Symbolic Dynamic Analysis of Relations Between Cardiac and Breathing Cycles in Patients on Weaning Trials

Traditional time-domain techniques of data analysis are often not sufficient to characterize the complex dynamics of the cardiorespiratory interdependencies during the weaning trials. In this paper, the interactions between the heart rate (HR) and the breathing rate (BR) were studied using joint symbolic dynamic analysis. A total of 133 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The word distribution matrix enabled a coarse-grained quantitative assessment of short-term nonlinear analysis of the cardiorespiratory interactions. The histogram of the occurrence probability of the cardiorespiratory words presented a higher homogeneity in group F than in group S, measured with a higher number of forbidden words in group S as well as a higher number of words whose probability of occurrence is higher than a probability threshold in group S. The discriminant analysis revealed the best results when applying symbolic dynamic variables. Therefore, we hypothesize that joint symbolic dynamic analysis provides enhanced information about different interactions between HR and BR, when comparing patients with successful weaning and patients that failed to maintain spontaneous breathing in the weaning procedure

Caracterização e detecção automática de eventos epileptiformes em sinais de eletroencefalograma por dinâmica simbólica

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2012.O presente trabalho apresenta uma nova metodologia para caracterização, detecção e classificação de sinais de exames de eletroencefalografia (EEG), visando o aprimoramento e agilidade na obtenção de diagnósticos com o objetivo de auxiliar o profissional de saúde, uma vez que os exames de EEG são caracterizados por longos trechos de registros temporais da atividade elétrica do cérebro, que devem ser minuciosamente examinados pelos médicos. Devido à dificuldade associada à caracterização e à detecção de sinais epileptiformes e à importância do diagnóstico, existem na literatura vários métodos desenvolvidos para abordar este problema, tais como: Métodos Auto-Regressivos (AR), Aproximação da Energia (TEO), Análise de Domínio de Freqüência, Análise de Tempo-Frequência, Transformada de Wavelets e Métodos Não- Lineares como os modelos estatísticos. Porém, nenhum modelo não foi capaz de atingir uma performance inteiramente confiável em seus resultados, onde a média de acertos entre os trabalhos expostos na literatura fica em torno de 80,7%, um índice de acertos que apesar de significativo, ainda não é satisfatório para este tipo de exame. A fim de contribuir para o estudo do problema, propôs-se neste trabalho a aplicação da Dinâmica Simbólica para caracterização dos sinais, que se baseia na representação por seqüências de símbolos do estado do sistema e operadores de mudança de estado, e algoritmos genéticos para a otimização da representação, de forma que os sinais epileptiformes pudessem ser distinguidos dos sinais considerados normais por uma rede neural artificial, treinada para este fim. Os resultados obtidos demonstram que a metodologia fornece 92,4% de precisão e 96% de acerto. _______________________________________________________________________________________ ABSTRACTThis paper presents a new technique for characterization and classification of signals of tests electroencephalography (EEG), seeking to improve agility and make a diagnosis in order to assist the professional, since the EEG tests are characterized by long stretches of time records electrical activity of the brain that should be thoroughly examined by doctors. Due to the difficulty associated with the characterization and detection of epileptiform signs and the importance of diagnosis exist in literature several methods developed to address this problem, such as autoregressive method (AR), Energy Approach (TEO), Domain Analysis Frequency, Time-Frequency Analysis, Wavelet Transform and Non-Linear methods such as statistical models. But no model has not been able to achieve a performance fully confident in their results, where the mean score among the works exhibited in the literature is around 80.7%, although a number is not a significant acceptable value for this type of examination. To contribute to the study of the problem, proposed in this paper the application of symbolic dynamics for the characterization of signals, which is based on representation by sequences of symbols of the state of the system and operators of state change, and genetic algorithms for optimization of the representation, so that the signals could be distinguished from epileptiform signals considered normal for an artificial neural network trained for this purpose. The results demonstrate that the method provides 92.4% precision and 96% accuracy

