Medical microprocessor systems

The practical classes and laboratory work in the discipline "Medical microprocessor systems", performed using software in the programming environment of microprocessors Texas Instruments (Code Composer Studio) and using of digital microprocessors of the Texas Instruments DSK6400 family, and models of electrical equipment in the environment of graphical programming LabVIEW 2010.Лабораторний практикум з програмування та побудови медичних мікропроцесорних систем, який викладено у навчальному посібнику допомагає накопичувати й ефективно використовувати отриману інформацію з теоретичного курсу на всіх стадіях навчального процесу, що є важливим для підготовки магістрів та необхідною ланкою у науковому пізнанні практичних основ біомедичної електроніки.The laboratory workshop on the programming and construction of medical microprocessor systems, which is outlined in the tutorial, helps to accumulate and effectively use the information obtained from a theoretical course at all stages of the educational process, which is important for the preparation of masters and a necessary link in the scientific knowledge of the practical basics of biomedicine.Лабораторный практикум по программированию и построению медицинских микропроцессорных систем, который изложен в учебном пособии помогает накапливать и эффективно использовать полученную информацию из теоретического курса на всех стадиях учебного процесса, что важно для подготовки магистров и является необходимым звеном в научном познании практических основ биомедицинской электроники

Remote noninvasive allograft rejection monitoring for heart transplant recipients: study protocol for the novel evaluation with home electrocardiogram and remote transmission (NEW HEART) study

Background: Acute allograft rejection is a major cause of early mortality in the first year after heart transplantation in adults. Although endomyocardial biopsy (EMB) is not a perfect "gold standard" for a correct diagnosis of acute allograft rejection, it is considered the best available test and thus, is the current standard practice. Unfortunately, EMB is an invasive and costly procedure that is not without risk. Recent evidence suggests that acute allograft rejection causes delays in ventricular repolarization and thereby increases the cellular action potential duration resulting in a longer QT interval on the electrocardiogram (ECG). No prospective study to date has investigated whether such increases in the QT interval could provide early detection of acute allograft rejection. Therefore, in the Novel Evaluation With Home Electrocardiogram And Remote Transmission (NEW HEART) study, we plan to investigate the potential benefit of daily home QT interval monitoring to predict acute allograft rejection. Methods/design: The NEW HEART study is a prospective, double-blind, multi-center descriptive research study. A sample of 325 adult heart transplant recipients will be recruited within six weeks of transplant from three sites in the United States. Subjects will receive the HeartView™ ECG recorder and its companion Internet Transmitter, which will transmit the subject's ECG to a Core Laboratory. Subjects will be instructed to record and transmit an ECG recording daily for 6 months. An increase in the QTC interval from the previous day of at least 25 ms that persists for 3 consecutive days will be considered abnormal. The number and grade of acute allograft rejection episodes, as well as all-cause mortality, will be collected for one year following transplant surgery. Discussion: This study will provide "real world" prospective data to determine the sensitivity and specificity of QTC as an early non invasive marker of cellular rejection in transplant recipients during the first post-transplant year. A non-invasive indicator of early allograft rejection in heart transplant recipients has the potential to limit the number and severity of rejection episodes by reducing the time and cost of rejection surveillance and by shortening the time to recognition of rejection. Trial Registration: ClinicalTrials.gov: NCT0136580

Evolutionary Optimization of Atrial Fibrillation Diagnostic Algorithms

The goal of this research is to introduce an improved method for detecting atrial fibrillation (AF). The foundation of our algorithm is the irregularity of the RR intervals in the electrocardiogram (ECG) signal, and their correlation with AF. Three statistical techniques, including root mean squares of successive differences (RMSSD), turning points ratio (TPR), and Shannon entropy (SE), are used to detect RR interval irregularity. We use the Massachusetts Institution of Technology / Beth Israel Hospital (MIT-BIH) atrial fibrillation databases and their annotations to tune the parameters of the statistical methods by biogeography-based optimization (BBO), which is an evolutionary optimization algorithm. We trained each statistical method to diagnose AF on each database. Then each trained method was tested on the rest of the databases. We were able to obtain accuracy levels as high as 99 for the detection of AF in the trained databases. We obtained accuracy levels of up to 75 in the tested database

Progress Report No. 12

Progress report of the Biomedical Computer Laboratory, covering period 1 July 1975 to 30 June 1976

