Modelling mobile-based technology adoption among people with dementia.

The work described in this paper builds upon our previous research on adoption modelling and aims to identify the best subset of features that could offer a better understanding of technology adoption. The current work is based on the analysis and fusion of two datasets that provide detailed information on background, psychosocial, and medical history of the subjects. In the process of modelling adoption, feature selection is carried out followed by empirical analysis to identify the best classification models. With a more detailed set of features including psychosocial and medical history information, the developed adoption model, using kNN algorithm, achieved a prediction accuracy of 99.41% when tested on 173 participants. The second-best algorithm built, using NN, achieved 94.08% accuracy. Both these results have improved accuracy in comparison to the best accuracy achieved (92.48%) in our previous work, based on psychosocial and self-reported health data for the same cohort. It has been found that psychosocial data is better than medical data for predicting technology adoption. However, for the best results, we should use a combination of psychosocial and medical data where it is preferable that the latter is provided from reliable medical sources, rather than self-reported.

The "Worktivity" mHealth intervention to reduce sedentary behaviour in the workplace: a feasibility cluster randomised controlled pilot study.

BACKGROUND: Office work generally consists of high amounts of sedentary behaviour (SB) which has been associated with negative health consequences. We developed the "WorktivIty" mobile app to help office workers reduce their SB through self-monitoring and feedback on sedentary time, prompts to break sedentary time, and educational facts. The aim of this paper is to report the feasibility of delivering the Worktivity intervention to desk-based office workers in the workplace setting and describe methodological considerations for a future trial. METHODS: We conducted a three-arm feasibility cluster randomised controlled pilot study over an 8-week period with full time-desk based employees. Clustered randomisation was to one of three groups: Worktivity mobile app (MA; n = 20), Worktivity mobile app plus SSWD (MA+SSWD; n = 20), or Control (C; n = 16). Feasibility was assessed using measures of recruitment and retention, intervention engagement, intervention delivery, completion rates and usable data, adverse events, and acceptability. RESULTS: Recruitment of companies to participate in this study was challenging (8% of those contacted), but retention of individual participants within the recruited groups was high (81% C, 90% MA + SSWD, 95% MA). Office workers' engagement with the app was moderate (on average 59%). Intervention delivery was partially compromised due to diminishing user engagement and technical issues related to educational fact delivery. Sufficient amounts of useable data were collected, however either missing or unusable data were observed with activPAL™, with data loss increasing at each follow up time point. No serious adverse events were identified during the study. The majority of participants agreed that the intervention could be implemented within the workplace setting (65% MA; 72% MA + SSWD) but overall satisfaction with the intervention was modest (58% MA; 39% MA + SSWD). CONCLUSIONS: The findings suggest that, in principle, it is feasible to implement a mobile app-based intervention in the workplace setting however the Worktivity intervention requires further technical refinements before moving to effectiveness trials. Challenges relating to the initial recruitment of workplaces and maintaining user engagement with the mHealth intervention over time need to be addressed prior to future large-scale implementation. Further research is needed to identify how best to overcome these challenges.

Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders.

Activity recognition has received considerable attention in many research fields, such as industrial and healthcare fields. However, many researches about activity recognition have focused on static activities and dynamic activities in current literature, while, the transitional activities, such as stand-to-sit and sit-to-stand, are more difficult to recognize than both of them. Consider that it may be important in real applications. Thus, a novel framework is proposed in this paper to recognize static activities, dynamic activities, and transitional activities by utilizing stacked denoising autoencoders (SDAE), which is able to extract features automatically as a deep learning model rather than utilize manual features extracted by conventional machine learning methods. Moreover, the resampling technique (random oversampling) is used to improve problem of unbalanced samples due to relatively short duration characteristic of transitional activity. The experiment protocol is designed to collect twelve daily activities (three types) by using wearable sensors from 10 adults in smart lab of Ulster University, the experiment results show the significant performance on transitional activity recognition and achieve the overall accuracy of 94.88% on three types of activities. The results obtained by comparing with other methods and performances on other three public datasets verify the feasibility and priority of our framework. This paper also explores the effect of multiple sensors (accelerometer and gyroscope) to determine the optimal combination for activity recognition.

Using computer, mobile and wearable technology enhanced interventions to reduce sedentary behaviour: a systematic review and meta-analysis.