Breathing pattern characterization in patients with respiratory and cardiac failure

El objetivo principal de la tesis es estudiar los patrones respiratorios de pacientes en proceso de extubación y pacientes con insuficiencia cardiaca crónica (CHF), a partirde la señal de flujo respiratorio. La información obtenida de este estudio puede contribuir a la comprensión de los procesos fisiológicos subyacentes,y ayudar en el diagnóstico de estos pacientes. Uno de los problemas más desafiantes en unidades de cuidados intensivos es elproceso de desconexión de pacientes asistidos mediante ventilación mecánica. Más del 10% de pacientes que se extuban tienen que ser reintubados antes de 48 horas. Una prueba fallida puede ocasionar distrés cardiopulmonar y una mayor tasa de mortalidad. Se caracterizó el patrón respiratorio y la interacción dinámica entre la frecuenciacardiaca y frecuencia respiratoria, para obtener índices no invasivos que proporcionen una mayor información en el proceso de destete y mejorar el éxito de la desconexión.Las señales de flujo respiratorio y electrocardiográfica utilizadas en este estudio fueron obtenidas durante 30 minutos aplicando la prueba de tubo en T. Se compararon94 pacientes que tuvieron éxito en el proceso de extubación (GE), 39 pacientes que fracasaron en la prueba al mantener la respiración espontánea (GF), y 21 pacientes quesuperaron la prueba con éxito y fueron extubados, pero antes de 48 horas tuvieron que ser reintubados (GR). El patrón respiratorio se caracterizó a partir de las series temporales. Se aplicó la dinámica simbólica conjunta a las series correspondientes a las frecuencias cardiaca y respiratoria, para describir las interacciones cardiorrespiratoria de estos pacientes. Técnicas de "clustering", ecualización del histograma, clasificación mediante máquinasde soporte vectorial (SVM) y técnicas de validación permitieron seleccionar el conjunto de características más relevantes. Se propuso una nueva métrica B (índice de equilibrio) para la optimización de la clasificación con muestras desbalanceadas. Basado en este nuevo índice, aplicando SVM, se seleccionaron las mejores características que mantenían el mejor equilibrio entre sensibilidad y especificidad en todas las clasificaciones. El mejor resultado se obtuvo considerando conjuntamente la precisión y el valor de B, con una clasificación del 80% entre los grupos GE y GF, con 6 características. Clasificando GE vs. el resto de los pacientes, el mejor resultado se obtuvo con 9 características, con 81%. Clasificando GR vs. GE y GR vs. el resto de pacientes la precisión fue del 83% y 81% con 9 y 10 características, respectivamente. La tasa de mortalidad en pacientes con CHF es alta y la estratificación de estospacientes en función del riesgo es uno de los principales retos de la cardiología contemporánea. Estos pacientes a menudo desarrollan patrones de respiraciónperiódica (PB) incluyendo la respiración de Cheyne-Stokes (CSR) y respiración periódica sin apnea. La respiración periódica en estos pacientes se ha asociadocon una mayor mortalidad, especialmente en pacientes con CSR. Por lo tanto, el estudio de estos patrones respiratorios podría servir como un marcador de riesgo y proporcionar una mayor información sobre el estado fisiopatológico de pacientes con CHF. Se pretende identificar la condición de los pacientes con CHFde forma no invasiva mediante la caracterización y clasificación de patrones respiratorios con PBy respiración no periódica (nPB), y patrón de sujetos sanos, a partir registros de 15minutos de la señal de flujo respiratorio. Se caracterizó el patrón respiratorio mediante un estudio tiempo-frecuencia estacionario y no estacionario, de la envolvente de la señal de flujo respiratorio. Parámetros relacionados con la potencia espectral de la