Intelligent Computing in Medical Ultrasonic System

兵庫県立大学大学院201

Contributions to the Development of Objective Techniques for Presence Measurement in Virtual Environments by means of Brain Activity Analysis

En esta tesis, se propone el uso de la técnica de Doppler transcraneal (DTC) para monitorizar la actividad cerebral durante la exposición a entornos virtuales (EV) y así poder analizar los correlatos cerebrales del sentido de presencia. Las hipótesis de partida son las siguientes: 1) DTC se podrá utilizar fácilmente en combinación con sistemas de realidad virtual. 2) Los datos de velocidad de flujo sanguíneo medidos por DTC se podrán utilizar para analizar cambios de actividad cerebral durante la exposición a EV. 3) Habrá diferencias en la velocidad del flujo sanguíneo asociadas a distintos niveles de presencia. 4) Habrá correlación entre el grado de presencia medido por cuestionarios y parámetros de la velocidad de flujo sanguíneo. 5) Cada componente de la experiencia virtual tendrá una influencia en las variaciones de velocidad observadas. Para analizar las hipótesis planteadas, se realizaron cuatro experimentos distintos, en los que se analizó la velocidad del flujo sanguíneo durante: 1) distintas condiciones de navegación, 2) distintas condiciones de inmersión, 3) una tarea de percepción visual y 4) tareas motoras para manejo de un joystick. Durante la tesis, se han propuesto distintas técnicas de procesado de señal basadas en análisis espectral y en la obtención parámetros no lineales de la señal, que no habían sido utilizadas previamente en experimentos psicofisiológicos con DTC. Se ha observado que existe un incremento en la velocidad del flujo sanguíneo durante la exposición a un EV, el cual puede deberse a distintos factores que intervienen en la experiencia: tareas de interacción visuoespacial, tareas de atención, la creación y ejecución de un plan motor, cambios emocionales Los análisis han mostrado que existen correlaciones significativas entre la velocidad media de flujo sanguíneo en las arterias cerebrales medias durante la exposición al EV y respuestas a los cuestionarios de presencia utilizados.Rey Solaz, B. (2010). Contributions to the Development of Objective Techniques for Presence Measurement in Virtual Environments by means of Brain Activity Analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8505Palanci

From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation

A network-based approach is presented to investigate the cerebrovascular flow patterns during atrial fibrillation (AF) with respect to normal sinus rhythm (NSR). AF, the most common cardiac arrhythmia with faster and irregular beating, has been recently and independently associated with the increased risk of dementia. However, the underlying hemodynamic mechanisms relating the two pathologies remain mainly undetermined so far; thus the contribution of modeling and refined statistical tools is valuable. Pressure and flow rate temporal series in NSR and AF are here evaluated along representative cerebral sites (from carotid arteries to capillary brain circulation), exploiting reliable artificially built signals recently obtained from an in silico approach. The complex network analysis evidences, in a synthetic and original way, a dramatic signal variation towards the distal/capillary cerebral regions during AF, which has no counterpart in NSR conditions. At the large artery level, networks obtained from both AF and NSR hemodynamic signals exhibit elongated and chained features, which are typical of pseudo-periodic series. These aspects are almost completely lost towards the microcirculation during AF, where the networks are topologically more circular and present random-like characteristics. As a consequence, all the physiological phenomena at microcerebral level ruled by periodicity - such as regular perfusion, mean pressure per beat, and average nutrient supply at cellular level - can be strongly compromised, since the AF hemodynamic signals assume irregular behaviour and random-like features. Through a powerful approach which is complementary to the classical statistical tools, the present findings further strengthen the potential link between AF hemodynamic and cognitive decline.Comment: 12 pages, 10 figure

