Adoption Space and the Idea-to-Market Process of Health Technologies.

Although Europe 'produces' excellent science, it has not been equally successful in translating scientific results into commercially successful companies in spite of European and national efforts invested in supporting the translation process. The Idea-to-Market process is highly complex due to the large number of actors and stakeholders. ITECH was launched to propose recommendations which would accelerate the Idea-to-Market process of health technologies leading to improvements in the competitiveness of the European health technology industry in the global markets. The project went through the following steps: defining the Idea-to-Market process model; collection and analysis of funding opportunities; identification of 12 gaps and barriers in the Idea-to-Market process; a detailed analysis of these supported by interviews; a prioritization process to select the most important issues; construction of roadmaps for the prioritized issues; and finally generating recommendations and associated action plans. Seven issues were classified as in need of actions. Three of these are part of the ongoing Medical Device Directive Reform (MDR), namely health technology assessment, post-market surveillance and regulatory process, and therefore not within the scope of ITECH. Recommendations were made for eHealth taxonomy; Education and training; Clinical trials and Adoption space and Human Factors Engineering (HFE).

A Personalized Self-Management Rehabilitation System for Stroke Survivors: A Quantitative Gait Analysis Using a Smart Insole.

BACKGROUND: In the United Kingdom, stroke is the single largest cause of adult disability and results in a cost to the economy of £8.9 billion per annum. Service needs are currently not being met; therefore, initiatives that focus on patient-centered care that promote long-term self-management for chronic conditions should be at the forefront of service redesign. The use of innovative technologies and the ability to apply these effectively to promote behavior change are paramount in meeting the current challenges. OBJECTIVE: Our objective was to gain a deeper insight into the impact of innovative technologies in support of home-based, self-managed rehabilitation for stroke survivors. An intervention of daily walks can assist with improving lower limb motor function, and this can be measured by using technology. This paper focuses on assessing the usage of self-management technologies on poststroke survivors while undergoing rehabilitation at home. METHODS: A realist evaluation of a personalized self-management rehabilitation system was undertaken in the homes of stroke survivors (N=5) over a period of approximately two months. Context, mechanisms, and outcomes were developed and explored using theories relating to motor recovery. Participants were encouraged to self-manage their daily walking activity; this was achieved through goal setting and motivational feedback. Gait data were collected and analyzed to produce metrics such as speed, heel strikes, and symmetry. This was achieved using a "smart insole" to facilitate measurement of walking activities in a free-living, nonrestrictive environment. RESULTS: Initial findings indicated that 4 out of 5 participants performed better during the second half of the evaluation. Performance increase was evident through improved heel strikes on participants' affected limb. Additionally, increase in performance in relation to speed was also evident for all 5 participants. A common strategy emerged across all but one participant as symmetry performance was sacrificed in favor of improved heel strikes. This paper evaluates compliance and intensity of use. CONCLUSION: Our findings suggested that 4 out of the 5 participants improved their ability to heel strike on their affected limb. All participants showed improvements in their speed of gait measured in steps per minute with an average increase of 9.8% during the rehabilitation program. Performance in relation to symmetry showed an 8.5% average decline across participants, although 1 participant improved by 4%. Context, mechanism, and outcomes indicated that dual motor learning and compensatory strategies were deployed by the participants.

Feature selection and classification in supporting report-based self-management for people with chronic pain.

Chronic pain is a common long-term condition that affects a person's physical and emotional functioning. Currently, the integrated biopsychosocial approach is the mainstay treatment for people with chronic pain. Self-reporting (the use of questionnaires) is one of the most common methods to evaluate treatment outcome. The questionnaires can consist of more than 300 questions, which is tedious for people to complete at home. This paper presents a machine learning approach to analyze self-reporting data collected from the integrated biopsychosocial treatment, in order to identify an optimal set of features for supporting self-management. In addition, a classification model is proposed to differentiate the treatment stages. Four different feature selection methods were applied to rank the questions. In addition, four supervised learning classifiers were used to investigate the relationships between the numbers of questions and classification performance. There were no significant differences between the feature ranking methods for each classifier in overall classification accuracy or AUC ( p > 0.05); however, there were significant differences between the classifiers for each ranking method ( p < 0.001). The results showed the multilayer perceptron classifier had the best classification performance on an optimized subset of questions, which consisted of ten questions. Its overall classification accuracy and AUC were 100% and 1, respectively.

Smart self management: assistive technology to support people with chronic disease.

