Aprendizaje automático para la anotación de ritmos en parada cardiorrespiratoria

Resumen (castellano) Las paradas cardiorrespiratorias extrahospitalarias (PCREH) se posicionan como una de las principales causas de defunción en los países desarrollados. Ante dicho evento, existen ciertos factores determinantes para la supervivencia del sujeto, incluyendo la reanimación cardio pulmonar, una pronta desfibrilación y la calidad del tratamiento ofrecido por el Servicio de Emergencias Médicas. El corazón del paciente puede presentar hasta cinco tipos de ritmos distintos. Puesto que cada estado clínico precisa un tratamiento diferente, es de vital importancia para el personal médico, la pronta y correcta identificación del ritmo/estado del paciente. Por consiguiente, existen numerosos estudios dedicados al entendimiento de dichas patologías, los cuales emplean grabaciones de la señal electrocardiograma (ECG) durante episodios PCREH. Dichas grabaciones deben ser anotadas manualmente por un grupo de expertos clínicos. Por lo tanto, resulta una tarea dispendiosa, lo cual ocasiona escasez de bases de datos debidamente caracterizadas y anotadas. Con el objetivo de facilitar el acceso a colecciones de datos correctamente anotadas, existen algoritmos de anotación semiautomáticos. Estos algoritmos permiten identificar con elevada certeza, las patologías presentes en distintos intervalos temporales de la señal ECG. De esta forma, los expertos clínicos se focalizan en repasar las decisiones del algoritmo, ahorrando tiempo y coste. Por todo ello, los algoritmos de anotación facilitan los estudios de enfermedades cardiacas, mejorando la calidad del tratamiento realizado y, de esta forma, la probabilidad de supervivencia del paciente. En este trabajo se presentan cuatro clasificadores de ritmos de pacientes en PCREH. Para su desarrollo, primero se prepara una colección de episodios PCREH con los que entrenar los algoritmos. El primer clasificador extrae información únicamente de la señal ECG. El segundo añade la información presente en la impedancia transtorácica del paciente. Después, se desarrolla un tercer clasificador mediante técnicas de Deep Learning, puesto que recientemente ha demostrado su potencial en este campo. El cuarto clasificador lo conforma una versión optimizada del anterior modelo. Finalmente, se analizan los resultados y se compara el rendimiento de las distintas soluciones propuestas.Summary (English) Out-of-hospital cardiac arrest (OHCA) is one of the leading causes of death in developed countries. There are several key factors to survive an OHCA event, including cardiopulmonary resuscitation, early defibrillation and the overall quality of treatment given by the Emergency Medical System. The patient's heart can present up to five different types of rhythms. Since each clinical condition requires a different treatment, a fast and precise identification of the patient's rhythm/status is crucial for the medical staff. Therefore, there are numerous studies that focus on the understanding of these pathologies, using electrocardiogram signals (ECG) recorded during OHCA events. These recordings must be manually annotated by a group of clinical experts. Because the high costs associated to manual annotation, there is a lack of properly characterized and annotated databases. In order to facilitate access to correctly annotated data collections, there are semiautomatic annotation algorithms. These algorithms identify with high accuracy the pathologies present in different time intervals of the ECG signal. In this way, clinical experts would focus on reviewing the algorithm's decisions, saving time and money. All these considerations make annotation algorithms a key factor to develop studies on OHCA, improving the quality of the treatment performed and the probability of patient survival. In this work, four classifiers of OHCA rhythms are presented. For their development, first a collection of OHCA episodes is prepared, in order to train the algorithms. The first classifier extracts information only from the ECG signal. The second one, adds the information present in the patient's transthoracic impedance. Then, a third classifier is developed using Deep Learning techniques, since it has recently demonstrated its potential in this field. After that, a fourth classifier is made optimizing the previous model. Lastly, the results are analysed and the performance of the different proposed solutions is compared.Laburpena (Euskara) Hospitalez kanpoko bihotz geldiketa (HKBG) mundo garatuko heriotza kausa handienetariko bat dira. Geldiketa bat ematen denean zenbait gertakari gako dira pazientearen biziraupenerako, adibidez bihotz biriketako masajea, desfibrilazio goiztiarra edota emergentzia zerbitzuek emandako tratamendua. Pazientearen bihotzak bost erritmo desberdin aurkez ditzazke HKGB batean. Egoera kliniko bakoitzak tratamendu desberdina behar duenez, pazientearen erritmoa/egoera goiz eta zehatz detektatzea oso garrantzitsua da. Ondorioz, lan asko egin dira patologia horiek ulertzeko eta identifikatzeko, orokorrean pazientearen grabatutako elektrokardiograma (EKG) erabiliz. Grabaketa horietan aditu klinikoek erritmoa identifikatu eta anotatu behar dute. Azken hau kostu handiko lana da, eta ondorioz oso HKBG datubase gutxi dago erritmo anotazio egokiekin. Erritmo anotazioak dituzten HKGB datubaseak sortzeko badira erritmoa modu erdiautomatikoan anotatzeko algoritmoak. Algoritmo hauek modu nahiko zehatzean identifika dezaketa HKGB pazientearen erritmo/egoera, horretarako grabatutako EKG erabiliz. Horrela aditu klinikoek emandako diagnostikoa baino ez dute berrikusi behar, denbora eta kostuak aurreztuz. Horregatik anotaziorako algoritmoek HKGBaren inguruko ikerkuntza errazteu eta hobetuko lituzkete, emandako tratamendua hobetuz, eta pazienteen biziraupen aukerak handituz. Lan honetan lau algoritmo garatu dira HKGB erritmoak modu automatikoan sailkatzeko. Algoritmoak garatzeko lehendabizi HKGB kasuen datubase bat prestatu da, algoritmoak entrenatzeko. Lehen sailkatzailea EKG-tik soilik lortzen du informazioa. Bigarrenak bular inpedantziako informazioa ere erabiltzen du. Gero, ikasketa sakonean oinarritutako sailkatzailea garatu da, esparru honetan teknika hauek oso emaitza onak eman izan baitituzte. Azkenik laugarren sailkatzailea aurrekoaren bertsio hobetua da. Bukatzeko, emaitzak aztertu eta sailkatzaileen errendimenduak alderatu dira

