Performances of three minimally invasive cardiac output monitoring systems

We evaluated cardiac output (CO) using three new methods – the auto-calibrated FloTrac–Vigileo (CO_ed), the non-calibrated Modelflow (CO_mf ) pulse contour method and the ultra-sound HemoSonic system (CO_hs) – with thermodilution (CO_td) as the reference. In 13 postoperative cardiac surgical patients, 104 paired CO values were assessed before, during and after four interventions: (i) an increase of tidal volume by 50%; (ii) a 10 cm H₂O increase in positive end-expiratory pressure; (iii) passive leg raising and (iv) head up position. With the pooled data the difference (bias (2SD)) between CO_ed and CO_td, CO_mf and CO_td and CO_hs and CO_td was 0.33 (0.90), 0.30 (0.69) and −0.41 (1.11) l.min⁻¹, respectively. Thus, Modelflow had the lowest mean squared error, suggesting that it had the best performance. CO_ed significantly overestimates changes in cardiac output while CO_mf and CO_hs values are not significantly different from those of CO_td. Directional changes in cardiac output by thermodilution were detected with a high score by all three methods.

Development and application of electroanalytical field-based chemical monitoring systems

The development and application of a novel combination of electrochemical techniques and computerized field-based battery-operated instrumentation is investigated. Low-power complementary metal oxide semiconductor technology has been utilized for the development of the field data acquisition systems and the instrumental performance of the complete analytical system is critically evaluated.

Security considerations for wireless carrier agonistic bio-monitoring systems

Advances in information and communications technology has led to a significant advances in noncontact portable devices capable of monitoring vital signals of patients. These wearable and implantable bio-monitoring systems allow collections of wearable sensors to be constructed as a Body Area Network (BAN) to record biological data for a subject. Such systems can be used to improve the quality of life and treatment outcomes for patients. One of the main uses for a bio-monitoring system is to record biological data values from a subject and provide them to a doctor or other medical professional. However, wearable bio-monitoring systems raise unique security considerations. In this paper, we discuss some of the security considerations that have arisen in our work around communications agnostic bio-monitoring, and how we have addressed these concerns. Furthermore, the issues related to the identifying and trusting sender and receiver entities are discussed.

An intelligent forex monitoring system

The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. We attempt to compare the performance of a Takagi-Sugeno, type neuro-fuzzy system and a feedforward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. We considered the exchange values of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that the neuro-fuzzy technique performed better than the neural network

Anaesthesia monitoring using fuzzy logic

Objective. Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. Method. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of offline tests. The training and testing data set are selected randomly from 30 sets of patients' data. Results. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. Conclusion. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.

Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics

Stroke is a common neurological condition which is becoming increasingly common as the population ages. This entails healthcare monitoring systems suitable for home use, with remote access for medical professionals and emergency responders. The mobile phone is becoming the easy access tool for self-evaluation of health, but it is hindered by inherent problems including computational power and storage capacity. This research proposes a novel cloud based architecture of a biomedical system for a wearable motion kinematic analysis system which mitigates the above mentioned deficiencies of mobile devices. The system contains three subsystems: 1. Bio Kin WMS for measuring the acceleration and rotation of movement 2. Bio Kin Mobi for Mobile phone based data gathering and visualization 3. Bio Kin Cloud for data intensive computations and storage. The system is implemented as a web system and an android based mobile application. The web system communicates with the mobile application using an encrypted data structure containing sensor data and identifiable headings. The raw data, according to identifiable headings, is stored in the Amazon Relational Database Service which is automatically backed up daily. The system was deployed and tested in Amazon Web Services.

Monitoring and accountability for the Pacific response to the non-communicable diseases crisis

BACKGROUND: Non-communicable diseases (NCD) are the leading cause of premature death and disability in the Pacific. In 2011, Pacific Forum Leaders declared "a human, social and economic crisis" due to the significant and growing burden of NCDs in the region. In 2013, Pacific Health Ministers' commitment to 'whole of government' strategy prompted calls for the development of a robust, sustainable, collaborative NCD monitoring and accountability system to track, review and propose remedial action to ensure progress towards the NCD goals and targets. The purpose of this paper is to describe a regional, collaborative framework for coordination, innovation and application of NCD monitoring activities at scale, and to show how they can strengthen accountability for action on NCDs in the Pacific. A key component is the Dashboard for NCD Action which aims to strengthen mutual accountability by demonstrating national and regional progress towards agreed NCD policies and actions.

DISCUSSION: The framework for the Pacific Monitoring Alliance for NCD Action (MANA) draws together core country-level components of NCD monitoring data (mortality, morbidity, risk factors, health system responses, environments, and policies) and identifies key cross-cutting issues for strengthening national and regional monitoring systems. These include: capacity building; a regional knowledge exchange hub; innovations (monitoring childhood obesity and food environments); and a robust regional accountability system. The MANA framework is governed by the Heads of Health and operationalised by a multi-agency technical Coordination Team. Alliance membership is voluntary and non-conditional, and aims to support the 22 Pacific Island countries and territories to improve the quality of NCD monitoring data across the region. In establishing a common vision for NCD monitoring, the framework combines data collected under the WHO Global Framework for NCDs with a set of action-orientated indicators captured in a NCD Dashboard for Action.

SUMMARY : Viewing NCD monitoring as a multi-component system and providing a robust, transparent mutual accountability mechanism helps align agendas, roles and responsibilities of countries and support organisations. The dashboard provides a succinct communication tool for reporting progress on implementation of agreed policies and actions and its flexible methodology can be easily expanded, or adapted for other regions.

Carbon dioxide levels and ventilation strategies in 'green' office buildings

Energy efficient office buildings are intended to provide a comfortable and healthy environment for their occupants as well as reducing the energy consumption of the building. They are often designed as "showcase" buildings illustrating the potential for savings through some innovative design technology. But do such buildings actually deliver the desired energy savings and satisfactory comfort conditions for occupants? Measurements of a "green" University campus building in Victoria, Australia, designed with an innovative fabric energy storage system, demonstrate that the ventilation system is not providing acceptable indoor air quality conditions. The design strategies used to reduce energy consumption have had negative consequences on the air quality of the building. Insufficient fresh air is being drawn into the building leading to an excessive build up of carbon dioxide. It is recommended that monitoring systems need to use a wider range of measurements than temperature alone to guarantee good quality indoor air and working conditions and that commissioning of buildings should include adequate monitoring of the operational performance of the building. Designers need to be made aware of the potential consequences of their decisions when attempting innovative energy-efficient designs.

Session initiation protocol (SIP) based event notification system architecture for telemedicine applications

Mobility service for hospital technicians involved in telemedicine applications is one of the key issues in providing more flexible and efficient in-house or remote health care services. Today, the Internet based communication has widened the opportunity of event monitoring systems in the medical field. The session initiation protocol (SIP) can work on a variety of devices and can be used to create a medical event notification system. Its adoption as the protocol of choice for third generation wireless networks allows for a robust and scalable environment. One of the advantages of SIP is that it supports personal mobility through the separation of user addressing and device addressing. In this paper, the authors propose a possible solution framework for telemedicine alert notification system based SIP-specific event notification.

Australian forex market anaylsis using connectionist models

The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The forex market is difficult to understand by an average individual. However, once the market is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. This paper is an attempt to compare the performance of a Takagi-Sugeno type neuro-fuzzy system and a feed forward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. The exchange values of Australian dollar are considered with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pound. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that neuro-fuzzy technique performed better than the neural network.

AdaBoost.MRF: boosted Markov random forests and application to multilevel activity recognition

Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maximum likelihood is known to be computationally challenging (except for extreme cases), and thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. Distinct from most existing work, our algorithm can handle hidden variables (missing labels) and is particularly attractive for smarthouse domains where reliable labels are often sparsely observed. Furthermore, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithm to a home video surveillance application and demonstrate its efficacy.

Synthetic evaluation method for bridge priority ranking

In order to maintain the transportation operation, proper monitoring systems should be established on road structures, especially bridges. Since these systems need enormous investments, only a part of bridges should be equipped. Thus, the priorities of the bridges should be ranked. In this paper, a method based on two-level synthetic evaluation is proposed. First, the importance of each bridge is analyzed through the economic analysis. Six factors are considered for the bridges in a network, including construction cost, service duration, length, location importance coefficient, traffic volume, and reconstruction time. Second, the safety condition of the bridge is evaluated by using improved entropy method (IEM) which combines subjective weight with objective entropy weight. Five indices are incorporated in this step, i.e., design and construction condition, technical condition, level of overloading, hazard of wind and earthquake and environmental factors. Finally, the priorities of all the bridge in one network can be ranked and classified through a judge matrix. To demonstrate the effectiveness of the proposed method, a main highway including 16 bridges is taken as an illustrative example. The results show that the bridges can be ranked and classified quickly by using the proposed method.

Online social capital : mood, topical and psycholinguistic analysis

Social media provides rich sources of personal information and community interaction which can be linked to aspect of mental health. In this paper we investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and authors' mood, of a large corpus of blog posts, to analyze the aspect of social capital in social media communities. Using data collected from Live Journal, we find that bloggers with lower social capital have fewer positive moods and more negative moods than those with higher social capital. It is also found that people with low social capital have more random mood swings over time than the people with high social capital. Significant differences are found between low and high social capital groups when characterized by a set of latent topics and psycholinguistic features derived from blogposts, suggesting discriminative features, proved to be useful for classification tasks. Good prediction is achieved when classifying among social capital groups using topic and linguistic features, with linguistic features are found to have greater predictive power than latent topics. The significance of our work lies in the importance of online social capital to potential construction of automatic healthcare monitoring systems. We further establish the link between mood and social capital in online communities, suggesting the foundation of new systems to monitor online mental well-being.

Progress on obesity prevention over 20 years in Australia and New Zealand

The lessons learned from over 20 years of obesity prevention efforts in Australia and New Zealand are presented. The obesity epidemic started in the 1980s but poor monitoring systems meant the rise in obesity prevalence initially went undetected. In the 1990s, experts started advocating for government action; however, it was the rapid increase in media reports on obesity in the early 2000s which created the pressure for action. Several, comprehensive reports produced some programme investment but no regulatory policies were implemented. The powerful food industry lobby ensured this lack of policies on front-of-pack food labelling, restrictions on unhealthy food marketing to children, or taxes on unhealthy foods. The New Zealand government even backpedalled by rescinding healthy school food guidelines and withdrawing funding for the comprehensive national obesity strategy. In 2007, Australian Governments started a major long term-investment in preventive health in order to improve economic productivity. Other positive initiatives, especially in Australia, were: the establishment of several advocacy organizations; successful, long-term, whole-of-community projects reducing childhood obesity; a national knowledge exchange system for practitioners; and some innovative programmes and social marketing. However, despite multiple reports and strong advocacy, key recommended regulatory policies remain unimplemented, largely due to the private sector interests dominating public policy development.

An evaluation of API calls hooking performance

An open research question in malware detection is how to accurately and reliably distinguish a malware program from a benign one, running on the same machine. In contrast to code signatures, which are commonly used in commercial protection software, signatures derived from system calls have the potential to form the basis of a much more flexible defense mechanism. However, the performance degradation caused by monitoring systems calls could adversely impact the machine. In this paper we report our experimental experience in implementing API hooking to capture sequences of API calls. The loading time often common programs was benchmarked with three different settings: plain, computer with antivirus and computer with API hook. Results suggest that the performance of this technique is sufficient to provide a viable approach to distinguishing between benign and malware code execution