Advanced bioelectrical signal processing methods: Past, present and future approach - Part III: Other biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).Web of Science2118art. no. 606

Методе за оцену електричне активности глатких мишића

Recording of the smooth stomach muscles' electrical activity can be performed by means of Electrogastrography (EGG), a non-invasive technique for acquisition that can provide valuable information regarding the functionality of the gut. While this method had been introduced for over nine decades, it still did not reach its full potential. The main reason for this is the lack of standardization that subsequently led to the limited reproducibility and comparability between different investigations. Additionally, variability between many proposed recording approaches could make EGG unappealing for broader application. The aim was to provide an evaluation of a simplified recording protocol that could be obtained by using only one bipolar channel for a relatively short duration (20 minutes) in a static environment with limited subject movements. Insights into the most suitable surface electrode placement for EGG recording was also presented. Subsequently, different processing methods, including Fractional Order Calculus and Video-based approach for the cancelation of motion artifacts – one of the main pitfalls in the EGG technique, was examined. For EGG, it is common to apply long-term protocols in a static environment. Our second goal was to introduce and investigate the opposite approach – short-term recording in a dynamic environment. Research in the field of EGG-based assessment of gut activity in relation to motion sickness symptoms induced by Virtual Reality and Driving Simulation was performed. Furthermore, three novel features for the description of EGG signal (Root Mean Square, Median Frequency, and Crest Factor) were proposed and its applicability for the assessment of gastric response during virtual and simulated experiences was evaluated. In conclusion, in a static environment, the EGG protocol can be simplified, and its duration can be reduced. In contrast, in a dynamic environment, it is possible to acquire a reliable EGG signal with appropriate recommendations stated in this Doctoral dissertation. With the application of novel processing techniques and features, EGG could be a useful tool for the assessment of cybersickness and simulator sickness.Снимање електричне активности глатких мишића желуца може се реализовати употребом електрогастрографије (ЕГГ), неинвазивне методе која пружа значајне информације везане за функционисање органа за варење. Упркост чињеници да је откривена пре више од девет деценија, ова техника још увек није остварила свој пун потенцијал. Основни разлог за то је недостатак стандардизације који условљава ограничења у смислу поновљивости и упоредивости између различитих истраживања. Додатно, варијабилност која је присутна у примени различитих препоручених поступака снимања, може смањити интерес за употребу ЕГГ-а код широког опсега потенцијалних корисника. Наш циљ је био да пружимо евалуацију поједностављене методе мерења тј. протокола који укључује само један канал током релативно кратког временског периода (20 минута) у статичким условима са ограниченим кретањем субјекта тј. у мировању. Такође, приказали смо наше ставове у вези најприкладније позиције површинских електрода за ЕГГ снимање. Презентовали смо и резултате испитивања метода, на бази обраде видео снимка као и фракционог диференцијалног рачуна, за отклањање артефаката помераја – једног од највећих изазова са којима је суочена ЕГГ метода. За ЕГГ је уобичајено да се користе дуготрајни протоколи у статичким условима. Наш други циљ био је да представимо и оценимо употребљивост супротног приступа – краткотрајних снимања у динамичким условима. Реализовали смо истраживање на пољу оцене активности желуца током појаве симптома мучнине изазване виртуелном реалношћу и симулацијом вожње. За потребе методе за оцену електричне активности желуца, предложили смо три нова параметра за квантификацију ЕГГ сигнала (ефективну вредност амплитуде, медијану и крест фактор) и извршили процену њихове прикладности за оцену гастроинтестиналног тракта током коришћења виртуелне реалности и симулатора вожње. Закључак је да ЕГГ протокол у статичким условима може бити упрошћен и његово трајање може бити редуковано, док је у динамичким условима могуће снимити одговарајући ЕГГ сигнал, али уз праћење препорука наведених у овој тези. Употребом нових техника за процесирање сигнала и прорачун одговарајућих параметара, ЕГГ може бити корисна техника за оцену мучнине изазване коришћењем симулатора и производа виртуелне реалност

Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram

Funding Information: For Arnaldo Batista and Manuel Ortigueira, this work was supported by the Portuguese National Funds, through the FCT Foundation for Science and Technology, within the scope of the CTS Research Unit, Center of Technology and Systems, UNINOVA, under the project UIDB/00066/2020 (FCT). Helena Mouriño was financed by national funds through FCT, Fundação para a Ciência e a Tecnologia, under the project UIDB/00006/2020. Publisher Copyright: © 2022 by the authors.The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be −16.74%, −20.32%, and −15.78%, respectively (p-value = 1.31 × 10−12).publishersversionpublishe

Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions

Electrohysterography (EHG) is a noninvasive technique for monitoring uterine electrical activity. However, the presence of artifacts in the EHG signal may give rise to erroneous interpretations and make it difficult to extract useful information from these recordings. The aim of this work was to develop an automatic system of segmenting EHG recordings that distinguishes between uterine contractions and artifacts. Firstly, the segmentation is performed using an algorithm that generates the TOCO-like signal derived from the EHG and detects windows with significant changes in amplitude. After that, these segments are classified in two groups: artifacted and nonartifacted signals. To develop a classifier, a total of eleven spectral, temporal, and nonlinear features were calculated from EHG signal windows from 12 women in the first stage of labor that had previously been classified by experts. The combination of characteristics that led to the highest degree of accuracy in detecting artifacts was then determined. The results showed that it is possible to obtain automatic detection of motion artifacts in segmented EHG recordings with a precision of 92.2% using only seven features. The proposed algorithm and classifier together compose a useful tool for analyzing EHG signals and would help to promote clinical applications of this technique

Técnicas de Filtragem Adaptativa para Redução da Interferência Respiratória Materna no Eletrohisterograma

