Aerospace medicine and biology: A continuing bibliography with indexes (supplement 323)

This bibliography lists 125 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April, 1989. Subject coverage includes; aerospace medicine and psychology, life support systems and controlled environments, safety equipment exobiology and extraterrestrial life, and flight crew behavior and performance

Real-Time Profiling of Respiratory Motion: Baseline Drift, Frequency Variation and Fundamental Pattern Change

To precisely ablate tumor in radiation therapy, it is important to locate the tumor position in real time during treatment. However, respiration-induced tumor motions are difficult to track. They are semi-periodic and exhibit variations in baseline, frequency and fundamental pattern (oscillatory amplitude and shape). In this study, we try to decompose the above-mentioned components from discrete observations in real time. Baseline drift, frequency (equivalently phase) variation and fundamental pattern change characterize different aspects of respiratory motion and have distinctive clinical indications. Furthermore, smoothness is a valid assumption for each one of these components in their own spaces, and facilitates effective extrapolation for the purpose of estimation and prediction. We call this process 'profiling' to reflect the integration of information extraction, decomposition, processing and recovery. The proposed method has three major ingredients: (1) real-time baseline and phase estimation based on elliptical shape tracking in augmented state space and Poincaré sectioning principle; (2) estimation of the fundamental pattern by unwarping the observation with phase estimate from the previous step; (3) filtering of individual components and assembly in the original temporal-displacement signal space. We tested the proposed method with both simulated and clinical data. For the purpose of prediction, the results are comparable to what one would expect from a human operator. The proposed approach is fully unsupervised and data driven, making it ideal for applications requiring economy, efficiency and flexibility.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85908/1/Fessler14.pd

Associations of pulmonary parameters with accelerometer data

Some papers of this thesis are not available in Munin: Paper 2. Dias, A.; Gorzelniak, L.; Jorres, R.; Fischer, R.; Hartvigsen, G.; Horsch,A.: 'Assessing Physical Activity in the daily life of cystic fibrosis patients', Journal of Pervasive Computing (2012), vol. 8(6):837–844. Available at http://dx.doi.org/10.1016/j.pmcj.2012.08.001 Paper 3. Gorzelniak, L.; Dias, A.; Schultz,K.; Wittmann, M.; Karrasch, S.; Jorres, R.; Horsch,A.: 'Comparison of recording positions of physical activity in severe COPD', Journal Of Chronic Obstructive Pulmonary Disease (2012), vol. 9(5):528-537. Available at http://dx.doi.org/10.3109/15412555.2012.708066 Paper 4. Dias, A.; Gorzelniak, L.; Schultz,K.;Wittmann, M.; Rudnik, J.;Jorres, R.; Horsch,A.: 'Classification of exacerbation episodes in Chronic Obstructive Pulmonary Disease patients' (manuscript) Paper 5. Ortlieb, S.; Gorzelniak, L.; Dias,A.; Schulz, H.; Horsch,A.: 'Recommendations for Collecting and Processing Accelerometry Data in Older Healthy People' (manuscript) Additional paper 1. Dias, A.; Gorzelniak, L.; Doring, A.; Hartvigsen, G.; Horsch, A.: 'Extracting Gait Parameters from Raw Data Accelerometers', Studies in Health Technology and Informatics (2011), vol. 169:445-449. Additional paper 2. Gorzelniak, L.; Dias, A.; Soyer, H.; Knoll, A.; Horsch, A.; 'Using a Robotic Arm to Assess the Variability of Motion Sensors', Studies in Health Technology and Informatics (2011), vol. 169:897-901. Additional paper 3. Chen, C.; Dias, A.; Knoll, A.; Horsch, A.: 'A Prototype of a Wireless Body Sensor Network for Healthcare Monitoring', Medical informatics in Europe (2011). Additional paper 4. Skrovseth, S.; Dias, A.; Gorzelniak, L.; Godtliebsen, F.; Horsch, A.: 'Scale-space methods for live processing of sensor data', Medical informatics in Europe (2012). Additional paper 7. Peters A, Döring A, Ladwig KH, Meisinger C, Linkohr B, Autenrieth C, Baumeister SE, Behr J, Bergner A, Bickel H, Bidlingmaier M, Dias A, Emeny RT, Fischer B, Grill E, Gorzelniak L, Hänsch H, Heidbreder S, Heier M, Horsch A, Huber D, Huber RM, Jörres RA, Kääb S, Karrasch S, Kirchberger I, Klug G, Kranz B, Kuch B, Lacruz ME, Lang O, Mielck A, Nowak D, Perz S, Schneider A, Schulz H, Müller M, Seidl H, Strobl R, Thorand B, Wende R, Weidenhammer W, Zimmermann AK, Wichmann HE, Holle R.: 'Multimorbidity and successful aging: the populationbased KORA-Age study', Zeitschrift für Gerontologie und Geriatrie (2011), vol. 44(2):41-54. Available at http://dx.doi.org/10.1007/s00391-011-0245-7In Europe it is estimated that the number of elderly people aged above 65 will have doubled by 2060. In several chronic pulmonary diseases patients can suffer recurrent exacerbation episodes that can lead to severe breathing or death. In this thesis we explore the association of physical activity to lung health parameters, focusing on cystic fibrosis and chronic obstructive pulmonary disease patients and a group of the general population. The main goals of the thesis were to assess the feasibility of classifying exacerbation episodes in cystic fibrosis and chronic obstructive pulmonary disease patients and to implement new parameters in the context of a cohort study. We conducted four distinct studies involving in total over 250 subjects. We asked them to wear a set of accelerometers, including GT3X and RT3, recording physical activity for up to 14 days. The data was processed and several features extracted that were used as inputs in three different classification algorithms: logarithmic regression, neural networks and support vector machines. We achieved an area under the curve of 67% with logarithmic regression, 83% with neural networks and 90% with support vector machines when classifying exacerbation episodes in chronic obstructive pulmonary disease. A neural network was achieved an accuracy of 85% distinguishing cystic fibrosis patients from healthy controls. We proposed, extracted and tested a large set of physical activity parameters for use in KORA-Age. The work on classification of exacerbations in COPD patients is, to our knowledge, the first attempt based on features from accelerometer data. Overall SVM showed to be the most robust classifier with an area under the curve of 90%. Nevertheless the number of patients and episodes is too low to draw definitive conclusions. The next step to classify exacerbations in COPD is to design a study with a statistically significant number of exacerbation episodes

