictions of more accurate performance. The proposed predictive model could reduce the required observation window (i.e., a reduction of 83%) for the prediction task while improving the performance. In fact, the proposed significant small size of observation window could obtain higher results which outperformed all previous works that utilize different sizes of observation window (i.e., 48 hours and 24 hours). Moreover, this research demonstrates the ability of the proposed predictive model to achieve accurate results (>80%) on 'raw' data in an experimental work. This shows that the rule-based pre-processing of clinical features is unnecessary for deep learning predictive models

User-centric based vertical handover decision algorithm for telecardiology application in heterogeneous networks

The traditional telecardiology system which is integrated with a single wireless technology is unable to guarantee the patient always get connected to the telecardiology service provider. To overcome this problem, an adaptive user-centric based vertical handover algorithm is proposed to allow the telecardiology system operates in heterogeneous wireless technologies. The proposed algorithm guarantees the quality of service and maintains the user’s satisfaction at the highest level. The algorithm was compared with traditional quality of service based and cost based vertical handover algorithms. The results show that proposed algorithm is performed better than the traditional algorithms

Autonomous network selection strategy for telecardiology application in heterogeneous wireless networks

Existing telecardiology systems are mostly relying on a high bandwidth wireless technology. However, in developing countries, the coverage of high bandwidth wireless network is still imperfect. Thus, the existing telecardiology systems are unable to guarantee users are always connected to the healthcare service provider at anywhere. To overcome this issue, an autonomous network selection strategy for telecardiology application in heterogeneous wireless networks is proposed. This strategy is aware of user velocity, network quality, and telecardiology service setting (e.g. image, vital signs, ECG, etc.). It performs handover from one network to another without disruption to the link. The simulation results show that the proposed strategy outperforms conventional bandwidthbased strategy in term of handover rate, ping-pong effect and handover failure. It has successfully reduced the handover rate up to 97%, eliminated the ping-pong effect and handover failure in both high and low speed scenarios