The growth in wearable medical sensor-based technologies has made it possible to capture high volume physiological data of patients, both within and outside the hospital The acquired physiological data are analyzed, usually in real-time, using a patient monitoring application for early disease detection or to detect any other changing conditions of a patient In some cases, it is desirable to have a distributed, scalable patient monitoring system to which the physiological data of different patients can be submitted for online analysis Such a system should be able to support the concurrent analysis of multiple data streams of different patients, allowing a clinician to remotely monitor more than one patient from a single location This type of system also conserves resources, since in this case, there is no need to provision computational resources for every single patient being monitored In this paper, we explore the usability of Apache Storm, an open-source real-time processing engine, in the development of such a scalable patient monitoring system The contribution of this work, therefore, is to demonstrate that it is possible to achieve a more resource-efficient alternative to the isolated patient monitoring systems by using a distributed real-time computation platform, Apache Storm, to develop a scalable health monitoring system that can support the concurrent monitoring of multiple patients To show how the proposed system can be developed, we describe a prototype implementation of a multi-tenant health monitoring application that monitors the arrhythmia status of multiple patients, based on a simple ECG analysis, using Apache Storm

A Scalable Patient Monitoring System Using Apache Storm

Transferring collected physiological data from health facilities to a cloud-based health analytical platform can be seen as an efficient and cost effective approach to provide clinical support to rural and remote healthcare centres from urban based specialists. A cloud-based healthcare platform will reduce the requirement of patient transfer due to lack of clinical experts or providing consultative support through the phone. However transferring physiological data streams through a data path with insufficient quality and unsatisfactory conditions may have negative performance impact on real-time data processing. To address this issue, we study the benefit of using software-defined networking (SDN) technology. SDN as an emerging networking paradigm, can be employed to manage and control network conditions and apply desired policies. This research introduces the significant features in SDN technology to transfer physiological data streams through an alternative path with a better quality rather than the congested predetermined shortest path in order to enhance data transfer reliability and improve real-time data processing quality.

Enhance Network Communications in a Cloud-Based Real-Time Health Analytics Platform Using SDN

Texture is one of the important characteristics used in identifying objects or regions of interest in an image. This paper describes some textural features based on integrated spatial gray level co-occurrence matrix, and illustrates the effectiveness of four textural features in categorizing ultrasound breast tumor images by means of Fuzzy C-means and K-medoid clustering algorithms respectively. The experimental identification accuracy is 72.6415 percent. These results indicate that textural features probably have a general applicability for classification of breast tumors.

Textural Feature Analysis for Ultrasound Breast Tumor Images

Critical care patients are monitored by a range of medical devices collecting high frequency data. New computing frameworks and platforms are being proposed to review and analyze the data in detail. The application of these approaches in a low resource setting is challenged by the approaches used for data acquisition. Software as a Service (SaaS) is a form of cloud computing where a cloud-based software application enables the storage, analysis and visualization of data within the cloud. A subset of SaaS is Health Analytics as a Service (HAaaS), which provides software to support health analytics in the cloud. The objective of this study is to design, implement, and demonstrate an extendable big-data compatible HAaaS framework that offers both real-time and retrospective analysis where data acquisition is not tightly coupled. A data warehousing framework is presented to facilitate analysis within a low resource setting. The framework has been instantiated in the Artemis platform within the context of the Belgaum Children Hospital (BCH) case study. Initial end-to-end testing with the Nellcor monitor (bedside monitor at BCH), which was not connected to any human, was completed. This testing confirms the functionality of the new Artemis cloud instance to receive data from test device using an alternate data acquisition approach.

/pdf/a-cloud-based-big-data-health-analytics-as-a-service-ng4ws9w7dz.pdf

A Cloud Based Big Data Health-Analytics-as-a-Service Framework to Support Low Resource Setting Neonatal Intensive Care Unit

Opportunities exist to improve healthcare delivery by extending to the domain of mobile healthcare the emerging clinical support system that facilitates patient monitoring and early disease detection Current research focuses on hospitallevel patient monitoring using an automated clinical support system A similar system can be adapted to support medical monitoring and remote care for mobile patients Unlike the hospital environment where stationary computing infrastructures can be leveraged for the medical data acquisition, processing and storage, the mobile healthcare environment may rely solely on mobile devices, such as a smartphone, to acquire and process the medical data Given that the mobile devices are constrained by energy, processing and storage capabilities, outsourcing some of the operations to the cloud is a plausible approach Cloud-assisted clinical support system for mobile patients creates more opportunities for healthcare delivery, but there are attendant challenges that must be considered in the development of the system This paper identifies these opportunities and the challenges that exist with the development of cloud-assisted clinical support system for patient monitoring and disease detection in mobile environments, and introduces an architecture for developing the system, with a proof of concept