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 355)

This bibliography lists 147 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during October, 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Characterization and interpretation of cardiovascular and cardiorespiratory dynamics in cardiomyopathy patients

Aplicat embargament des de la data de defensa fins el dia 20/5/2022The main objective of this thesis was to study the variability of the cardiac, respiratory and vascular systems through electrocardiographic (ECG), respiratory flow (FLW) and blood pressure (BP) signals, in patients with idiopathic (IDC), dilated (DCM), or ischemic (ICM) disease. The aim of this work was to introduce new indices that could contribute to characterizing these diseases. With these new indices, we propose methods to classify cardiomyopathy patients (CMP) according to their cardiovascular risk or etiology. In addition, a new tool was proposed to reconstruct artifacts in biomedical signals. From the ECG, BP and FLW signals, different data series were extracted: beat to beat intervals (BBI - ECG), systolic and diastolic blood pressure (SBP and DBP - BP), and breathing duration (TT - FLW). -Firstly, we propose a novel artifact reconstruction method applied to biomedical signals. The reconstruction process makes use of information from neighboring events while maintaining the dynamics of the original signal. The method is based on detecting the cycles and artifacts, identifying the number of cycles to reconstruct, and predicting the cycles used to replace the artifact segments. The reconstruction results showed that most of the artifacts were correctly detected, and physiological cycles were incorrectly detected as artifacts in fewer than 1% of the cases. The second part is related to the cardiac death risk stratification of patients based on their left ventricular ejection (LVEF), using the Poincaré plot analysis, and classified as low (LVEF > 35%) or high (LVEF = 35%) risk. The BBI, SBP, and IT series of 46 CMP patients were applied. The linear discriminant analysis and support vector machines (SVM) classification methods were used. When comparing low risk vs high risk, an accuracy of 98 12% was obtained. Our results suggest that a dysfunction in the vagal activity could prevent the body from correctly maintaining circulatory homeostasis Next, we studied cardio-vascular couplings based on heart rate (HRV) and blood pressure (BPV) variability analyses in order to introduce new indices for noninvasive risk stratification in IDC patients. The ECG and BP signals of 91 IDC patients, and 49 healthy subjects were used. The patients were stratified by their sudden cardiac death risk as: high risk (IDCHR), when after two years the subject either died or suffered complications, or low risk (IDCLR) otherwise. Several indices were extracted from the BBI and SBP, and analyzed using the segmented Poincaré plot analysis, the high-resolution joint symbolic dynamics, and the normalized short time partial directed coherence methods. SVM models were built to classify these patients based on their sudden cardiac death risk. The SVM IDCLR vs IDCHR model achieved 98 9% accuracy with an area under the curve (AUC) of 0.96. Our results suggest that IDCHR patients have decreased HRV and increased BPV compared to both the IDCLR patients and the control subjects, suggesting a decrease in their vagal activity and the compensation of sympathetic activity. Lastly, we analyzed the cardiorespiratory interaction associated with the systems related to ICM and DCM disease. We propose an analysis based on vascular activity as the input and output of the baroreflex response. The aim was to analyze the suitability of cardiorespiratory and vascular interactions for the classification of ICM and DCM patients. We studied 41 CMP patients and 39 healthy subjects. Three new sub-spaces were defined: 'up' for increasing values, 'down' for decreasing values, and 'no change' otherwise, and a three-dimensional representation was created for each sub-space that was characterized statistically and morphologically. The resulting indices were used to classify the patients by their etiology through SVM models achieving 92.7% accuracy for ICM vs DCM patients comparison. The results reflected a more pronounced deterioration of the autonomous regulation in DCM patients.El objetivo de esta tesis fue estudiar la variabilidad de los sistemas cardíaco, respiratorio y vascular a través de señales electrocardiográficas (ECG), de flujo respiratorio (FLW) y de presión arterial (BP), en pacientes con cardiopatía idiopática (IDC). dilatada (DCM) o isquémica (ICM). El objetivo de este trabajo fue introducir nuevos indices que contribuyan a caracterizar estas enfermedades. Proponemos métodos para clasificar pacientes con cardiomiopatía (CMP) de acuerdo con su riesgo cardiovascular o etiología. Además, se propuso una nueva herramienta para reconstruir artefactos en señales biomédicas. De las señales de ECG, BP y FLW, se extrajeron diferentes series temporales: intervalos latido-a-latido (BBI - ECG), presión arterial sistólica y diastólica (SBP y DBP - BP) y la duración de la respiración (TT - FLW). En primer lugar, proponemos un método de reconstrucción de artefactos aplicado a señales biomédicas. El proceso de reconstrucción usa la información de eventos vecinos manteniendo la dinámica de la señal. El método se basa en detectar ciclos y artefactos, en identificar el número de ciclos a reconstruir y en predecir los ciclos utilizados para reemplazar los artefactos. La mayoría de los artefactos probados fueron detectados y reconstruidos correctamente y los ciclos fisiológicos fueron detectados incorrectamente como artefactos en menos del 1% de los casos, La segunda parte está relacionada con la estratificación de riesgo de muerte cardiovascular en función de la fracción de eyección ventricular izquierda (FEVI), mediante el análisis de Poincaré, en bajo (FEVI > 35%) y alto riesgo (FEVI 5 35%). Se utilizaron las series BBI, SBP y TT de 46 pacientes con CMP. Se utilizaron para la clasificación el análisis discriminante lineal y las máquinas de soporte vectorial (SVM). Al comparar los pacientes de bajo y alto riesgo, se obtuvo una exactitud del 98%. Los resultados sugieren la disfunción de la actividad vagal en pacientes de alto riesgo. A continuación, estudiamos los acoplamientos cardiovasculares basados en el análisis de la variabilidad de la frecuencia cardiaca (HRV) y la presión arterial (BPV) para introducir nuevos índices de estratificación de riesgo en pacientes con IDC. Se utilizaron las señales de ECG y BP de 91 pacientes con IDC y 49 sujetos sanos. Los pacientes fueron estratificados por su riesgo cardíaco como: alto riesgo (IDCHR), cuando después de dos años el sujeto murió, o bajo riesgo (IDCLR) en otro caso. Se extrajeron indices utilizando el análisis de Poincaré segmentado, la dinámica simbólica articulada de alta resolución y la coherencia parcial dirigida a corto plazo normalizada. Se construyeron modelos SVM para clasificar a estos pacientes en función de su riesgo cardiovascular. El modelo IDCLR vs IDCHR logró una exactitud del 98% con un área bajo la curva de 0.96. Los resultados sugieren que los pacientes IDCHR tienen sus HRV y BPV disminuidos en comparación con los pacientes IDCLR, lo que sugiere una disminución en su actividad vagal y la compensación de la actividad simpática. Finalmente, analizamos la interacción cardiorrespiratoria asociada con los sistemas relacionados con ICM y DCM. Proponemos un análisis basado en la actividad vascular como entrada y salida de la respuesta baroreflectora. El objetivo fue analizar la capacidad de las interacciones cardiorrespiratorias y vasculares para la clasificación de pacientes con ICM y DCM. Estudiamos 41 pacientes con CMP y 39 sujetos sanos. Se definieron tres sub-espacios: 'up' para valores crecientes, 'down' para los decrecientes, y 'no-change' en otro caso, y se creó una representación tridimensional que se caracterizó estadística y morfológicamente. Los indices resultantes se usaron para clasificar a los pacientes por su etiología con modelos SVM que lograron una exactitud de 92% cuando los pacientes ICM y DCM fueron comparados. Los resultados reflejaron un deterioro más pronunciado de la regulación autónoma en pacientes con DCM.Postprint (published version