O parto prematuro é qualquer nascimento que ocorre antes das 37 semanas completas de gestação e possui elevados riscos para a saúde do feto. A prematuridade apresenta-se como a principal causa de morte em recém-nascidos no mundo e a segunda principal causa de morte infantil, pelo que a previsão de partos pré-termo é um problema de extrema importância que ainda carece de uma solução satisfatória. Nesta perspetiva e em estudos recentes, o eletrohisterograma (EHG) surgiu como uma alternativa mais precisa ao instrumento atualmente usado na descrição das contrações uterinas, o tocograma. O EHG consiste no registo do sinal elétrico gerado pela atividade contráctil do útero e apresenta potencial para a previsão do parto termo e pré-termo, podendo auxiliar na tomada de decisão médica relativamente à utilização de terapia tocolítica (antagonista da oxitocina) para retardar o parto. Esta medicação possui efeitos secundários pelo que deve ser evitada quando possível. Deste modo, o EHG não invasivo é uma ferramenta apropriada para a monitorização da gravidez e do parto. As ondas Alvarez (Alv) são uma das componentes que constitui o sinal do EHG e, segundo vários autores, possuem potencial para a previsão do parto termo e pré-termo, pelo que o seu estudo é um assunto de grande interesse. Contudo, o sinal elétrico proveniente do eletromiograma da respiração materna (MR-EMG) apresenta-se como uma interferência prevalente no EHG, na medida em que a sua banda de frequência se sobrepõe à das ondas Alv, afetando os resultados obtidos. Portanto, um método de atenuação do MR-EMG sem comprometer as ondas Alv é necessário. As propriedades dos filtros adaptativos tornam-nos adequados para esta tarefa. No entanto, selecionar o filtro adaptativo ótimo e os respetivos parâmetros é uma tarefa importante para o sucesso da operação de filtragem. Neste trabalho foram estudados 16 algoritmos de filtragem adaptativa. Para a seleção automática dos filtros com melhor desempenho e dos respetivos parâmetros otimizados foi implementado um algoritmo, que visa minimizar a raiz do erro quadrático médio (RMSE) entre o sinal uterino puro e o sinal filtrado. Deste modo, para uma fase inicial, a utilização de sinais sintéticos é fundamental, pois de outra forma não seria possível aceder ao sinal uterino puro. Os filtros Wiener, Recursive Least Squares (RLS), Householder Recursive Least Squares (HRLS) e QR-Decomposition Recursive Least Squares (QRD-RLS) foram os que obtiveram melhor desempenho para os parâmetros otimizados, cujo comprimento do filtro (L) adota o valor de 2 (para todos eles) e o fator de esquecimento (λ) é 1 (para os três últimos). Numa segunda fase do trabalho, os filtros e parâmetros otimizados foram aplicados a dados de EHG da vida real (Icelandic Reykjavik University EHG database). Para avaliar quantitativamente os resultados obtidos analisaram-se seis propriedades dos periodogramas das ondas Alv: a largura de banda ocupada (bw), os limites inferior e superior da bw (f lo e f hi), a potência contida na bw (power), a amplitude máxima (peak) e a frequência para a qual essa amplitude ocorre (f req_peak). Os resultados obtidos mostraram que foi possível atenuar o MR-EMG preservando as ondas Alv. Verificaram-se variações como o aumento de bw e f hi, bem como, a diminuição de f lo, power, peak e f req_peak, sobretudo para o filtroWiener. Estas alterações indicaram que a componente do MR-EMG foi atenuada das ondas Alv.Preterm birth is defined as any birth occurring before the 37 weeks of pregnancy, which poses several risks for the newborn. It is the leading cause of death among newborns and the second major cause of infant death. Preterm risk assessment is, therefore, very important but it still lacks of an accurate monitoring tool. Recent studies have shown that eletrohysterogram (EHG) is a more precise alternative to the tocogram, where the latter is frequently used to describe uterine contractions. EHG consists in the registration of the electric signals resulting from uterine contractions and it can potentially be used to predict both preterm and term birth. Hence, this exam can improve medical decision-making for tocolithic therapy (i.e., the use of labor suppressants). Since this kind of medication presents secondary effects, it should be avoided whenever possible. Thus, the non-invasive EHG is a suitable tool for monitoring pregnancy and birth. Alvarez (Alv) waves are one of the components of the EHG signal and, according to many authors, can potentially predict term and preterm birth. Therefore, the study of these EHG signal components is very relevant. Yet, the electromyographic signals resulting from the maternal respiration (MR-EMG) tend to interfere with the EHG. This affects the EHG signal since the MR-EMG signal bandwidth overlaps with that of the Alv waves. Therefore, a method capable of attenuating the MR-EMG signal without compromising the Alv waves would be suitable for this application. Adaptive filtering seems to be a good choice for such a task. However, selecting the optimal adaptive filter and its parameters is an important first step for the filtering operation’s success. Sixteen different adaptive filtering algorithms were investigated in the present work. The best performing algorithms and their respective parameters were automatically selected based on a root mean squared error (RMSE) optimisation procedure between the pure and the filtered uterine signals. Thus, for an initial phase, the use of synthetic signals is essential, as otherwise, it was not possible to access the pure uterine signal. TheWiener, the Recursive Least Squares (RLS), the Householder Recursive Least Squares (HRLS) and the QR-Decomposition Recursive Least Squares (QRD-RLS) filters were found as the best performing adaptive filters. All of them presented an optimised filter length L=2 and, the last three, a forgetting factor λ=1. Afterwards, the optimised filter parameters obtainedwere applied to real EHG signals, namely signals from the Icelandic Reykjavik University EHG database. The performance of the aformentioned filters was assessed based on specific properties of the periodograms obtained from the Alv waves: the occupied bandwidth (bw), the limiting frequencies of bw (lower-bound frequency, f lo, and upper-bound frequency, f hi), the power contained in the bw (power), the maximum amplitude (peak) and the frequency for which this amplitude occurs (f req_peak). The results showed that it is possible to attenuate the MR-EMG signal while preserving the Alv waves. Moreover, increases in bw and f hi were observed as well as decreases in f lo, power, peak and f req_peak, especially for theWiener filter. These variations account for MR-EMG attenuation in the Alv waves

Fear Classification using Affective Computing with Physiological Information and Smart-Wearables