비침습적 뇌파 신호를 이용한 응급환자의 생체반응 모니터링 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2021. 2. 김희찬.뇌파는 대뇌피질이나 두피의 전극을 통해서 뇌의 전기적 신호를 기록한 것을 의미한다. 뇌 기능 관찰을 위한 진단도구로써 뇌파는 뇌전증이나 치매 진단 등의 목적으로 활용되고 있다. 본 논문에서는 비침습적 뇌파를 이용하여 응급환자의 주요 생리학적 지표를 모니터링하는 기술을 개발하였다. 처음 두 연구에서 심폐소생술의 효과를 평가하기 위한 심정지 돼지실험모델을 고안하였다. 현재의 심폐소생술 지침은 체순환 평가를 위해 기도삽관을 통한 호기말 이산화탄소 분압의 측정을 권고한다. 하지만, 정확한 기도삽관이 특히 병원 밖 상황에서 어려울 수 있다. 따라서, 간편히 측정할 수 있고 소생 환자의 신경학적 예후를 진단하는데 사용되는 뇌파를 이용한 예측 기술이 심폐소생술 품질평가지표의 대안으로 제안되었다. 첫 번째 실험에서는 고품질과 저품질 기본심폐소생술을 10회 반복하면서 측정된 뇌파를 분석하였다. 심폐소생술의 품질에 따른 뇌파의 변화를 이용하여 체순환 평가를 위한 EEG-based Brain Resuscitation Index (EBRI) 모델을 도출하였다. EBRI 모델에서 획득한 호기말 이산화탄소 분압 예측치는 실제 값과 양의 상관관계를 보이며, 병원 밖 상황에서의 활용 가능성을 보였다. 두 번째 실험에서는 두 가지 심폐소생술(기본심폐소생술, 전문심폐소생술)이 수행되었다. 제세동 직전에 수집된 뇌파는 심폐소생술 도중 경동맥혈류의 회복률과 함께 분석되었다. 심폐소생술 도중 경동맥혈류의 회복률을 반영하는 뇌파 변수를 규명한 후, 이를 이용하여 높은 회복률(30% 이상)과 낮은 회복률(30% 미만)을 구분하는 기계학습 기반 이진분류모델을 도출하였다. 서포트 벡터 머신 기반의 예측모델이 0.853의 정확도와 0.909의 곡선하면적을 보이며 가장 우수한 성능을 보였다. 이러한 예측모델은 심정지 환자의 뇌 소생을 향상시켜 빠른 뇌 기능 회복을 가능하게 할 것으로 기대된다. 세 번째 연구에서 비침습적 뇌파를 이용하여 두개내압을 예측하는 모델을 개발하기 위한 외상성 뇌손상 돼지실험모델이 고안되었다. 외상성 뇌손상은 물리적 충격에 의해 정상적인 뇌 기능이 중단된 상태를 의미하며, 이 때의 두개내압 상승과 관류저하가 뇌파에 영향을 끼칠 수 있다. 따라서, 우리는 뇌파 기반 두개내압 예측모델을 개발하였다. 폴리카테터로 실험동물의 두개내압을 변경하면서 뇌파를 획득하였다. 두개내압의 정상구간(25 mmHg 미만)과 위험구간(25 mmHg 이상)을 유의미하게 구분하는 뇌파 변수를 규명한 후 기계학습 기반 이진분류모델을 도출하였다. 다층 퍼셉트론 기반의 예측모델이 0.686의 정확도와 0.754의 곡선하면적을 보이며 가장 우수한 성능을 보였다. 또다른 비침습 데이터인 심박수 정보와 함께 사용하였을 때 정확도와 곡선하면적은 각각 0.760과 0.834로 향상되었다. 제안된 예측모델은 응급상황에서 비침습적으로 두개내압을 관찰하여 정상 수준의 두개내압을 유지하는데 도움을 줄 것으로 기대된다. 본 논문은 응급환자의 주요 생리학적 지표를 비침습적 뇌파를 이용하여 관찰하는 예측모델을 제안하고 성능을 검증하였다. 본 연구에서는 뇌파를 이용하여 즉각적인 호기말 이산화탄소 분압, 경동맥혈류, 두개내압을 추정하기 위한 예측모델을 수립하였다. 하지만, 뇌파 데이터는 장기간의 신경학적, 기능적 회복과 함께 평가되어야 한다. 본 논문에서 개발한 예측모델의 성능과 적용 가능성은 향후 다양한 임상연구를 통해 cerebral performance category와 modified Rankin scale 등의 신경학적 평가지표와 함께 분석, 개선되어야 할 것이다.Electroencephalogram (EEG) is a recording of the electrical activity of the brain, measured using electrodes attached to the cerebrum cortex or the scalp. As a diagnostic tool for brain disorders, EEG has been widely used for clinical purposes such as epilepsy- and dementia diagnosis. This study develops an EEG-based noninvasive critical care monitoring method for emergency patients. In the first two studies, ventricular fibrillation swine models were designed to develop EEG-based monitoring methods for evaluating the effectiveness of cardiopulmonary resuscitation (CPR). The CPR guidelines recommend measuring end-tidal carbon dioxide (ETCO2) via endotracheal intubation to assess systemic circulation. However, accurate insertion of the endotracheal tube might be difficult in an out-of-hospital setting (OOHS). Therefore, an easily measurable EEG, which has been used to predict resuscitated patients neurologic prognosis, was suggested as a surrogate indicator for CPR feedback. In the first experimental setup, the high- and low quality CPRs were altered 10 times repeatedly, and the EEG parameters were analyzed. Linear regression of an EEG-based brain resuscitation index (EBRI) was used to estimate ETCO2 levels as a novel feedback indicator of systemic circulation during CPR. A positive correlation was found between the EBRI and the real ETCO2, which indicates the feasibility of EBRI in OOHSs. In the second experimental setup, two types of CPR mode were performed: basic life support and advanced cardiovascular life support. EEG signals that were measured between chest compressions and defibrillation shocks were analyzed to monitor the cerebral circulation with respect to the recovery of carotid blood flow (CaBF) during CPR. Significant EEG parameters were identified to represent the CaBF recovery, and machine learning (ML)-based classification models were established to differentiate between the higher (≥ 30%) and lower (< 30%) CaBF recovery. The prediction model based on the support vector machine (SVM) showed the best performance, with an accuracy of 0.853 and an area under the curve (AUC) of 0.909. The proposed models are expected to guide better cerebral resuscitation and enable early recovery of brain function. In the third study, a swine model of traumatic brain injury (TBI) was designed to develop an EEG-based prediction model of an elevated intracranial pressure (ICP). TBI is defined as the disruption