A personalized rehabilitation system based on wireless motion capture sensors

We live in an aging society, an issue that will be exacerbated in the coming decades, due to low birth rates and increasing life expectancy. With the decline in physical and cognitive functions with age, it is of the utmost importance to maintain regular physical activity,in order to preserve an individual’s mobility, motor capabilities and coordination. Within this context, thispaper describes the development of a wireless sensor network and its application in a human motion capturesystem based on wearable inertial and magnetic sensors. The goal is to enable, through continuous real-time monitoring, the creation of a personalized home-based rehabilitation system for the elderly population and/or injured people. Within this system, the user can benefit from an assisted mode, in which their movements can be compared to a reference motion model of the same movements, resulting in visual feedback alerts given by the application. This motion model can be created previously, in a ‘learning phase’, under supervision of a caregiver.Fundação para a Ciência e a Tecnologia (FCT

Breathing Monitoring and Pattern Recognition with Wearable Sensors

This chapter introduces the anatomy and physiology of the respiratory system, and the reasons for measuring breathing events, particularly, using wearable sensors. Respiratory monitoring is vital including detection of sleep apnea and measurement of respiratory rate. The automatic detection of breathing patterns is equally important in other respiratory rehabilitation therapies, for example, magnetic resonance exams for respiratory triggered imaging, and synchronized functional electrical stimulation. In this context, the goal of many research groups is to create wearable devices able to monitor breathing activity continuously, under natural physiological conditions in different environments. Therefore, wearable sensors that have been used recently as well as the main signal processing methods for breathing analysis are discussed. The following sensor technologies are presented: acoustic, resistive, inductive, humidity, acceleration, pressure, electromyography, impedance, and infrared. New technologies open the door to future methods of noninvasive breathing analysis using wearable sensors associated with machine learning techniques for pattern detection

A Stable Sparse Fear Memory Trace in Human Amygdala

Pavlovian fear conditioning is highly conserved across species, providing a powerful model of aversive learning. In rodents, fear memory is stored and reactivated under the influence of the amygdala. There is no evidence for an equivalent mechanism in primates, and an opposite mechanism is proposed whereby primate amygdala contributes only to an initial phase of aversive learning, subsequently ceding fear memory to extra-amygdalar regions. Here, we reexamine this question by exploiting human high-resolution functional magnetic resonance imaging in conjunction with multivariate methods. By assuming a sparse neural coding, we show it is possible, at an individual subject level, to discriminate responses to conditioned (CS+ and CS-) stimuli in both basolateral and centro-cortical amygdala nuclei. The strength of this discrimination increased over time and was tightly coupled to the behavioral expression of fear, consistent with an expression of a stable fear memory trace. These data highlight that the human basolateral and centro-cortical amygdala support initial learning as well more enduring fear memory storage. A sparse neuronal representation for fear, here revealed by multivariate pattern classification, resolves why an enduring memory trace has proven elusive in previous human studies

Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community