envolvente de la señal presentaron losmejores resultados en la clasificación de sujetos sanos y pacientes con CHF con CSR, PB y nPB. Las curvas ROC validan los resultados obtenidos. Se aplicó la "correntropy" para una caracterización tiempo-frecuencia mas completa del patrón respiratorio de pacientes con CHF. La "corretronpy" considera los momentos estadísticos de orden superior, siendo más robusta frente a los "outliers". Con la densidad espectral de correntropy (CSD) tanto la frecuencia de modulación como la dela respiración se representan en su posición real en el eje frecuencial. Los pacientes con PB y nPB, presentan diferentesgrados de periodicidad en función de su condición, mientras que los sujetos sanos no tienen periodicidad marcada. Con único parámetro se obtuvieron resultados del 88.9% clasificando pacientes PB vs. nPB, 95.2% para CHF vs. sanos, 94.4% para nPB vs. sanos.The main objective of this thesis is to study andcharacterize breathing patterns through the respiratory flow signal applied to patients on weaning trials from mechanicalventilation and patients with chronic heart failure (CHF). The aim is to contribute to theunderstanding of the underlying physiological processes and to help in the diagnosis of these patients. One of the most challenging problems in intensive care units is still the process ofdiscontinuing mechanical ventilation, as over 10% of patients who undergo successfulT-tube trials have to be reintubated in less than 48 hours. A failed weaning trial mayinduce cardiopulmonary distress and carries a higher mortality rate. We characterize therespiratory pattern and the dynamic interaction between heart rate and breathing rate toobtain noninvasive indices that provide enhanced information about the weaningprocess and improve the weaning outcome. This is achieved through a comparison of 94 patients with successful trials (GS), 39patients who fail to maintain spontaneous breathing (GF), and 21 patients who successfully maintain spontaneous breathing and are extubated, but require thereinstitution of mechanical ventilation in less than 48 hours because they are unable tobreathe (GR). The ECG and the respiratory flow signals used in this study were acquired during T-tube tests and last 30 minute. The respiratory pattern was characterized by means of a number of respiratory timeseries. Joint symbolic dynamics applied to time series of heart rate and respiratoryfrequency was used to describe the cardiorespiratory interactions of patients during theweaning trial process. Clustering, histogram equalization, support vector machines-based classification (SVM) and validation techniques enabled the selection of the bestsubset of input features. We defined a new optimization metric for unbalanced classification problems, andestablished a new SVM feature selection method, based on this balance index B. The proposed B-based SVM feature selection provided a better balance between sensitivityand specificity in all classifications. The best classification result was obtained with SVM feature selection based on bothaccuracy and the balance index, which classified GS and GFwith an accuracy of 80%, considering 6 features. Classifying GS versus the rest of patients, the best result wasobtained with 9 features, 81%, and the accuracy classifying GR versus GS, and GR versus the rest of the patients was 83% and 81% with 9 and 10 features, respectively.The mortality rate in CHF patients remains high and risk stratification in these patients isstill one of the major challenges of contemporary cardiology. Patients with CHF oftendevelop periodic breathing patterns including Cheyne-Stokes respiration (CSR) and periodic breathing without apnea. Periodic breathing in CHF patients is associated withincreased mortality, especially in CSR patients. Therefore it could serve as a risk markerand can provide enhanced information about thepathophysiological condition of CHF patients. The main goal of this research was to identify CHF patients' condition noninvasively bycharacterizing and classifying respiratory flow patterns from patients with PB and nPBand healthy subjects by using 15-minute long respiratory flow signals. The respiratory pattern was characterized by a stationary and a nonstationary time-frequency study through the envelope of the respiratory flow