We have developed a personalised self management system to support self management of chronic conditions with support from health-care professionals. Accelerometers are used to measure gross levels of activity, for example walking around the house, and used to infer higher level activity states, such as standing, sitting and lying. A smart phone containing an accelerometer and a global positioning system (GPS) module can be used to monitor outdoor activity, providing both activity and location based information. Heart rate, blood pressure and weight are recorded and input to the system by the user. A decision support system (DSS) detects abnormal activity and distinguishes life style patterns. The DSS is used to assess the self management process, and automates feedback to the user, consistent with the achievement of their life goals. We have found that telecare and assistive technology is feasible to support self management for chronic conditions within the home and local community environments.

Optimal electrocardiographic lead systems: practical scenarios in smart clothing and wearable health systems.

Advances in wearable health systems, from a smart textile, signal processing, and wireless communications perspective, have resulted in the recent deployment of such systems in real clinical and healthcare settings. Nevertheless, the problem of identifying the most appropriate sites from which biological parameters can be recorded still remains unsolved. This paper aims to asses the effects of various practical constraints that may be encountered when choosing electrocardiographic recording sites for wearable health systems falling within the category of smart shirts for cardiac monitoring and analysis. We apply a lead selection algorithm to a set of 192 lead body surface potential maps (BSPM) and simulate a number of practical constraints by only allowing selection of recording sites from specific regions available in the 192 lead array. Of the various scenarios that were investigated, we achieved the best results when the selection process to identify the recording sites was constrained to an area around the precordial region. The top ten recording sites chosen in this region exhibited an rms voltage error of 25.8 mu V when they were used to estimate total ECG information. The poorest performing scenario was that which constrained the selection to two vertical strips on the posterior surface. The top ten recording sites chosen in this scenario exhibited an rms voltage error of 41.1 muV. In general, it was observed that out of all the scenarios investigated, those which constrained available regions to the posterior and lateral surfaces performed less favorably than those where electrodes could also be chosen on the anterior surface. The overall results from our approach have validated the proposed algorithm and its ability to select optimal recording sites taking into consideration the practical constraints that may exist with smart shirts.

Lead selection: old and new methods for locating the most electrocardiogram information.

The present article summarizes the work presented in several key studies over the past 3 decades in the area of limited lead selection. Specifically, we summarize the pioneering research of those investigators searching for the most "signal" information and those searching for the most "diagnostic" information. Initially, we present the work conducted by Barr et al and, later, Lux et al who investigated body surface potential maps to locate those recording sites containing the most signal information that subsequently facilitated the estimation of the electrical potentials at all other areas of the thoracic surface. Subsequently, the discussion focuses on the early work conducted by Kornreich et al, who used statistical methods to identify those recording sites containing optimal measurement features to improve upon the identification of different disease types. In addition to the aforementioned work, an overview of more recent complementary work is summarized.

Selection of optimal recording sites for limited lead body surface potential mapping in myocardial infarction and left ventricular hypertrophy.

A lead selection algorithm was applied to find optimal recording sites for limited lead body surface potential maps. The studied population consisted of a set of 117 lead body surface potential maps recorded from 744 subjects (229, normal; 278, with myocardial infraction [MI]; and 237, with left ventricular hypertrophy [LVH]). One generic lead set derived from all disease groups was found. Also found were 3 disease-specific lead sets (normal, MI, and LVH) and one specific to abnormal subjects (MI and LVH combined). The performance of each lead set in estimating data from other disease groups was largely similar. This was with the exception of leads specific to LVH in the estimation of normal data and normal leads in the estimation of LVH data. Here, the difference was found to be significant (P < .001). The top 6 recording sites in each lead set did not occupy the same positions as the 6 precordial leads. Although disease-specific lead sets are of limited practical use, this study has illustrated that, largely, there is little difference between the performance of different lead sets. The suboptimality of the 6 precordial leads has also been illustrated.

Personalisation and configuration of assistive technologies.

In this paper we present a multimedia tool that allows personalisation and configuration of an assistive technology. We have developed a tool called HomePUI that allows configuration of three disease domains; cognitive, physical and chronic. In this paper we present the rationale for our work, details of the developed tool and also present two case studies in which the tool has been used within cognitive and chronic disease management domains.

Synthesising the 12-lead electrocardiogram: Trends and challenges.

An area of electrocardiography which has received much interest of late is that of synthesising the 12-lead ECG from a reduced number of leads. The main advantage of this approach is obvious, as fewer recording sites are required to capture the same information. This, in turn, streamlines the ECG acquisition process with little detriment to the integrity of information used for interpretation. In the current article, we provide an overview of ECG synthesis along with a description of various 'limited lead' systems that have been reported in the literature. Based on this, several suggestions as to what the ECG of the future may entail have been made.

The next generation of mobile medication management solutions.