Aprendizaje automático para la anotación de ritmos en parada cardiorrespiratoria

Resumen (castellano) Las paradas cardiorrespiratorias extrahospitalarias (PCREH) se posicionan como una de las principales causas de defunción en los países desarrollados. Ante dicho evento, existen ciertos factores determinantes para la supervivencia del sujeto, incluyendo la reanimación cardio pulmonar, una pronta desfibrilación y la calidad del tratamiento ofrecido por el Servicio de Emergencias Médicas. El corazón del paciente puede presentar hasta cinco tipos de ritmos distintos. Puesto que cada estado clínico precisa un tratamiento diferente, es de vital importancia para el personal médico, la pronta y correcta identificación del ritmo/estado del paciente. Por consiguiente, existen numerosos estudios dedicados al entendimiento de dichas patologías, los cuales emplean grabaciones de la señal electrocardiograma (ECG) durante episodios PCREH. Dichas grabaciones deben ser anotadas manualmente por un grupo de expertos clínicos. Por lo tanto, resulta una tarea dispendiosa, lo cual ocasiona escasez de bases de datos debidamente caracterizadas y anotadas. Con el objetivo de facilitar el acceso a colecciones de datos correctamente anotadas, existen algoritmos de anotación semiautomáticos. Estos algoritmos permiten identificar con elevada certeza, las patologías presentes en distintos intervalos temporales de la señal ECG. De esta forma, los expertos clínicos se focalizan en repasar las decisiones del algoritmo, ahorrando tiempo y coste. Por todo ello, los algoritmos de anotación facilitan los estudios de enfermedades cardiacas, mejorando la calidad del tratamiento realizado y, de esta forma, la probabilidad de supervivencia del paciente. En este trabajo se presentan cuatro clasificadores de ritmos de pacientes en PCREH. Para su desarrollo, primero se prepara una colección de episodios PCREH con los que entrenar los algoritmos. El primer clasificador extrae información únicamente de la señal ECG. El segundo añade la información presente en la impedancia transtorácica del paciente. Después, se desarrolla un tercer clasificador mediante técnicas de Deep Learning, puesto que recientemente ha demostrado su potencial en este campo. El cuarto clasificador lo conforma una versión optimizada del anterior modelo. Finalmente, se analizan los resultados y se compara el rendimiento de las distintas soluciones propuestas.Summary (English) Out-of-hospital cardiac arrest (OHCA) is one of the leading causes of death in developed countries. There are several key factors to survive an OHCA event, including cardiopulmonary resuscitation, early defibrillation and the overall quality of treatment given by the Emergency Medical System. The patient's heart can present up to five different types of rhythms. Since each clinical condition requires a different treatment, a fast and precise identification of the patient's rhythm/status is crucial for the medical staff. Therefore, there are numerous studies that focus on the understanding of these pathologies, using electrocardiogram signals (ECG) recorded during OHCA events. These recordings must be manually annotated by a group of clinical experts. Because the high costs associated to manual annotation, there is a lack of properly characterized and annotated databases. In order to facilitate access to correctly annotated data collections, there are semiautomatic annotation algorithms. These algorithms identify with high accuracy the pathologies present in different time intervals of the ECG signal. In this way, clinical experts would focus on reviewing the algorithm's decisions, saving time and money. All these considerations make annotation algorithms a key factor to develop studies on OHCA, improving the quality of the treatment performed and the probability of patient survival. In this work, four classifiers of OHCA rhythms are presented. For their development, first a collection of OHCA episodes is prepared, in order to train the algorithms. The first classifier extracts information only from the ECG signal. The second one, adds the information present in the patient's transthoracic impedance. Then, a third classifier is developed using Deep Learning techniques, since it has recently demonstrated its potential in this field. After that, a fourth classifier is made optimizing the previous model. Lastly, the results are analysed and the performance of the different proposed solutions is compared.Laburpena (Euskara) Hospitalez kanpoko bihotz geldiketa (HKBG) mundo garatuko heriotza kausa handienetariko bat dira. Geldiketa bat ematen denean zenbait gertakari gako dira pazientearen biziraupenerako, adibidez bihotz biriketako masajea, desfibrilazio goiztiarra edota emergentzia zerbitzuek emandako tratamendua. Pazientearen bihotzak bost erritmo desberdin aurkez ditzazke HKGB batean. Egoera kliniko bakoitzak tratamendu desberdina behar duenez, pazientearen erritmoa/egoera goiz eta zehatz detektatzea oso garrantzitsua da. Ondorioz, lan asko egin dira patologia horiek ulertzeko eta identifikatzeko, orokorrean pazientearen grabatutako elektrokardiograma (EKG) erabiliz. Grabaketa horietan aditu klinikoek erritmoa identifikatu eta anotatu behar dute. Azken hau kostu handiko lana da, eta ondorioz oso HKBG datubase gutxi dago erritmo anotazio egokiekin. Erritmo anotazioak dituzten HKGB datubaseak sortzeko badira erritmoa modu erdiautomatikoan anotatzeko algoritmoak. Algoritmo hauek modu nahiko zehatzean identifika dezaketa HKGB pazientearen erritmo/egoera, horretarako grabatutako EKG erabiliz. Horrela aditu klinikoek emandako diagnostikoa baino ez dute berrikusi behar, denbora eta kostuak aurreztuz. Horregatik anotaziorako algoritmoek HKGBaren inguruko ikerkuntza errazteu eta hobetuko lituzkete, emandako tratamendua hobetuz, eta pazienteen biziraupen aukerak handituz. Lan honetan lau algoritmo garatu dira HKGB erritmoak modu automatikoan sailkatzeko. Algoritmoak garatzeko lehendabizi HKGB kasuen datubase bat prestatu da, algoritmoak entrenatzeko. Lehen sailkatzailea EKG-tik soilik lortzen du informazioa. Bigarrenak bular inpedantziako informazioa ere erabiltzen du. Gero, ikasketa sakonean oinarritutako sailkatzailea garatu da, esparru honetan teknika hauek oso emaitza onak eman izan baitituzte. Azkenik laugarren sailkatzailea aurrekoaren bertsio hobetua da. Bukatzeko, emaitzak aztertu eta sailkatzaileen errendimenduak alderatu dira