signal. Power-related parameters achieved the best results in all of the classifications involving healthy subjects and CHF patients with CSR, PB and nPB and the ROC curves validated theresults obtained for the identification of different respiratory patterns. We investigated the use of correntropy for the spectral characterization of respiratory patterns in CHF patients. The correntropy function accounts for higher-order moments and is robust to outliers. Due to the former property, the respiratory and modulationfrequencies appear at their actual locations along the frequency axis in the correntropy spectral density (CSD). The best results were achieved with correntropy and CSD-related parameters that characterized the power in the modulation and respiration discriminant bands, definedas a frequency interval centred on the modulation and respiration frequency peaks,respectively. All patients, i.e. both PB and nPB, exhibit various degrees of periodicitydepending on their condition, whereas healthy subjects have no pronounced periodicity.This fact led to excellent results classifying PB and nPB patients 88.9%, CHF versushealthy 95.2%, and nPB versus healthy 94.4% with only one parameter.Postprint (published version

Zhodnocení použitelnosti kontrolních a zkusných ploch pro hodnocení vlivu zvěře na les

[ES] La Bioingeniería constituye un área de trabajo e investigación multidisciplinar entre las ingenierías y la medicina que resulta de un interés humano, social y económico creciente. La automática en particular, en sus aspectos de percepción, modelado, control, monitorización, actuación e interacción, entre otros, ofrece importantes conocimientos y herramientas para abordar los problemas relacionados con el diagnóstico y el seguimiento de patologías, con las necesidades funcionales especiales e igualmente con las diferentes terapias a aplicar. Este tutorial presenta aspectos relacionados con el estado del arte y últimos avances en los siguientes campos: Interfaces para la interacción y comunicación de personas con discapacidad, robótica para la rehabilitación y compensación funcional, y sistemas para la mejora de la terapia clínica.[EN] Bioengineering is a field of interdisciplinary research between engineering and medicine resulting from a growing human, social and economic interest. Automatica in particular, with its aspects of perception, modeling, control, monitoring, action and interaction, among others, provides important insights and tools to overcome problems related to diagnosis and monitoring of diseases, to special functional needs and also with different applied treatments. This tutorial presents aspects related to the state of the art and recent advances in the following areas: Interfaces for interaction and communication of people with disabilities, rehabilitation robotics and functional compensation, and systems to improve clinical therapy.Los autores desean agradecer el apoyo recibido en su actividad investigadora al Ministerio de Ciencia e Innovación (proyectos DPI2008-06875-C03-03 y TEC2008-002754).Ceres, R.; Mañanas, MA.; Azorín, JM. (2011). Interfaces y Sistemas en Rehabilitación y Compensación Funcional para la Autonomía Personal y la Terapia Clínica. Revista Iberoamericana de Automática e Informática industrial. 8(2):5-15. https://doi.org/10.1016/S1697-7912(11)70021-8OJS51582Achermann, P., & Borbély, A. A. (1998). Coherence analysis of the human sleep electroencephalogram. Neuroscience, 85(4), 1195-1208. doi:10.1016/s0306-4522(97)00692-1Alonso, J. F., Mañanas, M. A., Romero, S., Hoyer, D., Riba, J., & Barbanoj, M. J. (2009). Drug effect on EEG connectivity assessed by linear and nonlinear couplings. Human Brain Mapping, 31(3), 487-497. doi:10.1002/hbm.20881Allison, B. Z., & Pineda, J. A. (2003). ERPs evoked by different matrix sizes: implications for a brain computer interface (bci) system. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2), 110-113. doi:10.1109/tnsre.2003.814448Andreasen, D. S., Allen, S. K., & Backus, D. A. (s. f.). Exoskeleton with EMG Based Active Assistance for Rehabilitation. 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005. doi:10.1109/icorr.2005.1501113Arimura, H., Magome, T., Yamashita, Y., & Yamamoto, D. (2009). Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images. Algorithms, 2(3), 925-952. doi:10.3390/a2030925AZORIN POVEDA, J. M., IÁÑEZ MARTINEZ, E., FERNANDEZ JOVER, E., & SABATER NAVARRO, J. M. (2010). INTERACCION OCULAR CON ROBOTS, UNA AYUDA PARA DISCAPACITADOS. DYNA INGENIERIA E INDUSTRIA, 85(3), 768-776. doi:10.6036/3738Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., … Flor, H. (1999). A spelling device for the paralysed. Nature, 398(6725), 297-298. doi:10.1038/18581Barea, R., Boquete, L., Mazo, M., & López, E. (2002). Journal of Intelligent and Robotic Systems, 34(3), 279-299. doi:10.1023/a:1016359503796Baselli, G., Porta, A., Rimoldi, O., Pagani, M., & Cerutti, S. (1997). Spectral decomposition in multichannel recordings based on multivariate parametric identification. IEEE Transactions on Biomedical Engineering, 44(11), 1092-1101. doi:10.1109/10.641336Bennett, J. W., & Glasziou, P. P. (2003). Computerised reminders and feedback in medication management: a systematic review of randomised controlled trials. Medical Journal of Australia, 178(5), 217-222. doi:10.5694/j.1326-5377.2003.tb05166.xCaminal, P., Vallverdu, M., Giraldo, B., Benito, S., Vazquez, G., & Voss, A. (2005). Optimized Symbolic Dynamics Approach for the Analysis of the Respiratory Pattern. IEEE Transactions on Biomedical Engineering, 52(11), 1832-1839. doi:10.1109/tbme.2005.856293Caminal, P., Giraldo, B. F., Vallverdú, M., Benito, S., Schroeder, R., & Voss, A. (2010). Symbolic Dynamic Analysis of Relations Between Cardiac and Breathing Cycles in Patients on Weaning Trials. Annals of Biomedical Engineering, 38(8), 2542-2552. doi:10.1007/s10439-010-0027-1Carmena, J. M., Lebedev, M. A., Crist, R. E., O’Doherty, J. E., Santucci, D. M., Dimitrov, D. F., … Nicolelis, M. A. L. (2003). Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates. PLoS Biology, 1(2), e42. doi:10.1371/journal.pbio.0000042Ceres, R., Pons, J. L., Calderon, L., Jimenez, A. R., & Azevedo, L. (2005). A robotic vehicle for disabled children. IEEE Engineering in Medicine and Biology Magazine, 24(6), 55-63. doi:10.1109/memb.2005.1549731Ceres, R., Pons, J. L., Calderón, L., & Moreno, J. (2008). La robótica en la discapacidad. Desarrollo de la prótesis diestra de extremidad inferior manus-hand. Revista Iberoamericana de Automática e Informática Industrial RIAI, 5(2), 60-68. doi:10.1016/s1697-7912(08)70145-6Cozens, J. A. (1999). Robotic assistance of an active upper limb exercise in neurologically impaired patients. IEEE Transactions on Rehabilitation Engineering, 7(2), 254-256. doi:10.1109/86.769416Cullell, A., Moreno, J. C., Rocon, E., Forner-Cordero, A., & Pons, J. L. (2009). Biologically based design of an actuator system for a knee–ankle–foot orthosis. Mechanism and Machine Theory, 44(4), 860-872. doi:10.1016/j.mechmachtheory.2008.04.001Dipietro, L., Ferraro, M., Palazzolo, J. J., Krebs, H. I., Volpe, B. T., & Hogan, N. (2005). Customized Interactive Robotic Treatment for Stroke: EMG-Triggered Therapy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(3), 325-334. doi:10.1109/tnsre.2005.850423Farkas, C., Hamilton-Wright, A., Parsaei, H., & Stashuk, D. W. (2010). A Review of Clinical Quantitative Electromyography. Critical Reviews™ in Biomedical Engineering, 38(5), 467-485. doi:10.1615/critrevbiomedeng.v38.i5.30Farry, K. A., Walker, I. D., & Baraniuk, R. G. (1996). Myoelectric teleoperation of a complex robotic hand. IEEE Transactions on Robotics and Automation, 12(5), 775-788. doi:10.1109/70.538982Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70(6), 510-523. doi:10.1016/0013-4694(88)90149-6Fazekas, G., Horvath, M., & Toth, A. (2006). A novel robot training system designed to supplement upper limb physiotherapy of patients with spastic hemiparesis. International Journal of Rehabilitation Research, 29(3), 251-254. doi:10.1097/01.mrr.0000230050.16604.d9Ferenets, R., Lipping, T., Anier, A., Jantti, V., Melto, S., & Hovilehto, S. (2006). Comparison of Entropy and Complexity Measures for the Assessment of Depth of Sedation. IEEE Transactions on Biomedical Engineering, 53(6), 1067-1077. doi:10.1109/tbme.2006.873543Galán, F., Nuttin, M., Lew, E., Ferrez, P. W., Vanacker, G., Philips, J., & Millán, J. del R. (2008). A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots. Clinical Neurophysiology, 119(9), 2159-2169. doi:10.1016/j.clinph.2008.06.001Galindo, C., Gonzalez, J., & Fernandez-Madrigal, J.