In this paper, we describe the development of an internet-based system and a novel mobile home based device for the management of medication. We extend these concepts through the descriptions of an enhanced service with the use of mobile phone technology and home based digital TV services.

Feature selection and classification model construction on type 2 diabetic patients' data.

OBJECTIVE: Diabetes affects between 2% and 4% of the global population (up to 10% in the over 65 age group), and its avoidance and effective treatment are undoubtedly crucial public health and health economics issues in the 21st century. The aim of this research was to identify significant factors influencing diabetes control, by applying feature selection to a working patient management system to assist with ranking, classification and knowledge discovery. The classification models can be used to determine individuals in the population with poor diabetes control status based on physiological and examination factors. METHODS: The diabetic patients' information was collected by Ulster Community and Hospitals Trust (UCHT) from year 2000 to 2004 as part of clinical management. In order to discover key predictors and latent knowledge, data mining techniques were applied. To improve computational efficiency, a feature selection technique, feature selection via supervised model construction (FSSMC), an optimisation of ReliefF, was used to rank the important attributes affecting diabetic control. After selecting suitable features, three complementary classification techniques (Naïve Bayes, IB1 and C4.5) were applied to the data to predict how well the patients' condition was controlled. RESULTS: FSSMC identified patients' 'age', 'diagnosis duration', the need for 'insulin treatment', 'random blood glucose' measurement and 'diet treatment' as the most important factors influencing blood glucose control. Using the reduced features, a best predictive accuracy of 95% and sensitivity of 98% was achieved. The influence of factors, such as 'type of care' delivered, the use of 'home monitoring', and the importance of 'smoking' on outcome can contribute to domain knowledge in diabetes control. CONCLUSION: In the care of patients with diabetes, the more important factors identified: patients' 'age', 'diagnosis duration' and 'family history', are beyond the control of physicians. Treatment methods such as 'insulin', 'diet' and 'tablets' (a variety of oral medicines) may be controlled. However lifestyle indicators such as 'body mass index' and 'smoking status' are also important and may be controlled by the patient. This further underlines the need for public health education to aid awareness and prevention. More subtle data interactions need to be better understood and data mining can contribute to the clinical evidence base. The research confirms and to a lesser extent challenges current thinking. Whilst fully appreciating the requirement for clinical verification and interpretation, this work supports the use of data mining as an exploratory tool, particularly as the domain is suffering from a data explosion due to enhanced monitoring and the (potential) storage of this data in the electronic health record. FSSMC has proved a useful feature estimator for large data sets, where processing efficiency is an important factor.

Optimizing the 12-lead electrocardiogram: a data driven approach to locating alternative recording sites.

BACKGROUND: Despite its widespread use, the limitations of the 12-lead electrocardiogram (ECG) are undisputed. The main deficiency is that just a small area of the precordium is interrogated and for some abnormalities information may be transmitted to a region of the body surface where information is not recorded. In this study, we attempted to optimize the 12-lead ECG by using a data-driven approach to suggest alternate recording sites. METHODS: A sequential lead selection algorithm was applied to a set of 744 body surface potential maps (BSPMs), consisting of recordings from subjects with myocardial infarction, left ventricular hypertrophy, and no apparent disease. A number of scenarios were investigated in which pairs of precordial leads were repositioned; these pairs were V3 and V5, V4 and V5, and V4 and V6. The algorithm was also used to find optimal positions for all 6 precordial leads. RESULT: Through estimation of entire surface potential distributions it was found that each of the scenarios, with 2 leads repositioned, captured more information than the standard 12-lead ECG. The scenario with V4 and V6 repositioned performed best with a root mean square error of 22.3 microvolts and a correlation coefficient of 0.967. This configuration also fared favorably when compared to the scenario where all 6 precordial leads were repositioned as optimizing all 6 leads offered no significant improvement. CONCLUSION: This study demonstrated the use of a lead selection algorithm in enhancing the 12-lead ECG. The results also indicated that repositioning just 2 precordial leads can provide the same level of information capture as that observed when all precordial leads are optimally placed.

Diagnosing old MI by searching for a linear boundary in the space of principal components.

Body surface potential mapping (BSPM) is a technique employing multiple electrodes to capture, via noninvasive means, an indication of the heart's condition. An inherent problem with this technique is the resulting high-dimensional recordings and the subsequent problems for diagnostic classifiers. A data set, recorded from a 192-lead BSPM system, containing 74 records is investigated. QRS isointegral maps, offering a summary of the information obtained during ventricular depolarization, were derived from 30 old inferior myocardial infarction and 44 normal recordings. Principal component analysis was applied to reduce the dimensionality of the recordings and a linear classifier was employed for classification. This perceptron-based classifier has been adapted so that the final weight and bias values are estimated prior to the learning process. This estimation process, referred to as the linear hyperplane approach (LHA), derives the estimated weights from a bisector hyperplane, placed orthogonal to the means of two class distributions in an n-dimensional Euclidean space. Estimating weights encourages a network to exhibit better generalization ability. Utilizing a number of different principal components as input features, the LHA achieved an average sensitivity and specificity of 79.58% and 76.45%, respectively, across all experiments. The average accuracy of 76.73% achieved with this approach was significantly better than the other benchmark classifiers evaluated against it.