BACKGROUND: High levels of sedentary behaviour (SB) are associated with negative health consequences. Technology enhanced solutions such as mobile applications, activity monitors, prompting software, texts, emails and websites are being harnessed to reduce SB. The aim of this paper is to evaluate the effectiveness of such technology enhanced interventions aimed at reducing SB in healthy adults and to examine the behaviour change techniques (BCTs) used. METHODS: Five electronic databases were searched to identify randomised-controlled trials (RCTs), published up to June 2016. Interventions using computer, mobile or wearable technologies to facilitate a reduction in SB, using a measure of sedentary time as an outcome, were eligible for inclusion. Risk of bias was assessed using the Cochrane Collaboration's tool and interventions were coded using the BCT Taxonomy (v1). RESULTS: Meta-analysis of 15/17 RCTs suggested that computer, mobile and wearable technology tools resulted in a mean reduction of -41.28 min per day (min/day) of sitting time (95% CI -60.99, -21.58, I2 = 77%, n = 1402), in favour of the intervention group at end point follow-up. The pooled effects showed mean reductions at short (≤ 3 months), medium (>3 to 6 months), and long-term follow-up (>6 months) of -42.42 min/day, -37.23 min/day and -1.65 min/day, respectively. Overall, 16/17 studies were deemed as having a high or unclear risk of bias, and 1/17 was judged to be at a low risk of bias. A total of 46 BCTs (14 unique) were coded for the computer, mobile and wearable components of the interventions. The most frequently coded were "prompts and cues", "self-monitoring of behaviour", "social support (unspecified)" and "goal setting (behaviour)". CONCLUSION: Interventions using computer, mobile and wearable technologies can be effective in reducing SB. Effectiveness appeared most prominent in the short-term and lessened over time. A range of BCTs have been implemented in these interventions. Future studies need to improve reporting of BCTs within interventions and address the methodological flaws identified within the review through the use of more rigorously controlled study designs with longer-term follow-ups, objective measures of SB and the incorporation of strategies to reduce attrition. TRIAL REGISTRATION: The review protocol was registered with PROSPERO: CRD42016038187.

Modelling assistive technology adoption for people with dementia.

PURPOSE: Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success. Generally speaking, the elderly are not well-disposed to technologies and have limited experience; these factors contribute towards limiting the widespread acceptance of technology. It is therefore important to evaluate the potential success of technologies prior to their deployment. MATERIALS AND METHODS: The research described in this paper builds upon our previous work on modelling adoption of assistive technology, in the form of cognitive prosthetics such as reminder apps and aims at identifying a refined sub-set of features which offer improved accuracy in predicting technology adoption. Consequently, in this paper, an adoption model is built using a set of features extracted from a user's background to minimise the likelihood of non-adoption. The work is based on analysis of data from the Cache County Study on Memory and Aging (CCSMA) with 31 features covering a range of age, gender, education and details of health condition. In the process of modelling adoption, feature selection and feature reduction is carried out followed by identifying the best classification models. FINDINGS: With the reduced set of labelled features the technology adoption model built achieved an average prediction accuracy of 92.48% when tested on 173 participants. CONCLUSIONS: We conclude that modelling user adoption from a range of parameters such as physical, environmental and social perspectives is beneficial in recommending a technology to a particular user based on their profile.

Methods for presenting and visualising electrocardiographic data: From temporal signals to spatial imaging.

INTRODUCTION: The electrocardiogram (ECG) is a recording of the electrical activity of the heart. It is commonly used to non-invasively assess the cardiac activity of a patient. Since 1938, ECG data has been visualised as 12 scalar traces (known as the standard 12-lead ECG). Although this is known as the standard approach, there has been a myriad of alternative methods proposed to visualise ECG data. The purpose of this paper is to provide an overview of these methods and to introduce the field of ECG visualisation to early stage researchers. A scientific purpose is to consider the future of ECG visualisation within routine clinical practice. METHODS: This paper structures the different ECG visualisation methods using four categories, i.e. temporal, vectorial, spatial and interactive. Temporal methods present the data with respect to time, vectorial methods present data with respect to direction and magnitude, spatial methods present data in 2D or 3D space and interactive methods utilise interactive computing to facilitate efficient interrogation of ECG data at different levels of detail. CONCLUSION: Spatial visualisation has been around since its introduction by Waller and vector based visualisation has been around since the 1920s. Given these approaches have already been given the 'test of time', they are unlikely to be replaced as the standard in the near future. Instead of being replaced, the standard is more likely to be 'supplemented'. However, the design and presentation of these ECG visualisation supplements need to be universally standardised. Subsequent to the development of 'standardised supplements', as a requirement, they could then be integrated into all ECG machines. We recognise that without intuitive software and interactivity on mobile devices (e.g. tablet PCs), it is impractical to integrate the more advanced ECG visualisation methods into routine practice (i.e. epicardial mapping using an inverse solution).