-A. (2006). Control Architecture for Human–Robot Integration: Application to a Robotic Wheelchair. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 36(5), 1053-1067. doi:10.1109/tsmcb.2006.874131Goud, R., van Engen-Verheul, M., de Keizer, N. F., Bal, R., Hasman, A., Hellemans, I. M., & Peek, N. (2010). The effect of computerized decision support on barriers to guideline implementation: A qualitative study in outpatient cardiac rehabilitation. International Journal of Medical Informatics, 79(6), 430-437. doi:10.1016/j.ijmedinf.2010.03.001Xiaorong Gao, Dingfeng Xu, Ming Cheng, & Shangkai Gao. (2003). A bci-based environmental controller for the motion-disabled. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2), 137-140. doi:10.1109/tnsre.2003.814449Hermens, H. J., Freriks, B., Disselhorst-Klug, C., & Rau, G. (2000). Development of recommendations for SEMG sensors and sensor placement procedures. Journal of Electromyography and Kinesiology, 10(5), 361-374. doi:10.1016/s1050-6411(00)00027-4Hernandez, A. M., Mananas, M. A., & Costa-Castello, R. (2008). Learning Respiratory System Function in BME Studies by Means of a Virtual Laboratory: RespiLab. IEEE Transactions on Education, 51(1), 24-34. doi:10.1109/te.2007.893355Hogan, N., Krebs, H. I., Rohrer, B., Palazzolo, J. J., Dipietro, L., Fasoli, S. E., … Volpe, B. T. (2006). Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery. The Journal of Rehabilitation Research and Development, 43(5), 605. doi:10.1682/jrrd.2005.06.0103Hutchinson, T. E., White, K. P., Martin, W. N., Reichert, K. C., & Frey, L. A. (1989). Human-computer interaction using eye-gaze input. IEEE Transactions on Systems, Man, and Cybernetics, 19(6), 1527-1534. doi:10.1109/21.44068Iáñez, E., Azorín, J. M., Úbeda, A., Ferrández, J. M., & Fernández, E. (2010). Mental tasks-based brain–robot interface. Robotics and Autonomous Systems, 58(12), 1238-1245. doi:10.1016/j.robot.2010.08.007Iturrate, I., Antelis, J. M., Kubler, A., & Minguez, J. (2009). A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation. IEEE Transactions on Robotics, 25(3), 614-627. doi:10.1109/tro.2009.2020347Jezernik, S., Colombo, G., Keller, T., Frueh, H., & Morari, M. (2003). Robotic Orthosis Lokomat: A Rehabilitation and Research Tool. Neuromodulation: Technology at the Neural Interface, 6(2), 108-115. doi:10.1046/j.1525-1403.2003.03017.xKawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005). Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ, 330(7494), 765. doi:10.1136/bmj.38398.500764.8fKumar, A., & Krol, G. (1992). Binocular Infrared Oculography. The Laryngoscope, 102(4), 367-378. doi:10.1288/00005537-199204000-00002Lapatki, B. G., Oostenveld, R., Van Dijk, J. P., Jonas, I. E., Zwarts, M. J., & Stegeman, D. F. (2010). Optimal placement of bipolar surface EMG electrodes in the face based on single motor unit analysis. Psychophysiology, 47(2), 299-314. doi:10.1111/j.1469-8986.2009.00935.xLauer, R. T., Stackhouse, C. A., Shewokis, P. A., Smith, B. T., Tucker, C. A., & McCarthy, J. (2007). A time–frequency based electromyographic analysis technique for use in cerebral palsy. Gait & Posture, 26(3), 420-427. doi:10.1016/j.gaitpost.2006.10.015MacIntyre, N. R. (2001). Evidence-Based Guidelines for Weaning and Discontinuing Ventilatory Support. Chest, 120(6), 375S-395S. doi:10.1378/chest.120.6_suppl.375sMcGill, S. M., & Karpowicz, A. (2009). Exercises for Spine Stabilization: Motion/Motor Patterns, Stability Progressions, and Clinical Technique. Archives of Physical Medicine and Rehabilitation, 90(1), 118-126. doi:10.1016/j.apmr.2008.06.026Merletti, R., Holobar, A., & Farina, D. (2008). Analysis of motor units with high-density surface electromyography. Journal of Electromyography and Kinesiology, 18(6), 879-890. doi:10.1016/j.jelekin.2008.09.002Millán, J. del R., Renkens, F., Mouriño, J., & Gerstner, W. (2004). Brain-actuated interaction. Artificial Intelligence, 159(1-2), 241-259. doi:10.1016/j.artint.2004.05.008Nicolelis, M. A. L. (2001). Actions from thoughts. Nature, 409(6818), 403-407. doi:10.1038/35053191Niemenlehto, P.