of normal brain function due to physical impact. This can increase ICP, and the resulting hypoperfusion can affect the cerebral electrical activity. Thus, we developed EEG-based prediction models to monitor ICP levels. During the experiments, EEG was measured while the ICP was adjusted with the Foley balloon catheter. Significant EEG parameters were determined to differentiate between the normal (< 25 mmHg) and dangerous (≥ 25 mmHg) ICP levels and ML-based binary classifiers were established to distinguish between these two groups. The multilayer perceptron model showed the best performance with an accuracy of 0.686 and an AUC of 0.754, which were improved to 0.760 and 0.834, respectively, when a noninvasive heart rate was also used as an input. The proposed prediction models are expected to instantly treat an elevated ICP (≥ 25 mmHg) in emergency settings. This study presents a new EEG-based noninvasive monitoring method of the physiologic parameters of emergency patients, especially in an OOHS, and evaluates the performance of the proposed models. In this study, EEG was analyzed to predict immediate ETCO2, CaBF, and ICP. The prediction models demonstrate that a noninvasive EEG can yield clinically important predictive outcomes. Eventually, the EEG parameters should be investigated with regard to the long-term neurological and functional outcomes. Further clinical trials are warranted to improve and evaluate the feasibility of the proposed method with respect to the neurological evaluation scores, such as the cerebral performance category and modified Rankin scale.Abstract i Contents iv List of Tables viii List of Figures x List of Abbreviations xii Chapter 1 General Introduction 1 1.1 Electroencephalogram 1 1.2 Clinical use of spontaneous EEG 5 1.3 EEG and cerebral hemodynamics 7 1.4 EEG use in emergency settings 9 1.5 Noninvasive CPR assessment 10 1.6 Noninvasive traumatic brain injury assessment 16 1.7 Thesis objectives 21 Chapter 2 EEG-based Brain Resuscitation Index for Monitoring Systemic Circulation During CPR 23 2.1 Introduction 23 2.2 Methods 25 2.2.1 Ethical statement 25 2.2.2 Study design and setting 25 2.2.3 Experimental animals and housing 27 2.2.4 Surgical preparation and hemodynamic measurements 27 2.2.5 EEG measurement 29 2.2.6 Data analysis 32 2.2.7 EBRI calculation 33 2.2.8 Delta-EBRI calculation 34 2.3 Results 36 2.3.1 Hemodynamic parameters 36 2.3.2 Changes in EEG parameters 37 2.3.3 EBRI calculation 39 2.3.4 Delta-EBRI calculation 41 2.4 Discussion 42 2.4.1 Accomplishment 42 2.4.2 Limitations 45 2.5 Conclusion 46 Chapter 3 EEG-based Prediction Model of the Recovery of Carotid Blood Flow for Monitoring Cerebral Circulation During CPR 47 3.1 Introduction 47 3.2 Methods 50 3.2.1 Ethical statement 50 3.2.2 Study design and setting 50 3.2.3 Experimental animals and housing 52 3.2.4 Surgical preparation and hemodynamic measurements 54 3.2.5 EEG measurement 55 3.2.6 Data processing 57 3.2.7 Data analysis 58 3.2.8 Development of machine-learning based prediction model 59 3.3 Results 63 3.3.1 Results of CPR process 63 3.3.2 EEG changes with the recovery of CaBF 66 3.3.3 Changes in EEG parameters depending on four CaBF groups 68 3.3.4 Changes in EEG parameters depending on two CaBF groups 69 3.3.5 EEG parameters for prediction models 70 3.3.6 Performances of prediction models 73 3.4 Discussion 76 3.4.1 Accomplishment 76 3.4.2 Limitations 78 3.5 Conclusion 80 Chapter 4 EEG-based Prediction Model of an Increased Intra-Cranial Pressure for TBI patients 81 4.1 Introduction 81 4.2 Methods 83 4.2.1 Ethical statement 83 4.2.2 Study design and setting 83 4.2.3 Experimental animals and housing 85 4.2.4 Surgical preparation and hemodynamic measurements 86 4.2.5 EEG measurement 88 4.2.6 Data processing 90 4.2.7 Data analysis 90 4.2.8 Development of machine-learning based prediction model 91 4.3 Results 92 4.3.1 Hemodynamic changes during brain injury phase 92 4.3.2 EEG changes with an increase of ICP 93 4.3.3 EEG parameters for prediction models 94 4.3.4 Performances for prediction models 95 4.4 Discussion 100 4.4.1 Accomplishment 100 4.4.2 Limitations 104 4.5 Conclusion 104 Chapter 5 Summary and Future works 105 5.1 Thesis summary and contributions 105 5.2 Future direction 108 Bibilography 113 Abstract in Korean 135Docto