Estimation performance of a reduced lead system during continuous 12-lead ECG ST-segment monitoring.

BACKGROUND: Reduced lead systems utilizing patient-specific transformation weights have been reported to achieve superior estimates than those utilizing population-based transformation weights. We report upon the effects of ischemic-type electrocardiographic changes on the estimation performance of a reduced lead system when utilizing patient-specific transformation weights and population-based transformation weights. METHOD: A reduced lead system that used leads I, II, V2 and V5 to estimate leads V1, V3, V4, and V6 was investigated. Patient-specific transformation weights were developed on electrocardiograms containing no ischemic-type changes. Patient-specific and population-based transformations weights were assessed on 45 electrocardiograms with ischemic-type changes and 59 electrocardiograms without ischemic-type changes. RESULTS: For patient-specific transformation weights the estimation performance measured as median root mean squared error values (no ischemic-type changes vs. ischemic-type changes) was found to be (V1, 27.5 µV vs. 95.8 µV, P<.001; V3, 33.9 µV vs. 65.2 µV, P<.001; V4, 24.8 µV vs. 62.0 µV, P<.001; V6, 11.7 µV vs. 51.5 µV, P<.001). The median magnitude of ST-amplitude difference 60 ms after the J-point between patient-specific estimated leads and actual recorded leads (no ischemic-type changes vs. ischemic-type changes) was found to be (V1, 18.9 µV vs. 61.4 µV, P<.001; V3, 14.3 µV vs. 61.1 µV, P<.001; V4, 9.7 µV vs. 61.3 µV, P<.001; V6, 5.9 µV vs. 46.0 µV, P<.001). CONCLUSION: The estimation performance of patient-specific transformations weights can deteriorate when ischemic-type changes develop. Performance assessment of patient-specific transformation weights should be performed using electrocardiographic data that represent the monitoring situation for which the reduced lead system is targeted.

Usability of a new electronic assistive device for community-dwelling persons with mild dementia.

OBJECTIVE: To evaluate a newly developed integrated digital prosthetic, the COGKNOW Day Navigator (CDN), to support persons with mild dementia in their daily lives, with memory, social contacts, daily activities and safety. METHODS: A user participatory method was applied in the development process, which consisted of three iterative 1-year cycles with field tests in Amsterdam, Belfast and Luleå. In the successive cycles 16, 14 and 12 persons with dementia and their carers participated. Data on usability were collected by means of interviews, observations, questionnaires, logging and diaries. The CDN prototype consists of a touch screen, a mobile device, sensors and actuators. RESULTS: The evaluation showed that persons with dementia and carers valued the CDN overall as user-friendly and useful. Conclusions regarding the effectiveness of the system in daily life were limited due to insufficient duration of the testing period caused by delays in development and some instability of the final prototype. CONCLUSION: With the suggested adaptations, the CDN is expected to be a useful tool for supporting community-dwelling persons with mild dementia and their carers.

A simulation tool for visualizing and studying the effects of electrode misplacement on the 12-lead electrocardiogram.

INTRODUCTION: Studies have shown that between 0.4% and 4% of all 12-lead electrocardiograms (ECGs) are recorded using incorrect electrode positions. Electrode misplacement can cause a misdiagnosis either by concealing a pathology or, on the contrary, by emulating a pathology. Irrespective of this fact, ECG textbooks contain little or no information regarding the effects of electrode misplacement. Moreover, current pedagogic tools, which include physical mannequins, do not allow for the free positioning of electrodes to demonstrate these effects. In recognition of this, an electrode misplacement simulator (EMS) has been developed in this study. METHODS: The EMS is a Web-based simulation developed using the Adobe Flash technology. The software allows the user to position the electrodes anywhere on the torso while rendering the corresponding ECG leads using body surface potential maps. A beta version of the EMS has been made available on the Internet. RESULTS: The EMS has been briefly evaluated by a random selection of delegates (n = 17) from the 37th Annual Conference on Computing in Cardiology. After completing representative tasks and using the EMS for approximately 30 minutes, all 17 participants completed a questionnaire. Overall, the responsiveness of the EMS was rated between 4 and 5 on a Likert scale, 94% of participants rated the "ease of use" between 4 and 5, and 88% of participants also rated the "look and feel" between 4 and 5 on a Likert scale. CONCLUSION: The EMS has the potential to be used to support researchers in enhancing criteria currently used for detecting electrode misplacement. It could also be used to assist academic staff in teaching the effects of electrode misplacement. In this respect, it is currently being used as part of an undergraduate "Clinical Physiology" degree program at the University of Ulster.