-H., & Juhola, M. (2009). Application of a modified two-point backward difference to sequential event detection in surface electromyography. Journal of Medical Engineering & Technology, 33(5), 349-360. doi:10.1080/03091900802323613Parker, P., Englehart, K., & Hudgins, B. (2006). Myoelectric signal processing for control of powered limb prostheses. Journal of Electromyography and Kinesiology, 16(6), 541-548. doi:10.1016/j.jelekin.2006.08.006Pierrot, F., Dombre, E., Dégoulange, E., Urbain, L., Caron, P., Boudet, S., … Mégnien, J.-L. (1999). Hippocrate: a safe robot arm for medical applications with force feedback. Medical Image Analysis, 3(3), 285-300. doi:10.1016/s1361-8415(99)80025-5Rees, S. E., Allerød, C., Murley, D., Zhao, Y., Smith, B. W., Kjærgaard, S., … Andreassen, S. (2006). Using physiological models and decision theory for selecting appropriate ventilator settings. Journal of Clinical Monitoring and Computing, 20(6), 421-429. doi:10.1007/s10877-006-9049-5Riba, J., Anderer, P., Jané, F., Saletu, B., & Barbanoj, M. J. (2004). Effects of the South American Psychoactive Beverage Ayahuasca on Regional Brain Electrical Activity in Humans: A Functional Neuroimaging Study Using Low-Resolution Electromagnetic Tomography. Neuropsychobiology, 50(1), 89-101. doi:10.1159/000077946Rocon, E., Ruíz, A. F., Belda-Lois, J. M., Moreno, J. C., Pons, J. L., Raya, R., & Ceres, R. (2008). Diseño, Desarrollo y Validación de Dispositivo Robótico para la Supresión del Temblor Patológico. Revista Iberoamericana de Automática e Informática Industrial RIAI, 5(2), 79-92. doi:10.1016/s1697-7912(08)70147-xRomero, S., Mañanas, M. A., & Barbanoj, M. J. (2009). Influence of ocular filtering in EEG data on the assessment of drug-induced effects on the brain. Human Brain Mapping, 30(5), 1470-1480. doi:10.1002/hbm.20614Rosen, J., Brand, M., Fuchs, M. B., & Arcan, M. (2001). A myosignal-based powered exoskeleton system. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 31(3), 210-222. doi:10.1109/3468.925661Ruiz, A. F., Rocon, E., & Forner-Cordero, A. (2009). Exoskeleton-based robotic platform applied in biomechanical modelling of the human upper limb. Applied Bionics and Biomechanics, 6(2), 205-216. doi:10.1080/11762320802697380Saletu, B., Anderer, P., & Saletu-Zyhlarz, G. M. (2006). EEG Topography and Tomography (LORETA) in the Classification and Evaluation of the Pharmacodynamics of Psychotropic Drugs. Clinical EEG and Neuroscience, 37(2), 66-80. doi:10.1177/155005940603700205Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., & Donoghue, J. P. (2002). Instant neural control of a movement signal. Nature, 416(6877), 141-142. doi:10.1038/416141aShin, H., Kim, K. H., Song, C., Lee, I., Lee, K., Kang, J., & Kang, Y. K. (2010). Electrodiagnosis support system for localizing neural injury in an upper limb. Journal of the American Medical Informatics Association, 17(3), 345-347. doi:10.1136/jamia.2009.001594Srinivasan, V., Eswaran, C., & Sriraam, N. (2007). Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks. IEEE Transactions on Information Technology in Biomedicine, 11(3), 288-295. doi:10.1109/titb.2006.884369Staudenmann, D., Kingma, I., Stegeman, D. F., & van Dieën, J. H. (2005). Towards optimal multi-channel EMG electrode configurations in muscle force estimation: a high density EMG study. Journal of Electromyography and Kinesiology, 15(1), 1-11. doi:10.1016/j.jelekin.2004.06.008Teixeira, C., Teixeira, P. J. Z., de Leon, P. P., & Oliveira, E. S. (2009). Work of breathing during successful spontaneous breathing trial. Journal of Critical Care, 24(4), 508-514. doi:10.1016/j.jcrc.2008.10.013Tobin, M. J. (2001). Advances in Mechanical Ventilation. New England Journal of Medicine, 344(26), 1986-1996. doi:10.1056/nejm200106283442606Tehrani, F. T., & Roum, J. H. (2008). Intelligent decision support systems for mechanical ventilation. Artificial Intelligence in Medicine, 44(3), 171-182. doi:10.1016/j.artmed.2008.07.006Velasco-Álvarez, F., Ron-Angevin, R., & Blanca-Mena, M. J. (2010). Free Virtual Navigation Using Motor Imagery Through an Asynchronous Brain–Computer Interface. Presence: Teleoperators and Virtual Environments, 19(1), 71-81. doi:10.1162/pres.19.1.71Wickelgren, I. (2003). NEUROSCIENCE: Tapping the Mind. Science, 299(5606), 496-499. doi:10.1126/science.299.5606.496Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791. doi:10.1016/s1388-2457(02)00057-3Zwarts, M. J., & Stegeman, D. F. (2003). Multichannel surface EMG: Basic aspects and clinical utility. Muscle & Nerve, 28(1), 1-17. doi:10.1002/mus.1035