The Dynamic Relationship between Photoplethysmography Features and Intracranial Pressure in Patients with Traumatic Brain Injury

Traumatic brain injury (TBI) is as an alteration in brain function pathology by a sudden trauma, causing damage to the brain. Symptoms vary from mild to severe, depending on the extent of the damage to the brain. Intracranial pressure (ICP) monitoring is a “gold standard” for severe TBI patients, measuring pressure inside the skull. An ICP crisis is a sustained ICP value above a threshold of 20-25 mmHg. Effective ICP monitoring and intervention at defined thresholds can reduce mortality and secondary brain injury. The gold standard for ICP monitoring involves invasive neurosurgical intervention to implant a pressure sensor into the brain, which is expensive, carries risk, requires expertise and is only accessible within a hospital setting. Consequently, timely and effective monitoring is restricted, exacerbating adverse outcomes, and excluding millions of mild and moderate TBI cases from continuous and quantitative assessment. There is a nascent body of research investigating non-invasive ICP monitoring aiming to reduce the barrier to entry to efficacious monitoring and intervention for patients and healthcare systems. A growing body of research is exploring the use of Photoplethysmograph (PPG) for the non-invasive estimation of ICP. This thesis makes novel contributions to knowledge by evaluating the relationship between PPG derived features and variations in ICP using the largest clinically collected, labelled PPG dataset, to-date. The novel PPG data, produced by an in-house, Near-infrared spectroscopy, reflectance, non-invasive optical ICP sensor, was collected from 40 TBI patients at The Royal London Hospital. Data is categorised based on “proximal” and “distal” photodiodes, hypothesised to correspond to extracerebral and cerebral data, respectively. The research makes novel contributions to the field via the testing of three main hypotheses: (i) PPG feature alterations correlate with ICP changes, (ii) distal PPG data shows stronger correlations with ICP than proximal data, and (iii) PPG features can estimate ICP non-invasively. A total of 141 features were extracted for each one-minute window of PPG data, including the original waveform and its first and second derivatives. Spearman’s correlation and the Kruskal-Wallis test evaluated the first two hypotheses. Results indicated significant correlations between PPG features and ICP levels, with 77.30% and 79.43% of features significantly correlated (p < 0.05) for distal and proximal datasets, respectively. Group analysis revealed significant changes across ICP groups (0-10, 10-20, 20-39 mmHg) in 81.56% and 75.89% of features. The mean absolute correlation of all features and significantly correlated features was 25.76% and 24.24% higher for distal than for proximal features, supporting the potential of PPG-based ICP monitoring. To test the third hypothesis, five classical machine learning models were implemented, optimised, and assessed across six key metrics using a leave-one-patient-out cross-validation approach. Distal models outperformed proximal ones, with the best model, a Random Forest, achieving a mean RMSE of 5.030 mmHg, MAE of 4.067 mmHg, and Bland-Altman limits of agreement around 8.5 mmHg, and a low correlation coefficient of -0.007. There is a need for the development of a continuous noninvasive, easy-to-use, inexpensive monitoring device. Such a device would reduce the barrier to entry for ICP monitoring across all severities, providing timely diagnosis whilst serving the currently undeserved majority of TBI cases. This work provides a credible foundation for further research in this domain

