Sources of Measurement Error in an ECG Examination: Implications for Performance-Based Assessments

Objective: To assess the sources of measurement error in an electrocardiogram (ECG) interpretation examination given in a third-year internal medicine clerkship. Design: Three successive generalizability studies were conducted. 1) Multiple faculty rated student responses to a previously administered exam. 2) The rating criteria were revised and study 1 was repeated. 3) The examination was converted into an extended matching format including multiple cases with the same underlying cardiac problem. Results: The discrepancies among raters (main effects and interactions) were dwarfed by the error associated with case specificity. The largest source of the differences among raters was in rating student errors of commission rather than student errors of omission. Revisions in the rating criteria may have helped increase inter-rater reliability slightly however, due to case specificity, it had little impact on the overall reliability of the exam. The third study indicated the majority of the variability in student performance across cases was in performance across cases within the same type of cardiac problem rather than between different types of cardiac problems. Conclusions: Case specificity was the overwhelming source of measurement error. The variation among cases came mainly from discrepancies in performance between examples of the same cardiac problem rather than from differences in performance across different types of cardiac problems. This suggests it is necessary to include a large number of cases even if the goal is to assess performance on only a few types of cardiac problems

Longitudinal study of patients with chronic Chagas cardiomyopathy in Brazil (SaMi-Trop project): a cohort profile.

PurposeWe have established a prospective cohort of 1959 patients with chronic Chagas cardiomyopathy to evaluate if a clinical prediction rule based on ECG, brain natriuretic peptide (BNP) levels, and other biomarkers can be useful in clinical practice. This paper outlines the study and baseline characteristics of the participants.ParticipantsThe study is being conducted in 21 municipalities of the northern part of Minas Gerais State in Brazil, and includes a follow-up of 2 years. The baseline evaluation included collection of sociodemographic information, social determinants of health, health-related behaviours, comorbidities, medicines in use, history of previous treatment for Chagas disease, functional class, quality of life, blood sample collection, and ECG. Patients were mostly female, aged 50-74 years, with low family income and educational level, with known Chagas disease for >10 years; 46% presented with functional class >II. Previous use of benznidazole was reported by 25.2% and permanent use of pacemaker by 6.2%. Almost half of the patients presented with high blood cholesterol and hypertension, and one-third of them had diabetes mellitus. N-terminal of the prohormone BNP (NT-ProBNP) level was >300 pg/mL in 30% of the sample.Findings to dateClinical and laboratory markers predictive of severe and progressive Chagas disease were identified as high NT-ProBNP levels, as well as symptoms of advanced heart failure. These results confirm the important residual morbidity of Chagas disease in the remote areas, thus supporting political decisions that should prioritise in addition to epidemiological surveillance the medical treatment of chronic Chagas cardiomyopathy in the coming years. The São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) represents a major challenge for focused research in neglected diseases, with knowledge that can be applied in primary healthcare.Future plansWe will continue following this patients' cohort to provide relevant information about the development and progression of Chagas disease in remotes areas, with social and economic inequalities.Trial registration numberNCT02646943; Pre-results

ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-time Constraints

Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. However, it is usually associated with computational complexity at the decoding end, increasing the latency of the system. Meanwhile, mobile processors are becoming computationally stronger and more efficient. Heterogeneous multicore platforms (HMPs) offer a local processing solution that can alleviate the limitations of remote signal processing. This paper demonstrates the real-time performance of compressed ECG reconstruction on ARM's big.LITTLE HMP and the advantages they provide as the primary processing unit of the IoT architecture. It also investigates the efficacy of CS in minimizing power consumption of a wearable device under real-time and hardware constraints. Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. The CS extends the battery life of wearable medical devices up to 15.4% considering ECGs suitable for wellness applications and up to 6.6% for clinical grade ECGs. Energy consumption at the gateway is largely due to an active internet connection; hence, processing the signals locally both mitigates system's latency and improves gateway's battery life. Many remote health solutions can benefit from an architecture centered around the use of HMPs, a step toward better remote health monitoring systems.Peer reviewedFinal Published versio

ECG Signal Acquisition System for Remote Healthcare Service With Telemetric Capability

Many of the tasks of daily life of human beings became easier with the advent of information communication technology. Complex tasks are becoming simpler with the application of this technology which ensures to meet the challenges with never-ending innovations. However its application in the field of healthcare is still lagging and geared up only in the last decade. In the field of healthcare service information communication technology has tremendous potential in bio-telemetry which is commonly termed as Telehealth or Telemedicine. Telemedicine makes the task easier for remote healthcare service where diagnosis of patients can be performed from remote places. As example, most of the rural areas are lacking of good physicians and healthcare services, and may be monitored by city hospitals through telemedicine. Hence healthcare community requires suitable low cost, low power yet affordable and sustainable gadgets compatible to biomedical signals. In this paper a development effort is presented to acquire ECG signal that is processed and transmitted over communication channel. Keywords: Telehealth, Telemedicine, Information Communication Technology, Remote healthcare, Electrocardiogram, Biopententia

