Neonatal seizure detection based on single-channel EEG: instrumentation and algorithms

Seizure activity in the perinatal period, which constitutes the most common neurological emergency in the neonate, can cause brain disorders later in life or even death depending on their severity. This issue remains unsolved to date, despite the several attempts in tackling it using numerous methods. Therefore, a method is still needed that can enable neonatal cerebral activity monitoring to identify those at risk. Currently, electroencephalography (EEG) and amplitude-integrated EEG (aEEG) have been exploited for the identification of seizures in neonates, however both lack automation. EEG and aEEG are mainly visually analysed, requiring a specific skill set and as a result the presence of an expert on a 24/7 basis, which is not feasible. Additionally, EEG devices employed in neonatal intensive care units (NICU) are mainly designed around adults, meaning that their design specifications are not neonate specific, including their size due to multi-channel requirement in adults - adults minimum requirement is ≥ 32 channels, while gold standard in neonatal is equal to 10; they are bulky and occupy significant space in NICU. This thesis addresses the challenge of reliably, efficiently and effectively detecting seizures in the neonatal brain in a fully automated manner. Two novel instruments and two novel neonatal seizure detection algorithms (SDAs) are presented. The first instrument, named PANACEA, is a high-performance, wireless, wearable and portable multi-instrument, able to record neonatal EEG, as well as a plethora of (bio)signals. This device despite its high-performance characteristics and ability to record EEG, is mostly suggested to be used for the concurrent monitoring of other vital biosignals, such as electrocardiogram (ECG) and respiration, which provide vital information about a neonate's medical condition. The two aforementioned biosignals constitute two of the most important artefacts in the EEG and their concurrent acquisition benefit the SDA by providing information to an artefact removal algorithm. The second instrument, called neoEEG Board, is an ultra-low noise, wireless, portable and high precision neonatal EEG recording instrument. It is able to detect and record minute signals (< 10 nVp) enabling cerebral activity monitoring even from lower layers in the cortex. The neoEEG Board accommodates 8 inputs each one equipped with a patent-pending tunable filter topology, which allows passband formation based on the application. Both the PANACEA and the neoEEG Board are able to host low- to middle-complexity SDAs and they can operate continuously for at least 8 hours on 3-AA batteries. Along with PANACEA and the neoEEG Board, two novel neonatal SDAs have been developed. The first one, termed G prime-smoothed (G ́_s), is an on-line, automated, patient-specific, single-feature and single-channel EEG based SDA. The G ́_s SDA, is enabled by the invention of a novel feature, termed G prime (G ́) and can be characterised as an energy operator. The trace that the G ́_s creates, can also be used as a visualisation tool because of its distinct change at a presence of a seizure. Finally, the second SDA is machine learning (ML)-based and uses numerous features and a support vector machine (SVM) classifier. It can be characterised as automated, on-line and patient-independent, and similarly to G ́_s it makes use of a single-channel EEG. The proposed neonatal SDA introduces the use of the Hilbert-Huang transforms (HHT) in the field of neonatal seizure detection. The HHT analyses the non-linear and non-stationary EEG signal providing information for the signal as it evolves. Through the use of HHT novel features, such as the per intrinsic mode function (IMF) (0-3 Hz) sub-band power, were also employed. Detection rates of this novel neonatal SDA is comparable to multi-channel SDAs.Open Acces

AUTOMATED ARTIFACT REMOVAL AND DETECTION OF MILD COGNITIVE IMPAIRMENT FROM SINGLE CHANNEL ELECTROENCEPHALOGRAPHY SIGNALS FOR REAL-TIME IMPLEMENTATIONS ON WEARABLES