Modelling the time-series of cerebrovascular pressure transmission variation in head injured patients

Cerebral autoregulation is the process by which blood ow is maintained over a changing cerebral perfusion pressure. Clinically autoregulation is an important topic because it directly effects overall patient management strategy. However accurately predicting autoregulatory state or even modelling the underlying general physiological processes is a complex task. There are a number of models published within the literature but there has been no active attempt to compare and classify these models. Starting with the hypothesis that a physiologically based model would be a better predictor of autoregulatory state than a purely statistically based one has led us to investigate approaches to model comparison. Using three different models: a new mathematical arrangement of a physiological model by Ursino, the Highest Model Frequency (HMF) model by Daley and the Pressure reactivity index (PRx) statistical model by Czosnyka, a general comparison was carried out using the Matthews correlation coecient against a known autoregulatory state. This showed that the Ursino model was approximately three times as predictive as both the HMF model and the PRx model. However, in general, all of the models predictive accuracies were relatively poor so a number of optimisation strategies were then assessed. These optimisation strategies ultimately were formed into a generalised modelling framework. This framework draws on the ideas of mathematical topology to underpin and explain any change or optimisation to a model. Within the framework different optimisations can be grouped into four categories, each of which are explored in the text of this thesis: 1) Model Comparison. This is the simplest technique to apply where the number of models under examination are reduced based on the predictive accuracy. 2) Parameter restriction. A classical form of optimisation by constraining a model parameter to cause a better predictive accuracy. In the case of both the HMF and PRx we showed between a two hundred and six hundred percent increase in predictive accuracy over the initial assessment. 3) Parameter alteration. This change allows for related parameters to be substituted into a model. Four different alterations are explored as a surrogate measure for arterial-arteriolar blood volume the most clinically applicable of which is a transcranial impedance technique. This latter technique has the potential to be a non invasive measure correlated with both mean ICP and ICP pulse amplitude. 4) Model alteration. Allows for larger changes to the underlying structure of the model. Two examples are presented: firstly a new asymmetric sigmoid curve to overcome computational issues in the Ursino model and secondly a novel use of fractal characterisation which is applied in a wavelet noise reduction technique. The framework also gives an overview of the autoregulatory research domain as a whole as a result of its abstract nature. This helps to highlight some general issues in the domain including a more standardised way to record autoregulatory status. Finally concluding with research addressing the requirement for easier access to data and the need for the research community to cohesively start to address these issues

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 385)

This bibliography lists 536 reports, articles and other documents introduced into the NASA Scientific and Technical Information System Database. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance