Cardiovascular instrumentation for spaceflight

The observation mechanisms dealing with pressure, flow, morphology, temperature, etc. are discussed. The approach taken in the performance of this study was to (1) review ground and space-flight data on cardiovascular function, including earlier related ground-based and space-flight animal studies, Mercury, Gemini, Apollo, Skylab, and recent bed-rest studies, (2) review cardiovascular measurement parameters required to assess individual performance and physiological alternations during space flight, (3) perform an instrumentation survey including a literature search as well as personal contact with the applicable investigators, (4) assess instrumentation applicability with respect to the established criteria, and (5) recommend future research and development activity. It is concluded that, for the most part, the required instrumentation technology is available but that mission-peculiar criteria will require modifications to adapt the applicable instrumentation to a space-flight configuration

Alternative Lead Systems for Diagnostic Electrocardiography: Validation and Clinical Applicability

The standard 12-lead electrocardiogram (ECG) remains one of the most important and most frequently used tools for diagnosing cardiac diseases, although several different examination modalities in cardio¬logy have been developed over the years. The standard ECG uses 10 electrodes placed on well-defined positions on the body, 6 on the torso and 4 distally on the limbs. Both industry and academia have invested many years in development of the criteria used to interpret the “diagnostic” standard ECG, and the waveform patterns are taught in medical school. In several situations, however – such as during long-term ECG monitoring or stress testing – use of the electrode positions of the standard ECG is not optimal because of the abundance of noise. In these situations, the limb electrodes must be placed proximally, often even on the torso, and the Mason-Likar (M-L) positions are commonly used. Interference with other clinical procedures, such as echocardiography, can also constitute a problem. An ECG-recording system with fewer electrodes and without any electrodes on the limbs that provides a 12-lead ECG similar to the standard ECG would be valuable. The so-called EASI system uses only 4 recording electrodes in easily determined locations on the torso from which the full 12-lead ECG can be derived. The 12-lead ECG derived from the EASI system has been evaluated in adults in several clinical situations. Physicians who use ECGs in their day-to-day work are often not aware of the differences between 12-lead ECGs recorded from standard versus alternative electrode positions, and they might use criteria developed for the standard ECG when interpreting an ECG obtained from an alternative lead system. This can lead to misinterpretation with the risk of potentially serious consequences for the patient. Optimizing the proximal positions for better concordance with the standard ECG would be of great value for improved diagnostic performance. A version of the “Lund” (LU) lead system has been reported to agree better with the standard lead system than does the M-L lead system, with regard to both Q-wave width and QRS frontal plane axis. To develop a uniform convention for ECG recording, i.e. both for diagnostic ECG and for monitoring, a recording must produce waveforms that have morphologies approximating those obtained with standard ECG and that has noise immunity close to that of M-L. The overall objectives of this thesis were 1) to further validate the EASI system to gain more knowledge about the agreement between EASI-derived and standard 12-lead ECGs, and 2) to investigate the possibility of optimizing the positions of proximally placed limb electrodes. EASI studies In Study I, age-specific transformation coefficients were determined for use in deriving 12-lead ECGs from the EASI signals. The agreement of the waveforms between simultaneously recorded standard and EASI-derived 12-lead ECGs in children (healthy and with various cardiac diagnoses) was studied. For children, it was better to use age-specific transformation coefficients than adult coefficients. The agreement between standard and EASI-derived ECGs was mostly good. In Study II, the intrareader variation of interpretations of 2 standard 12-lead ECGs was compared with the variation of interpretations of standard versus EASI-derived 12-lead ECGs in children (Study I population). The variation of the interpretation of standard versus EASI-derived ECGs was only slightly larger than the intrareader variation of interpretations of standard ECGs. In Study III, the amplitudes of myoelectric noise and baseline wander were compared between simultaneously recorded EASI-derived and M-L 12-lead ECGs in healthy adults. Overall, the 2 lead systems had similar susceptibilities to baseline wander, but EASI was less susceptible than M-L to myoelectric noise. In Study IV, differences in the estimated size of myocardial infarction (MI), as assessed by Selvester scores, were compared between standard and EASI-derived 12-lead ECGs among patients who had had an episode of chest pain suggestive of an acute coronary syndrome. These scores were also compared with MI size measured by cardiac magnetic resonance imaging (MRI). Estimated MI size did not differ significantly between the 2 lead systems, but neither the correlation nor the agreement between MRI and either of the 2 lead systems was very strong. Study to optimize the proximal positions of the limb electrodes In Study V, waveforms from the LU and M-L systems were compared with those from standard ECGs with regard to the QRS axis in the frontal plane and QRS changes of inferior MI. The noise immunities of the standard, LU, and M-L systems were also compared. LU produced ECG waveforms that more closely resembled those obtained with standard ECG than did M-L. The LU system was more noise-immune than was the standard system, and the noise immunities of the LU and the M-L systems were comparable

Transparent authentication: Utilising heart rate for user authentication

There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices - principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficents. Ten statistical features are extracted from each level of the coffecient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1 st , 2 nd , 3 rd and 4 th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25% As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 159

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1976

Applications of Vectorcardiography for Diagnosis and Risk Stratification in Subpopulations at Risk for Life-Threatening Arrhythmias

Introduction: Vectorcardiography, or 3-dimensional electrocardiography is a tool which can be used to identify subtle changes in the electrical forces of the heart, and which can be applied to atrial depolarization, ventricular depolarization and ventricular repolarization for prognostic and diagostic purposes. Methods: Kor’s regression-related and quasi orthogonal methods was used to derive vectorcardiographic parameters from the 12-lead electrocardiogram and applied to a cohort of cryptogenic stroke patients to assess atrial fibrillation, hypertrophic cardiomyopathy patients to assess for ventricular arrhythmias, applied with right-precordial directed quasi orthogonal method to arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVC/D) patients for diagnosis, and applied to ventricular repolarization only to patients with genotype-positive/phenotype-negative Long QT2 syndrome (KNCH2 mutation) to assess for cardiac events. Parametric and non-parameteric parameters were presented as mean ± standard deviation and median (1st to 3rd interquartile ranges). Pearson and Spearman correlation coefficients were used for parametric and non-parametric data, respectively. Odds ratios with univariate and multivariate analyses as well as hazard ratios and Kaplan-Meier curves are presented. P-values under 0.05 were represented as significant. Results: In cryptogenic stroke patients, first atrial fibrillation event was predicted by baseline P-wave duration divided by P-wave vector magnitude (p<0.05). In hypertrophic cardiomyopathy patients, the spatial peaks QRS-T angle differentiated sustained ventricular arrhythmias (VA) from no VA (P < 0.001) and at 124.1 degrees gave positive and negative predictive values and an odds ratio of 36.7%, 96.1%, and 14.2 (95% confidence interval: 3.1-65.6), respectively. Combined right precordial-directed parameters were able to identify ARVD/C patients who otherwise met criteria but did not meet any ECG-specfiic 2010 Taskforce criteria from controls with a positive predictive value of 90.0% and negative predictive value of 83.3%. In patients with genotype positive KCNH2 mutations, without prolongation of the QTc, when dichotomized by the median of 0.30 mV, a low T-wave vector magnitude (TwVM) was associated with elevated cardiac event risk compared to those with high TwVM (HR=2.55, 95%CI 1.07-6.04, p=0.034) and the genotype-negative family members (HR=2.64, 95%CI 1.64-4.24, p<0.001). Conclusion: Vector magnitudes and spatial angles, involving atrial and ventricular depolarization as well as ventricular repolarization, can be helpful in identifying disease as well as first-onset arrhythmia in subpopulations at risk for sudden death or stroke

Characterisation of atrial flutter variants based on the analysis of spatial vectorcardiographic trajectory from standard ECG

After atrial fibrillation, atrial flutter is the most common atrial tachyarrhythmia. Its diagnosis relies on the twelve lead electrocadriogram analysis of the distinctive waves in several leads. Nonetheless, the accurate identification of the type of atrial flutter still requires an invasive procedure. The maneuver for healing atrial flutter consists on ablating a section of the anatomy of the atria, to stop the macroreentrant circuit to keep happening, allowing the signal to travel to the ventricles in stead of staying at the atria. The region to ablate directly depends on the place at which the macroreentrant circuit is located, which at the same time depends on the type of atrial flutter. Being able to noninvasively detect the atrial flutter variant would produce a great advantage when healing this illness. The hypothesis stated at this dissertation is based on the slow conduction regions as the key factor to distinguish the atrial flutter class. This and unveiling further relations between cardiac illnesses and their signal’s alter ego are the purpose of this research project. With such aim, different methods are developed based on the vectorcardiographic representation of electrocardiograms from patients suffering from different atrial flutter types. These methods consist on the characterisation of vectorcardiographic signals from different standpoints. Besides, a mathematical model is implemented to create a large database with synthetic vectorcardiographic signals allowing to test the validity of the utilised methods. The results prove the importance of slow regions in the vectorcardiographic representation of the patient’s signals to characterise the atrial flutter type non-invasively. Furthermore, the analysis of the outcome of the different methods reveal a wide variety of features relating characteristics of the vectorcardiographic signal to the anatomy and physiology of this cardiac disease. Hence, not only results supporting the hypothesis were successful (taking into account some limitations), but also a variegated assortment of results unmasked remarkable relations among the vectorcardiographic signal and the characteristics of the atrial flutter disease.Ingeniería Biomédic

Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned

Evaluation of12-LeadElectrocardiogramReconstruction Methods forPatientMonitoring

In clinical practice, continuous recording of all leads of the 12-lead ECG is not always possible. For example, leads may fall off or signals may be noisy. Key information about the patient will then be unavailable, making retrospective assessment difficult. In telemetry or intensive care environments only a subset of leads can be recorded, because of technical and practical limitations. In this thesis methods to address these problems were developed and evaluated using ECG reconstruction methods with reduced lead sets of the 12-lead ECG. Reconstruction was performed with patient-specific and general reconstruction coefficients. Furthermore, methods were developed and evaluated to address continuous ECG registration problems which may occur as a result of changes in body position and differences in standard versus monitoring lead configurations