Mención Internacional en el título de doctorAmong the 17 Sustainable Development Goals proposed within the 2030 Agenda and adopted by all of the United Nations member states, the fifth SDG is a call for action to effectively turn gender equality into a fundamental human right and an essential foundation for a better world. It includes the eradication of all types of violence against women. Focusing on the technological perspective, the range of available solutions intended to prevent this social problem is very limited. Moreover, most of the solutions are based on a panic button approach, leaving aside the usage and integration of current state-of-the-art technologies, such as the Internet of Things (IoT), affective computing, cyber-physical systems, and smart-sensors. Thus, the main purpose of this research is to provide new insight into the design and development of tools to prevent and combat Gender-based Violence risky situations and, even, aggressions, from a technological perspective, but without leaving aside the different sociological considerations directly related to the problem. To achieve such an objective, we rely on the application of affective computing from a realist point of view, i.e. targeting the generation of systems and tools capable of being implemented and used nowadays or within an achievable time-frame. This pragmatic vision is channelled through: 1) an exhaustive study of the existing technological tools and mechanisms oriented to the fight Gender-based Violence, 2) the proposal of a new smart-wearable system intended to deal with some of the current technological encountered limitations, 3) a novel fear-related emotion classification approach to disentangle the relation between emotions and physiology, and 4) the definition and release of a new multi-modal dataset for emotion recognition in women. Firstly, different fear classification systems using a reduced set of physiological signals are explored and designed. This is done by employing open datasets together with the combination of time, frequency and non-linear domain techniques. This design process is encompassed by trade-offs between both physiological considerations and embedded capabilities. The latter is of paramount importance due to the edge-computing focus of this research. Two results are highlighted in this first task, the designed fear classification system that employed the DEAP dataset data and achieved an AUC of 81.60% and a Gmean of 81.55% on average for a subjectindependent approach, and only two physiological signals; and the designed fear classification system that employed the MAHNOB dataset data achieving an AUC of 86.00% and a Gmean of 73.78% on average for a subject-independent approach, only three physiological signals, and a Leave-One-Subject-Out configuration. A detailed comparison with other emotion recognition systems proposed in the literature is presented, which proves that the obtained metrics are in line with the state-ofthe- art. Secondly, Bindi is presented. This is an end-to-end autonomous multimodal system leveraging affective IoT throughout auditory and physiological commercial off-theshelf smart-sensors, hierarchical multisensorial fusion, and secured server architecture to combat Gender-based Violence by automatically detecting risky situations based on a multimodal intelligence engine and then triggering a protection protocol. Specifically, this research is focused onto the hardware and software design of one of the two edge-computing devices within Bindi. This is a bracelet integrating three physiological sensors, actuators, power monitoring integrated chips, and a System- On-Chip with wireless capabilities. Within this context, different embedded design space explorations are presented: embedded filtering evaluation, online physiological signal quality assessment, feature extraction, and power consumption analysis. The reported results in all these processes are successfully validated and, for some of them, even compared against physiological standard measurement equipment. Amongst the different obtained results regarding the embedded design and implementation within the bracelet of Bindi, it should be highlighted that its low power consumption provides a battery life to be approximately 40 hours when using a 500 mAh battery. Finally, the particularities of our use case and the scarcity of open multimodal datasets dealing with emotional immersive technology, labelling methodology considering the gender perspective, balanced stimuli distribution regarding the target emotions, and recovery processes based on the physiological signals of the volunteers to quantify and isolate the emotional activation between stimuli, led us to the definition and elaboration of Women and Emotion Multi-modal Affective Computing (WEMAC) dataset. This is a multimodal dataset in which 104 women who never experienced Gender-based Violence that performed different emotion-related stimuli visualisations in a laboratory environment. The previous fear binary classification systems were improved and applied to this novel multimodal dataset. For instance, the proposed multimodal fear recognition system using this dataset reports up to 60.20% and 67.59% for ACC and F1-score, respectively. These values represent a competitive result in comparison with the state-of-the-art that deal with similar multi-modal use cases. In general, this PhD thesis has opened a new research line within the research group under which it has been developed. Moreover, this work has established a solid base from which to expand knowledge and continue research targeting the generation of both mechanisms to help vulnerable groups and socially oriented technology.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: David Atienza Alonso.- Secretaria: Susana Patón Álvarez.- Vocal: Eduardo de la Torre Arnan