A Web-based tool for processing and visualizing body surface potential maps.

The body surface potential map (BSPM) is potentially more accurate for diagnosing cardiac pathologies when compared to the standard 12-lead electrocardiogram (ECG). However, a contributing factor to the lack of widespread adoption of the BSPM is the shortage of standard methods for its storage and visualization. Based on these observations, a BSPM storage format based on the eXtensible Markup Language has been developed within this study, alongside a Web-based BSPM viewer. This viewer was created using a lossless vector graphics tool (Adobe Flash) to maintain the quality of the ECG waveforms when they are enlarged. The viewer also runs inside the Web browser to facilitate BSPM visualization independent of the clinician's geographical location. This online nature enabled the creation of a comments system that can be used to assist in a collaborative diagnosis. This is useful because BSPM diagnostic criteria are not well established. Moreover, using the viewer's innovative tools (ie, calipers, isopotential maps), the clinician can explore BSPM datasets. Algorithms have also been integrated within the system to extract and display the 12-lead ECG and the vectorcardiogram from the BSPM. This viewer has been available online for 10 months alongside a Weblog, which has been used to record the user's feedback. During this period, 12 experts from both the clinical and visualization domains evaluated the viewer and contributed to its design. It has been the general consensus of all experts that the application is an effective solution for visualizing BSPMs. This viewer has been tested to visualize 2 different BSPMs using a PC (3 GHz CPU, 3 GB RAM, 6 MB broadband). The Lux-192 BSPM and the Kornreich-117 BSPM where both uploaded and visualized within 3.8 seconds (mean time from 10 trials). This BSPM storage format and its associated viewer provide a framework for a BSPM management system. If this system is made widely available, it has the potential to provide BSPM interoperability, knowledge sharing, and standardization. This has the potential to increase the uptake of BSPM integration into routine clinical practice.

Effects of electrode placement errors in the EASI-derived 12-lead electrocardiogram.

In this study, we assess the effects of electrode placement error on the EASI-derived 12-lead electrocardiogram (ECG). The study data set consisted of 744 body surface potential map (BSPM) recordings. The BSPMs, each of which was made up of 117 leads, were recorded from a mixture of healthy, myocardial infarction, and left ventricular hypertrophy subjects. The BSPMs were interpolated to increase the number of data points in the region of the EASI recording electrodes I, E, and A and the precordial leads. This facilitated 3 experiments. Firstly, recording sites I, E, and A were simultaneously moved ±5 cm vertically, in 0.5 cm increments, from their correct locations. Secondly, recording sites I and A were moved horizontally, again up to ±5 cm, in 0.5 cm increments. Finally, all 6 precordial leads were moved vertically in 0.5 cm increments up to ±5 cm. At each movement step, the resulting 12-lead ECG was compared with the original 12-lead ECG. Root mean square error was determined along with the absolute difference in J-point amplitude. Although the EASI leads were found to be less sensitive to electrode misplacement than the standard precordial leads, it was found that when precordial leads were moved up to ±3 cm vertically, the resulting 12-lead ECG more accurately resembled the original 12-lead ECG than a 12-lead ECG reconstructed from accurately positioned EASI leads. Further work is required to establish the effects of electrode misplacement beyond the ±5 cm limits assessed in this study.

XML-BSPM: an XML format for storing Body Surface Potential Map recordings.