Computational Modeling of Spinal Cord Stimulation for Inspiratory Muscle Activation and Chronic Pain Management

Spinal cord stimulation (SCS) is a neuromodulation technique that applies electrical stimulation to the spinal cord to alter neural activity or processing. While SCS has historically been used as a last-resort therapy for chronic pain management, novel applications and technologies have recently been developed that either increase the efficacy of treatment for chronic pain or drive neural activity to produce muscular activity/movement following a paralyzing spinal cord injury (SCI). Despite these recent innovations, there remain fundamental questions concerning the neural recruitment underlying these efficacious results. This work evaluated the neural activity and mechanisms for three SCS applications: both conventional SCS and closed-loop SCS for pain management, as well as ventral, high frequency spinal cord stimulation (HF-SCS) for inspiratory muscle activation following a SCI. I developed computational models to both predict the neural response to SCS and explore factors influencing neural activation. Models consisted of three components: a finite element model (FEM) of the spinal cord to predict the potential fields generated by stimulation, biophysical neuron models, and algorithms to apply time-dependent extracellular potentials to the neuron models and simulate their response. While this cutting-edge modeling methodology could be used to predict neural activity following stimulation, it was unclear how anatomical and technical factors affected neural predictions. To evaluate these factors, I designed an FEM of a T9 thoracic spine with an implanted electrode array. Then, I sequentially removed details from the model and quantified the changes in neural predictions. I identified several factors with large (>30%) effects on neural thresholds, including overall electrode impedance (for voltage-controlled stimulation), the electrode position relative to the spine, and dura mater conductivity. This work will be invaluable for basic science and clinical applications of SCS. Next, I developed a canine model to evaluate T2 ventral HF-SCS for inspiratory muscle activation after an SCI. This model infrastructure included two neuron populations hypothesized to lead to inspiratory behavior: ventrolateral funiculus fibers (VLF) leading to diaphragm activation and inspiratory intercostal motoneurons. With this model, I predicted robust VLF and T2-T5 motoneuron recruitment within the experimental range of stimulation. I used this model framework to optimize several design parameters related to HF-SCS for inspiration. The optimal lead design parameters were evaluated via in vivo experiments, which found excellent agreement with model predictions. This work expands our mechanistic understanding of this novel therapy, improves its implementation, and aids in future translational efforts towards human subjects. Finally, I developed a computational model to evaluate closed-loop SCS for chronic pain management. This work characterized the neural origins of the evoked compound action potential (ECAP), the controlling biomarker of closed-loop stimulation. This modeling work showed that ECAP properties depend on activation of a narrow range of axon diameters and quantified how anatomical and stimulation factors (e.g., CSF thickness, stimulation configuration, lead position, pulse width) influence ECAP morphology, timing, and neural recruitment. These results improve our mechanistic understanding of closed-loop stimulation and neural recruitment during SCS. In summary, this dissertation work improves the methodology, validation, and applications of computational models of SCS. It also has direct applications to the clinical/pre-clinical implementation of SCS and may be invaluable for expanding the utility and efficacy of several treatments. The improved mechanistic understandings of neural activation described in this work may also aid in the future development of these therapies.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169928/1/hzander_1.pd