An Architecture for Cloud-Assisted Clinical Support System for Patient Monitoring and Disease Detection In Mobile Environments

We present a method for transmission and processing of real-time trans-continental medical data streams. We apply fundamentals of existing network technologies to create a secure tunnel from a remote hospital through an open-network to the Artemis Cloud. We capture and store incoming 1Hz data stream in our real-time event stream processor to allow for online real-time monitoring of the patient status. The contributions of this paper extend the Critical Care as a Service paradigm by incorporating remote monitoring centers. The results establish feasibility of the system to support real-time monitoring. However, existing protocols were required significant optimization to account for variability in throughput and availability of the network.

Cloud framework for real-time synchronous physiological streams to support rural and remote Critical Care

A computerized classification based on morphologic and texture features is proposed to increase the accuracy of the ultrasonic diagnosis of breast tumors. Firstly, tumor boundaries are obtained with the gray-level threshold segmentation algorithm and the dynamic programming method. Then five morphologic features and two texture features are extracted. Finally, an artificial neural network with the error back propagation algorithm is applied to classify breast tumors as benign or malignant. Experiments on 168 cases show that the proposed system yields the high accuracy, sensitivity and specificity. Therefore, it is concluded that this system performs well in the ultrasonic classification of breast tumors.

Computerized Classification of Breast Tumors with Morphologic and Texture Features of Ultrasonic Images

Objective: To assess the feasibility and accuracy of Doppler audio-signal Processing system (DASPS) in noninvasive automatic estimation of pulmonary vascular resistance(PVR). Methods: PVR was estimated with DASPS in 32 children with left-to-right shunting congenital heart disease, compared with those estimated by Doppler echocardiography and cardiac catheterization. Results: PVR estimated by DASPS adopting aorta, pulmonary artery, pulmonary venous and mitral flow time intervals highly correlated with those measured by cardiac catheterization (r=0.93, p<0.001), better than those measured by Doppler echocardiography (r=0.79, p<0.01). The time for estimating PVR with DASPS was shorter than with Doppler echocardiography (7.1±±2.3 min vs 57.5±±16.5min, p<0.05), the repeatability for DASPS was better than for Doppler echocardiography (p<0.05). Conclusion: PVR could be accurately quantitatively, noninvasively, auto-estimated by DASPS.

Study on Noninvasive Auto-Estimation of Pulmonary Vascular Resistance by Doppler Audio-Signal Processing System

The objective of this paper is to assess the accuracy of audio signal Processing system in noninvasive automatic estimation of Pulmonary arterial pressure(PAP). Comparative studies among Doppler echocardiography, audio signal processing system (ASPS) and cardiac catheterization for predicting PAP have been done in 51 children with left-to-right shunting congenital heart disease. Estimated pulmonary artery systolic and mean pressure adopting modified pulmonary to aortic flow time interval ratios (F
 PA
/F
 AO
) by ASPS correlated highly with those measured by cardiac catheterization (r = 0.95,0.96, p < 0.001), were correlated better than those measured by Doppler echocardiography (r = 0.86,0.86, p < 0.001). PAP can be estimated noninvasively adopting modified F
 PA
/F
 AO
 by ASPS.

Study on Noninvasive Auto-Estimation of Pulmonary Arterial Pressures by Doppler Echocardiography Audio Signal Processing System

Hemodynamic parameters of the pulmonary circulation are important reference factors in the diagnoses and treatments of children 's cardiovascular diseases. These parameters are usually measured by the cardiac catheterization. Recently several noninvasive methods have been proposed to estimate these parameters using the ultrasound technique. In order to execute the noninvasive estimation automatically, several signal and image processing algorithms are applied to obtain the feature points of the Doppler spectrogram and ECG signal, from which hemodynamic parameters can be calculated based on empirical formulas. To combine all the processes and simplify the manipulation, a computer-based system is designed and tested for sixty children with congenital heart diseases. Experimental results show that parameters estimated using this system highly correlate to the clinic ones measured by the cardiac catheterization. For its noninvasive and automatic advantage, this system may be used as an assistant tool in the diagnoses and treatments of children's cardiovascular diseases

A Noninvasive Auto-Estimation System for Hemodynamic Parameters of Pulmonary Circulation

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