Selection of optimal recording sites for limited lead body surface potential mapping: a sequential selection based approach.

BACKGROUND: In this study we propose the development of a new algorithm for selecting optimal recording sites for limited lead body surface potential mapping. The proposed algorithm differs from previously reported methods in that it is based upon a simple and intuitive data driven technique that does not make any presumptions about deterministic characteristics of the data. It uses a forward selection based search technique to find the best combination of electrocardiographic leads. METHODS: The study was conducted using a dataset consisting of body surface potential maps (BSPM) recorded from 116 subjects which included 59 normals and 57 subjects exhibiting evidence of old Myocardial Infarction (MI). The performance of the algorithm was evaluated using spatial RMS voltage error and correlation coefficient to compare original and reconstructed map frames. RESULTS: In all, three configurations of the algorithm were evaluated and it was concluded that there was little difference in the performance of the various configurations. In addition to observing the performance of the selection algorithm, several lead subsets of 32 electrodes as chosen by the various configurations of the algorithm were evaluated. The rationale for choosing this number of recording sites was to allow comparison with a previous study that used a different algorithm, where 32 leads were deemed to provide an acceptable level of reconstruction performance. CONCLUSION: It was observed that although the lead configurations suggested in this study were not identical to that suggested in the previous work, the systems did bear similar characteristics in that recording sites were chosen with greatest density in the precordial region.

A computational approach for peptidomic analysis in taxonomic study of amphibian species.

Peptides in the skin secretion of frogs have been studied for some time now because they frequently possess important biological activity such as antibiotic, antimicrobial, or anticancer properties. In this paper, we present a computational approach for measuring the degree of similarity between the entire peptide complement of the skin secretion of specimens from the same or different species. The first step in the analysis is the generation of a mass spectral profile from an experimental high-performance liquid chromatography/electrosparay ionization analysis of the sample. An "overlap" between the mass spectral profiles of different specimens is then proposed as a measure of their similarity. Analysis of specimens from three species of the genus Litoria, viz., L. Aurea, L. Caerulea, and L. Infrafrenata, and Rana Capito of genus Rana shows that the degree of similarity is highest between specimens from the same species, lower for specimens from different species of the same genus, and lowest between specimens from different genera. This indicates that comparison of skin peptide profiles (i.e., mass spectral profiles of skin secretion) is potentially a useful aid in the taxonomic study of amphibian species.

Mining for diagnostic information in body surface potential maps: a comparison of feature selection techniques.

BACKGROUND: In body surface potential mapping, increased spatial sampling is used to allow more accurate detection of a cardiac abnormality. Although diagnostically superior to more conventional electrocardiographic techniques, the perceived complexity of the Body Surface Potential Map (BSPM) acquisition process has prohibited its acceptance in clinical practice. For this reason there is an interest in striking a compromise between the minimum number of electrocardiographic recording sites required to sample the maximum electrocardiographic information. METHODS: In the current study, several techniques widely used in the domains of data mining and knowledge discovery have been employed to mine for diagnostic information in 192 lead BSPMs. In particular, the Single Variable Classifier (SVC) based filter and Sequential Forward Selection (SFS) based wrapper approaches to feature selection have been implemented and evaluated. Using a set of recordings from 116 subjects, the diagnostic ability of subsets of 3, 6, 9, 12, 24 and 32 electrocardiographic recording sites have been evaluated based on their ability to correctly asses the presence or absence of Myocardial Infarction (MI). RESULTS: It was observed that the wrapper approach, using sequential forward selection and a 5 nearest neighbour classifier, was capable of choosing a set of 24 recording sites that could correctly classify 82.8% of BSPMs. Although the filter method performed slightly less favourably, the performance was comparable with a classification accuracy of 79.3%. In addition, experiments were conducted to show how (a) features chosen using the wrapper approach were specific to the classifier used in the selection model, and (b) lead subsets chosen were not necessarily unique. CONCLUSION: It was concluded that both the filter and wrapper approaches adopted were suitable for guiding the choice of recording sites useful for determining the presence of MI. It should be noted however that in this study recording sites have been suggested on their ability to detect disease and such sites may not be optimal for estimating body surface potential distributions.