Development of quantitative scanning electrochemical microscopy (SECM) imaging of single and array micro and nanoband edge platinum electrodes

Imaging by scanning electrochemical microscopy (SECM) is an established technique that mixes electrochemistry with microscopy. To name a few examples of its broad applicability, it is used for imaging nanoparticles, enzymes, monitoring corrosion processes and redox processes in living cells. However, the information gathered from such images is mostly qualitative, rather than quantitative. Gold band arrays have been used as a standard substrate of nominally defined dimensions for conventional SECM imaging, but they are known to be non-uniform across the electrode surface and suffer ageing, producing significantly timevariant currents among other uncertainties. This thesis addresses this by imaging robust single and array micro and nanoband edge electrodes of controlled design, shape, size dimension and spacing and assists the quantitative analysis of the response. Both qualitative and quantitative analysis was performed on these SECM images. First, data from experimental probe approach curve (PAC) experiments obtained before imaging were compared to the established analytical model reported by Lefrou et al., confirming its applicability. This was then used to fit COMSOL simulated data to extract real tip working distances in μm above the insulator surface specific for these experiments. Then, qualitative analysis of SECM images of micro array, single micro and nanoband edge platinum (Pt) electrodes allowed the evaluation of the impact of effects such as convection, sample orientation and changes in the response of the SECM tip with time in feedback (FB) and substrate generator-tip collector (SG-TC) mode. From single electrode analysis, differences between the imaging modes regarding image resolution and imaging artefacts, also disc and square geometry impact, and differences between imaging micro and nanoband edge electrodes are discussed. Two types of arrays, hexagonal and square, were used as SECM substrates, also allowing to evaluate hemispherical diffusion field overlap evolution from differently arranged arrays. This allowed to visually evaluate the quality of the in-house fabricated electrodes which has not been reported before, together with visual evaluation and direct evidence that hemispherical diffusion field evolves from both disc and square geometry micro and nanoband edge electrodes. Extraction of line profiles from various parts of the images was then used to further compare SG-TC and FB modes. This lead to the quantitative analysis of the tip response of selected scan lines (both vertical and horizontal) in these 2D images collected at fixed tip working distances. The responses of square nanoband edge electrodes were shown to fit to Gaussian distributions and to be consistent with a combination of diffusional broadening and convolution of the sample and tip response. Further, the tip currents were shown to follow the expected concentration profiles of diffusion from the ring nanoband generated analytically using modified Bessel function. Finally, imaging settings and substrate and tip size were varied to evaluate their effect on image spatial resolution, on artefact occurrence and the effectiveness of the above quantitation. Images of a smaller disc nanoband edge electrode of 50 μm diameter instead of the previously used 100 μm were collected using SECM FB and SG-TC modes and were comparable to the 100 μm diameter electrodes. A Pt tip of 1 μm diameter, which is 10 times smaller than the original Pt tip, was used to probe the effect of the tip size. Finally, the effectiveness of quantitative approaches using Gaussian and modified Bessel functions on disc nanoband substrate of 50 μm diameter was evaluated and compared to 100 μm fitting results. Together this data analysis has enabled the evaluation of such electrodes as a benchmark system for SECM probe response validation, method development, optimisation and quantitation