Remote Heart Diagnosis

For those in impoverished communities or remote regions, obtaining adequate healthcare can be a burden. Furthermore, limited access to specialists like cardiologists can make curable conditions a death sentence by leading them to be identified too late. An essential factor in the identification of heart conditions is the use of an electrocardiograph to measure the signal of the heart. In this project, the Remote Heart Diagnosis Team endeavored to design and build a prototype capable of remotely collecting and analyzing an electrocardiogram and displaying the results to a cardiologist in any location for review. The prototype is composed of five main components. The first component is a printed circuit board designed to record the electrocardiogram. The second is a Raspberry Pi and touchscreen programmed to guide the user through the collection process, compile patient data and read the output of the circuit, run an artificial intelligence algorithm, and store all the information in the third component, a remotely deployed database, using a wireless connection. The fourth component is a website that accesses the database and allows doctors to view and interact with the device data. The final component is a three dimensional printed casing that houses the circuit, microcomputer, and touchscreen. In early stages of testing, the team identified the need to transfer the circuit from a breadboard to a printed circuit board as the circuit often failed after being moved due to loosened wires. The team also discovered that the noise in the circuit was dependent on the wall outlet being used, leading to the addition of a filter in the circuit. As shown in the success of all but one final test, the prototype meets all expected qualifications, allowing for the changes in the potential diagnoses with the approval of the project sponsor. The only design requirement not achieved was in regards to the ability of the website to replicate a commercial electrocardiogram in form with 90% accuracy; however, as there were limitations with the commercial electrocardiogram used in terms of details in the data, measurement methods, and accuracy, the visuals were deemed reasonable due to their similarity to a traditional electrocardiogram. Overall, the prototype is a fully functional proof of concept, as it is able to measure a clean electrocardiogram signal from a patient and collect their biographical data, analyze the electrocardiogram using a deployed artificial intelligence network, and store the results in a manner that can be remotely accessed on a website. This prototype is only a proof of concept, however, as, while the developed artificial intelligence network proved that the deployment and use of this type of network is possible, the network is unable to achieve functional accuracy due to a limited dataset. Before commercialization of the prototype, the neural network would need to be retrained using a large dataset of electrocardiograms collected using the device and labeled by a trained cardiologist. Furthermore, the neural network architecture and its ramifications in terms of classification should be reviewed with a professional cardiologist to ensure that image classification has the potential for functional accuracy. In addition, while outside the scope of this project, security code would need to be added to protect the website and transmissions before the prototype could be commercialized to comply with patient privacy laws and medical information regulations. Therefore, while the prototype was successful in achieving the desired functionality and in meeting the requirements, additional improvements will need to be made prior to the expansion of use

A comprehensive survey of wireless body area networks on PHY, MAC, and network layers solutions

Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted

Diseño e implementación de un Sistema embebido

This article presents the design and implementation of an embedded system called TES ROv2.0 non-invasive, capable of capturing and transmitting relevant biomedical signals such as: electrocardiography signals, heart rate, oxygen saturation in the blood and arterial pressure with the Support from the Clinical Information System called "SARURO", in which a detailed process of these biomedical signals is visualized and carried out by a doctor or specialist, without direct contact with the patient. Therefore, researchers have been able to affirm that integrated systems are tools that offer great versatility in the medical information market, allowing acquisition, adaptation, transformation in remote places, without linking operation to a single communication alternative, making the Telemonitoring system more versatile in its functionality to transmit over WPAN, WLAN, LAN and CELULARE networks.Este articulo presenta el diseño e implementación de un sistema embebido llamado TES ROv2.0 no invasivo, capaz de capturar y transmitir señales biomédicas relevantes como: las señales de electrocardiografía, frecuencia cardíaca, la saturación de oxígeno en la sangre y la presión arterial con el apoyo del  Sistema de Información clínico llamado "SARURO", en el cual se visualiza y efectúa un proceso detallado de estas señales biomédicas por parte de un médico o especialista, sin el contacto  directo con el paciente. Por tanto, los investigadores han podido afirmar que los sistemas integrados son herramientas que ofrecen una gran versatilidad en el mercado de la información médica, lo que permite la adquisición, adaptación, transformación en lugares remotos, sin ligar el funcionamiento a una única alternativa de comunicación, haciendo al sistema de telemonitorización más versátil en su funcionalidad para transmitir por redes WPAN, WLAN, LAN y CELULARES