Electroencephalogram (EEG) is a technique for recording asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. EEG signal is well studied to evaluate the cognitive state, detect brain diseases such as epilepsy, dementia, coma, autism spectral disorder (ASD), etc. In this dissertation, the EEG signal is studied for the early detection of the Mild Cognitive Impairment (MCI). MCI is the preliminary stage of Dementia that may ultimately lead to Alzheimers disease (AD) in the elderly people. Our goal is to develop a minimalistic MCI detection system that could be integrated to the wearable sensors. This contribution has three major aspects: 1) cleaning the EEG signal, 2) detecting MCI, and 3) predicting the severity of the MCI using the data obtained from a single-channel EEG electrode. Artifacts such as eye blink activities can corrupt the EEG signals. We investigate unsupervised and effective removal of ocular artifact (OA) from single-channel streaming raw EEG data. Wavelet transform (WT) decomposition technique was systematically evaluated for effectiveness of OA removal for a single-channel EEG system. Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), is studied with four WT basis functions: haar, coif3, sym3, and bior4.4. The performance of the artifact removal algorithm was evaluated by the correlation coefficients (CC), mutual information (MI), signal to artifact ratio (SAR), normalized mean square error (NMSE), and time-frequency analysis. It is demonstrated that WT can be an effective tool for unsupervised OA removal from single channel EEG data for real-time applications.For the MCI detection from the clean EEG data, we collected the scalp EEG data, while the subjects were stimulated with five auditory speech signals. We extracted 590 features from the Event-Related Potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with Radial Basis Kernel (RBF) (sigma = 10, cost = 102). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI. We also developed a single channel Electro-encephalography (EEG) based MCI severity monitoring algorithm by generating the Montreal Cognitive Assessment (MoCA) scores from the features extracted from EEG. We performed multi-trial and single-trail analysis for the algorithm development of the MCI severity monitoring. We studied Multivariate Regression (MR), Ensemble Regression (ER), Support Vector Regression (SVR), and Ridge Regression (RR) for multi-trial and deep neural regression for the single-trial analysis. In the case of multi-trial, the best result was obtained from the ER. In our single-trial analysis, we constructed the time-frequency image from each trial and feed it to the convolutional deep neural network (CNN). Performance of the regression models was evaluated by the RMSE and the residual analysis. We obtained the best accuracy with the deep neural regression method

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

Brain activity on encoding different textures EEG signal acquisition with ExoAtlet®

Powered exoskeletons play a crucial role in the rehabilitation field improving the quality of life for those who need them. Thus, being a major contribution for patients integration into society, providing them with more autonomy and freedom. In spite of these positive outcomes, a thorough description of the brain correlates connected to exoskeleton control is still needed. For instance, the perception of different pavement textures when wearing an exoskeleton is probably going to cause changes in cerebral activity, which could impact both sensory encoding and Brain-Computer Interface (BCI) control. Therefore, the main goal of this work is to describe the brain activity response to different textured pavements using ExoAtlet ® powered exoskeleton. In order to measure, process, analyze and classify the impact of different textures on neurophysiological rhythms, 4-minute signals were recorded by Electroencephalogram (EEG) with a 16-channel cap (actiCAP by Brain Products). Each of the three experimental subjects was instructed to walk in place on four different types of pavement (flat, carpet, foam, and rubber circles) with and without the exoskeleton, for a total of eight different experimental conditions. A counterbalanced design was applied, and informed consent was obtained from participants (Committee for Health Sciences of the Universidade Católica Portuguesa - 99/2022). Additionally, four machine learning methods, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN), were selected in order to analyze three distinct classification problems. This study found that there were changes associated with the delta frequency band for electrodes C3 and C4, and when comparing the classifiers performance, LDA presented the best accuracy across the three classification problems involving all subjects. Thereby, this work concludes that the results are consistent with the hypothesis that sensory processing of pavement textures during exoskeleton control induces neural changes and delta variations of the C3 and C4 electrodes. Additionally, LDA demonstrated the best performance across the three classifications of subject-independent problems.Os exoesqueletos motorizados desempenham um papel crucial no campo da reabilitação, melhorando a qualidade de vida das pessoas que deles necessitam. Deste modo, são um contributo importante para que os pacientes com condições físicas limitadas sejam mais facilmente integrados na sociedade, proporcionando-lhes mais autonomia e liberdade. Embora esta tecnologia tenha os seus aspetos positivos, ainda existe a necessidade de descrever os correlatos cerebrais direcionados para o controlo do exoesqueleto. Por exemplo, a percepção de diferentes pavimentos quando se usa um exoesqueleto vai provavelmente causar alterações na actividade cerebral, o que pode ter impacto tanto na codificação sensorial como no controlo da interface cérebro-máquina (BCI). Deste modo, o principal objetivo deste trabalho é descrever a atividade cerebral às diferentes texturas dos pavimentos, utilizando o exoesqueleto ExoAtlet ®. A fim de medir, processar, analisar e classificar o impacto de diferentes texturas em ritmos neurofisiológicos, foram registados sinais de 4 minutos atravês the Eletroencefalograma (EEG) com uma touca de 16 canais (actiCAP by Brain Products). Cada um dos três voluntários foi instruído a dar passos no lugar em quatro tipos diferentes de pavimento (plano, alcatifa, espuma, e círculos de borracha) com e sem o exosqueleto, num total de oito condições experimentais diferentes. Foi aplicado um desenho contrabalançado e foi obtido o consentimento informado dos participantes (Comissão para as Ciências da Saúde da Universidade Católica Portuguesa - 99/2022). Adicionalmente, foram selecionados quatro classificadores: máquinas de vetores de suporte (SVM), k-vizinhos mais próximos (KNN), análise discriminante linear (LDA) e redes neuronais artificiais (ANN) para analisar três problemas de classificação distintos. Os resultados obtidos por este estudo demonstraram que existiam alterações associadas à banda de frequência delta para os eléctrodos C3 e C4 e, ao comparar o desempenho dos classificadores, o LDA apresentou a melhor exatidão nos três problemas de classificação envolvendo todos os sujeitos. Assim, estes resultados são consistentes com a hipótese de que o processamento sensorial dos pavimentos durante o controlo do exoesqueleto induz alterações neuronais

Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies

BackgroundDespite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait.Research objectiveThis study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms.MethodsA comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included.Results and significanceClinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges

Wearable in-ear pulse oximetry: theory and applications

Wearable health technology, most commonly in the form of the smart watch, is employed by millions of users worldwide. These devices generally exploit photoplethysmography (PPG), the non-invasive use of light to measure blood volume, in order to track physiological metrics such as pulse and respiration. Moreover, PPG is commonly used in hospitals in the form of pulse oximetry, which measures light absorbance by the blood at different wavelengths of light to estimate blood oxygen levels (SpO2). This thesis aims to demonstrate that despite its widespread usage over many decades, this sensor still possesses a wealth of untapped value. Through a combination of advanced signal processing and harnessing the ear as a location for wearable sensing, this thesis introduces several novel high impact applications of in-ear pulse oximetry and photoplethysmography. The aims of this thesis are accomplished through a three pronged approach: rapid detection of hypoxia, tracking of cognitive workload and fatigue, and detection of respiratory disease. By means of the simultaneous recording of in-ear and finger pulse oximetry at rest and during breath hold tests, it was found that in-ear SpO2 responds on average 12.4 seconds faster than the finger SpO2. This is likely due in part to the ear being in close proximity to the brain, making it a priority for oxygenation and thus making wearable in-ear SpO2 a good proxy for core blood oxygen. Next, the low latency of in-ear SpO2 was further exploited in the novel application of classifying cognitive workload. It was found that in-ear pulse oximetry was able to robustly detect tiny decreases in blood oxygen during increased cognitive workload, likely caused by increased brain metabolism. This thesis demonstrates that in-ear SpO2 can be used to accurately distinguish between different levels of an N-back memory task, representing different levels of mental effort. This concept was further validated through its application to gaming and then extended to the detection of driver related fatigue. It was found that features derived from SpO2 and PPG were predictive of absolute steering wheel angle, which acts as a proxy for fatigue. The strength of in-ear PPG for the monitoring of respiration was investigated with respect to the finger, with the conclusion that in-ear PPG exhibits far stronger respiration induced intensity variations and pulse amplitude variations than the finger. All three respiratory modes were harnessed through multivariate empirical mode decomposition (MEMD) to produce spirometry-like respiratory waveforms from PPG. It was discovered that these PPG derived respiratory waveforms can be used to detect obstruction to breathing, both through a novel apparatus for the simulation of breathing disorders and through the classification of chronic obstructive pulmonary disease (COPD) in the real world. This thesis establishes in-ear pulse oximetry as a wearable technology with the potential for immense societal impact, with applications from the classification of cognitive workload and the prediction of driver fatigue, through to the detection of chronic obstructive pulmonary disease. The experiments and analysis in this thesis conclusively demonstrate that widely used pulse oximetry and photoplethysmography possess a wealth of untapped value, in essence teaching the old PPG sensor new tricks.Open Acces

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders