Process of designing robust, dependable, safe and secure software for medical devices: Point of care testing device as a case study

This article has been made available through the Brunel Open Access Publishing Fund.Copyright © 2013 Sivanesan Tulasidas et al. This paper presents a holistic methodology for the design of medical device software, which encompasses of a new way of eliciting requirements, system design process, security design guideline, cloud architecture design, combinatorial testing process and agile project management. The paper uses point of care diagnostics as a case study where the software and hardware must be robust, reliable to provide accurate diagnosis of diseases. As software and software intensive systems are becoming increasingly complex, the impact of failures can lead to significant property damage, or damage to the environment. Within the medical diagnostic device software domain such failures can result in misdiagnosis leading to clinical complications and in some cases death. Software faults can arise due to the interaction among the software, the hardware, third party software and the operating environment. Unanticipated environmental changes and latent coding errors lead to operation faults despite of the fact that usually a significant effort has been expended in the design, verification and validation of the software system. It is becoming increasingly more apparent that one needs to adopt different approaches, which will guarantee that a complex software system meets all safety, security, and reliability requirements, in addition to complying with standards such as IEC 62304. There are many initiatives taken to develop safety and security critical systems, at different development phases and in different contexts, ranging from infrastructure design to device design. Different approaches are implemented to design error free software for safety critical systems. By adopting the strategies and processes presented in this paper one can overcome the challenges in developing error free software for medical devices (or safety critical systems).Brunel Open Access Publishing Fund

Breathers in the weakly coupled topological discrete sine-Gordon system

Existence of breather (spatially localized, time periodic, oscillatory) solutions of the topological discrete sine-Gordon (TDSG) system, in the regime of weak coupling, is proved. The novelty of this result is that, unlike the systems previously considered in studies of discrete breathers, the TDSG system does not decouple into independent oscillator units in the weak coupling limit. The results of a systematic numerical study of these breathers are presented, including breather initial profiles and a portrait of their domain of existence in the frequency-coupling parameter space. It is found that the breathers are uniformly qualitatively different from those found in conventional spatially discrete systems.Comment: 19 pages, 4 figures. Section 4 (numerical analysis) completely rewritte

Quantum affine Toda solitons

We review some of the progress in affine Toda field theories in recent years, explain why known dualities cannot easily be extended, and make some suggestions for what should be sought instead.Comment: 16pp, LaTeX. Minor revision

Profiling Users in the Smart Grid

The implementation of the smart grid brings with it many new components that are fundamentally different to traditional power grid infrastructures. The most important addition brought by the smart grid is the application of the Advanced Metering Infrastructure (AMI). As part of the AMI, the smart meter device provides real time energy usage about the consumer to all of the smart grids stakeholders. Detailed statistics about a consumer’s energy usage can be accessed by the end user, utility companies and other parties. The problem, however, is in how to analyse, present and make best use of the data. This paper focuses on the data collected from the smart grid and how it can be used to detect abnormal user behaviour for energy monitoring applications. The proposed system employs a data classification technique to identify irregular energy usage in patterns generated by smart meters. The results show that it is possible to detect abnormal behaviour with an overall accuracy of 99.45% with 0.100 for sensitivity, 0.989 for specificity and an error of 0.006 using the LDC classifier

Nonstatistical dynamics on potentials exhibiting reaction path bifurcations and valley-ridge inflection points

We study reaction dynamics on a model potential energy surface exhibiting post-transition state bifurcation in the vicinity of a valley ridge inflection point. We compute fractional yields of products reached after the VRI region is traversed, both with and without dissipation. It is found that apparently minor variations in the potential lead to significant changes in the reaction dynamics. Moreover, when dissipative effects are incorporated, the product ratio depends in a complicated and highly non-monotonic fashion on the dissipation parameter. Dynamics in the vicinity of the VRI point itself play essentially no role in determining the product ratio, except in the highly dissipative regime.Comment: 33 pages, 10 figures, corrected the author name in reference [6

Smart Monitoring: An Intelligent System to Facilitate Health Care across an Ageing Population

In the UK, the number of people living with self-limiting conditions, such as Dementia, Parkinson’s disease and depression, is increasing. The resulting strain on national healthcare resources means that providing 24-hour monitoring for patients is a challenge. As this problem escalates, caring for an ageing population will become more demanding over the next decade. Our research directly proposes an alternative and cost effective method for supporting independent living that offers enhancements for Early Intervention Practices (EIP). In the UK, a national roll out of smart meters is underway, which enable detailed around-the-clock monitoring of energy usage. This granular data captures detailed habits and routines through the users’ interactions with electrical devices. Our approach utilises this valuable data to provide an innovative remote patient monitoring system. The system interfaces directly with a patient’s smart meter, enabling it to distinguish reliably between subtle changes in energy usage in real-time. The data collected can be used to identify any behavioural anomalies in a patient’s habit or routine, using a machine learning approach. Our system utilises trained models, which are deployed as web services using cloud infrastructures, to provide a comprehensive monitoring service. The research outlined in this paper demonstrates that it is possible to classify successfully both normal and abnormal behaviours using the Bayes Point Machine binary classifier

A Smart Health Monitoring Technology

With the implementation of the Advanced Metering Infrastructure (AMI), comes the opportunity to gain valuable insights into an individual’s daily habits, patterns and routines. A vital part of the AMI is the smart meter. It enables the monitoring of a consumer’s electricity usage with a high degree of accuracy. Each device reports and records a consumer’s energy usage readings at regular intervals. This facilitates the identification of emerging abnormal behaviours and trends, which can provide operative monitoring for people living alone with various health conditions. Through profiling, the detection of sudden changes in behaviour is made possible, based on the daily activities a patient is expected to undertake during a 24-hour period. As such, this paper presents the development of a system which detects accurately the granular differences in energy usage which are the result of a change in an individual’s health state. Such a process provides accurate monitoring for people living with self-limiting conditions and enables an early intervention practice (EIP) when a patient’s condition is deteriorating. The results in this paper focus on one particular behavioural trend, the detection of sleep disturbances; which is related to various illnesses, such as depression and Alzheimer’s. The results demonstrate that it is possible to detect sleep pattern changes to an accuracy of 95.96% with 0.943 for sensitivity, 0.975 for specificity and an overall error of 0.040 when using the VPC Neural Network classifier. This type of behavioral detection can be used to provide a partial assessment of a patient’s wellbeing

Identifying Behavioural Changes for Health Monitoring Applications using the Advanced Metering Infrastructure

The rising demand for health and social care, and around the clock monitoring services, is increasing and are unsustainable under current care provisions and legislation. Consequently, a safe and independent living environment is hard to achieve; yet the detection of sudden or worsening changes in a patient’s condition is vital for early intervention. The use of smart technologies in primary care delivery is increasing significantly. However, substantial research gaps remain in non-invasive and cost effective monitoring technologies. Where such technologies are used, they are considered too intrusive and often incapable of being personalised to the individual needs of patients. The inability to learn the unique characteristics of patients and their conditions seriously limits the effectiveness of most current solutions. The smart metering infrastructure provides new possibilities for a variety of emerging applications that are unachievable using the traditional energy grid. Between now and 2020, UK energy suppliers will install and configure of 50 million smart meters therefore providing access to a highly accurate sensing network. Each smart meter records accurately the electrical load for a given property at 30 minute intervals, 24 hours a day. This granular data captures detailed habits and routines through the occupant’s interactions with electrical devices, enabling the detection and identification of alterations in behaviour. The research presented in this paper explores how this data could be used to achieve a safe living environment for people living with progressive neurodegenerative disorders, such as Dementia

Warren McCulloch and the British cyberneticians

Warren McCulloch was a significant influence on a number of British cyberneticians, as some British pioneers in this area were on him. He interacted regularly with most of the main figures on the British cybernetics scene, forming close friendships and collaborations with several, as well as mentoring others. Many of these interactions stemmed from a 1949 visit to London during which he gave the opening talk at the inaugural meeting of the Ratio Club, a gathering of brilliant, mainly young, British scientists working in areas related to cybernetics. This paper traces some of these relationships and interaction

Role of unstable periodic orbits in phase transitions of coupled map lattices

The thermodynamic formalism for dynamical systems with many degrees of freedom is extended to deal with time averages and fluctuations of some macroscopic quantity along typical orbits, and applied to coupled map lattices exhibiting phase transitions. Thereby, it turns out that a seed of phase transition is embedded as an anomalous distribution of unstable periodic orbits, which appears as a so-called q-phase transition in the spatio-temporal configuration space. This intimate relation between phase transitions and q-phase transitions leads to one natural way of defining transitions and their order in extended chaotic systems. Furthermore, a basis is obtained on which we can treat locally introduced control parameters as macroscopic ``temperature'' in some cases involved with phase transitions.Comment: 13 pages, 9 figures; further explanation and 2 figures are added (minor revision