Advances in Integrated Circuits and Systems for Wearable Biomedical Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an impedance mapping technique that can be used to image the inner impedance distribution of the subject under test. It is non-invasive, inexpensive and radiation-free, while at the same time it can facilitate long-term and real-time dynamic monitoring. Thus, EIT lends itself particularly well to the development of a bio-signal monitoring/imaging system in the form of wearable technology. This work focuses on EIT system hardware advancement using complementary metal oxide semiconductor (CMOS) technology. It presents the design and testing of application specific integrated circuit (ASIC) and their successful use in two bio-medical applications, namely, neonatal lung function monitoring and human-machine interface (HMI) for prosthetic hand control. Each year fifteen million babies are born prematurely, and up to 30% suffer from lung disease. Although respiratory support, especially mechanical ventilation, can improve their survival, it also can cause injury to their vulnerable lungs resulting in severe and chronic pulmonary morbidity lasting into adulthood, thus an integrated wearable EIT system for neonatal lung function monitoring is urgently needed. In this work, two wearable belt systems are presented. The first belt features a miniaturized active electrode module built around an analog front-end ASIC which is fabricated with 0.35-µm high-voltage process technology with ±9 V power supplies and occupies a total die area of 3.9 mm². The ASIC offers a high power active current driver capable of up to 6 mAp-p output, and wideband active buffer for EIT recording as well as contact impedance monitoring. The belt has a bandwidth of 500 kHz, and an image frame rate of 107 frame/s. To further improve the system, the active electrode module is integrated into one ASIC. It contains a fully differential current driver, a current feedback instrumentation amplifier (IA), a digital controller and multiplexors with a total die area of 9.6 mm². Compared to the conventional active electrode architecture employed in the first EIT belt, the second belt features a new architecture. It allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It has intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement providing superior common-mode rejection ratio (CMRR) up to 74 dB, and with active gain, the noise level can be reduced by a factor of √3 using the adjacent scan. The second belt has a wider operating bandwidth of 1 MHz and multi-frequency operation. The image frame rate is 122 frame/s, the fastest wearable EIT reported to date. It measures impedance with 98% accuracy and has less than 0.5 Ω and 1° variation across all channels. In addition the ASIC facilitates several other functionalities to provide supplementary clinical information at the bedside. With the advancement of technology and the ever-increasing fusion of computer and machine into daily life, a seamless HMI system that can recognize hand gestures and motions and allow the control of robotic machines or prostheses to perform dexterous tasks, is a target of research. Originally developed as an imaging technique, EIT can be used with a machine learning technique to track bones and muscles movement towards understanding the human user’s intentions and ultimately controlling prosthetic hand applications. For this application, an analog front-end ASIC is designed using 0.35-µm standard process technology with ±1.65 V power supplies. It comprises a current driver capable of differential drive and a low noise (9μVrms) IA with a CMRR of 80 dB. The function modules occupy an area of 0.07 mm². Using the ASIC, a complete HMI system based on the EIT principle for hand prosthesis control has been presented, and the user’s forearm inner bio-impedance redistribution is assessed. Using artificial neural networks, bio-impedance redistribution can be learned so as to recognise the user’s intention in real-time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation. Experiments with five subjects show that the system can achieve an overall recognition accuracy of 95.8%

Developing/testing a new approach for assessing rapid visual identification of hematological cells using principles of visual cognition: a health science education study

The purpose of this study was the development and testing of a novel method for assessment of white blood cell (WBC) identification skills used in the field of Clinical Laboratory Sciences (CLS). A dual format exam was administered to both novices (students) and experts (laboratory professionals). Format 1 was similar to current assessment formats, simply presenting a series of single WBC images for identification. Format 2 applied principles of visual cognition, grouping WBCs for identification by patient and presenting multiple example images from the patient before requesting identification of individual cells. This novel exam format was intended to: (a) provide a contextualized visual background for single cell identifications, (b) mirror the process of WBC identification used in clinical practice, and (c) promote improved performance on difficult/atypical WBC identifications. The second phase of this study implemented qualitative methods to categorize the general cognitive processing styles used by novices/experts as either analytical or similarity-based. Cognitive processing styles were compared across the 2 levels of expertise as well as across exam formats. Statistical analyses did suggest that expert performance levels were significantly improved by the novel exam presentation format. Novice performance, however, was not significantly altered by exam format. Evaluation of response times indicated that expert response times were significantly shorter than novice response times in format 2, but not in format 1. In addition, analysis of qualitative data suggested that experts differed significantly from novices in their cognitive verbalizations for format 2, with experts making more statements at a higher cognitive level than did the novices. Format 1 verbalization differences were not found to be significant. Overall results indicated that the novel exam format invoked experts to implement similarity-based processing, allowing some identifications to be made at the level of the patient case, rather than simply at the feature identification level. Implications of this study include possible alterations to current certification/proficiency exam formats for questions requiring the visual identification of white blood cells. This study also suggests that using patient image sets as instructional stimuli may encourage the development of advanced cognitive processing skills in students