BACKGROUND: The Body Surface Potential Map (BSPM) is an electrocardiographic method, for recording and displaying the electrical activity of the heart, from a spatial perspective. The BSPM has been deemed more accurate for assessing certain cardiac pathologies when compared to the 12-lead ECG. Nevertheless, the 12-lead ECG remains the most popular ECG acquisition method for non-invasively assessing the electrical activity of the heart. Although data from the 12-lead ECG can be stored and shared using open formats such as SCP-ECG, no open formats currently exist for storing and sharing the BSPM. As a result, an innovative format for storing BSPM datasets has been developed within this study. METHODS: The XML vocabulary was chosen for implementation, as opposed to binary for the purpose of human readability. There are currently no standards to dictate the number of electrodes and electrode positions for recording a BSPM. In fact, there are at least 11 different BSPM electrode configurations in use today. Therefore, in order to support these BSPM variants, the XML-BSPM format was made versatile. Hence, the format supports the storage of custom torso diagrams using SVG graphics. This diagram can then be used in a 2D coordinate system for retaining electrode positions. RESULTS: This XML-BSPM format has been successfully used to store the Kornreich-117 BSPM dataset and the Lux-192 BSPM dataset. The resulting file sizes were in the region of 277 kilobytes for each BSPM recording and can be deemed suitable for example, for use with any telemonitoring application. Moreover, there is potential for file sizes to be further reduced using basic compression algorithms, i.e. the deflate algorithm. Finally, these BSPM files have been parsed and visualised within a convenient time period using a web based BSPM viewer. CONCLUSIONS: This format, if widely adopted could promote BSPM interoperability, knowledge sharing and data mining. This work could also be used to provide conceptual solutions and inspire existing formats such as DICOM, SCP-ECG and aECG to support the storage of BSPMs. In summary, this research provides initial ground work for creating a complete BSPM management system.

Iterative four-phase development of a theory-based digital behaviour change intervention to reduce occupational sedentary behaviour.

INTRODUCTION: As high amounts of occupational sitting have been associated with negative health consequences, designing workplace interventions to reduce sedentary behaviour (SB) is of public health interest. Digital technology may serve as a cost-effective and scalable platform to deliver such an intervention. This study describes the iterative development of a theory-based, digital behaviour change intervention to reduce occupational SB. METHODS: The behaviour change wheel and The Behaviour Change Technique Taxonomy were used to guide the intervention design process and form a basis for selecting the intervention components. The development process consisted of four phases: phase 1 - preliminary research, phase 2 - consensus workshops, phase 3 - white boarding and phase 4 - usability testing. RESULTS: The process led to the development and refinement of a smartphone application - Worktivity. The core component was self-monitoring and feedback of SB at work, complemented by additional features focusing on goal setting, prompts and reminders to break up prolonged periods of sitting, and educational facts and tips. Key features of the app included simple data entry and personalisation based on each individual's self-reported sitting time. Results from the 'think-aloud' interviews (n=5) suggest Worktivity was well accepted and that users were positive about its features. CONCLUSION: This study led to the development of Worktivity, a theory-based and user-informed mobile app intervention to reduce occupational SB. It is the first app of its kind developed with the primary aim of reducing occupational SB using digital self-monitoring. This paper provides a template to guide others in the development and evaluation of technology-supported behaviour change interventions.

Exploring the Views of Desk-Based Office Workers and Their Employers' Beliefs Regarding Strategies to Reduce Occupational Sitting Time, With an Emphasis on Technology-Supported Approaches.

OBJECTIVE: Employee and employer views regarding how technology-supported strategies can best meet their needs to reduce occupational sitting are not well known. This study explored target user and key stakeholder beliefs regarding strategies to reduce occupational sitting focusing on technology-supported approaches. METHODS: Nine focus groups and two interviews (employees, n = 27; employers, n = 19; board members, n = 2) were conducted, transcribed, and analyzed thematically. RESULTS: The main barrier to reducing sitting was job-related tasks taking primary priority. Intervention designers should consider individual preferences, environmental factors, judgmental culture, productivity concerns, and staff knowledge. Technology-supported strategies such as smartphone applications, computer software, wearables, and emails were deemed to be useful tools to provide prompts and allow behavioral self-monitoring in an easily individualized manner. CONCLUSIONS: Technology-supported strategies were seen to be valuable approaches and might fruitfully be incorporated into future interventions to reduce sitting time.

Data Mining and Fusion of Unobtrusive Sensing Solutions for Indoor Activity Recognition.

This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen: (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of: (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance-This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.

Development of a smart home simulator for use as a heuristic tool for management of sensor distribution.

Smart Homes offer potential solutions for various forms of independent living for the elderly. The assistive and protective environment afforded by smart homes offer a safe, relatively inexpensive, dependable and viable alternative to vulnerable inhabitants. Nevertheless, the success of a smart home rests upon the quality of information its decision support system receives and this in turn places great importance on the issue of correct sensor deployment. In this article we present a software tool that has been developed to address the elusive issue of sensor distribution within smart homes. Details of the tool will be presented and it will be shown how it can be used to emulate any real world environment whereby virtual sensor distributions can be rapidly implemented and assessed without the requirement for physical deployment for evaluation. As such, this approach offers the potential of tailoring sensor distributions to the specific needs of a patient in a non-evasive manner. The heuristics based tool presented here has been developed as the first part of a three stage project.

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.

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.

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.