INDEPENDENT COMPONENT ANALYSIS AND DISCRETE WAVELET TRANSFORM FOR ARTIFACT REMOVAL IN BIOMEDICAL SIGNAL PROCESSING

Recent works have shown that artifact removal in bi omedical signals can be performed by using Discrete Wavelet Transform (DWT) or Independent Component Analysis (ICA). It results often very difficult to remove some artifacts because they could be superimposed on the recordings and they could corrupt the signals in the frequency domain. The two conditions could compromise the performance of both DWT and ICA methods. In this study we show that if the two methods are jointly implemented, it is possible to improve the performances for the artifact rejection procedure. We discuss in detail the new method and we also show how this method provides advantages with respect to DWT of ICA procedure. Finally, we tested the new approach on real data

Independent Component Analysis and Complex Wavelet Decomposition for Classifying Medical Data

Abstract In this article, we describe a new classification methodology based on the use of Independent Component Analysis and Wavelet decomposition (ICAW) techniques. An ensemble system of classifiers is built such that each classifier independently decides the assignation of the test examples on several representations resulted by taking projections computed by wavelets and Independent Component Analysis (ICA). The representations used by the individual classifiers are obtained by taking the real and imaginary part of the wavelet decompositions, as well as the magnitude and phase. The decision of the ensemble system is based on several types of voting rules (such as the majority voting rule or a weighted voting rule). The experimental results presented in the paper show that the proposed ensemble systems of classifiers provide higher accuracy in the particular problem of classifying biomedical data

Evaluation of methods for extraction of the volitional EMG in dynamic hybrid muscle activation

BACKGROUND: Hybrid muscle activation is a modality used for muscle force enhancement, in which muscle contraction is generated from two different excitation sources: volitional and external, by means of electrical stimulation (ES). Under hybrid activation, the overall EMG signal is the combination of the volitional and ES-induced components. In this study, we developed a computational scheme to extract the volitional EMG envelope from the overall dynamic EMG signal, to serve as an input signal for control purposes, and for evaluation of muscle forces. METHODS: A "synthetic" database was created from in-vivo experiments on the Tibialis Anterior of the right foot to emulate hybrid EMG signals, including the volitional and induced components. The database was used to evaluate the results obtained from six signal processing schemes, including seven different modules for filtration, rectification and ES component removal. The schemes differed from each other by their module combinations, as follows: blocking window only, comb filter only, blocking window and comb filter, blocking window and peak envelope, comb filter and peak envelope and, finally, blocking window, comb filter and peak envelope. RESULTS AND CONCLUSION: The results showed that the scheme including all the modules led to an excellent approximation of the volitional EMG envelope, as extracted from the hybrid signal, and underlined the importance of the artifact blocking window module in the process. The results of this work have direct implications on the development of hybrid muscle activation rehabilitation systems for the enhancement of weakened muscles

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

Signal processing approaches to diagnosis of esophageal motility disorders

Esophageal Motility Disorders (EGMDs) are a group of abnormalities characterized by the muscular dysfunction of the esophagus in the transportation of food from the oral cavity to the stomach. EGMDs typically cause chronic problems and affect a vast and ever-increasing number of the global population. The diagnosis of EGMDs mainly relies on a key test presently used to study the esophagus motility, known as esophageal manometry (EGM). EGM involves pressure measurements inside the esophagus, which provide information pertaining to its contractions. The diagnosis process is mainly based on visual inspection of the EGM test results to find certain characteristics of the manometric patterns. There are several factors that make such inspection tedious. For instance, manometry test results are often contaminated with a considerable amount of noise, (e.g. noise from external environment) and artifacts, (e.g. respiration artifacts) leading to a longer and more complex diagnosis process. As such, the diagnosis based on visual inspection is prone to human error and demands extensive amount of expert's time. This thesis introduces new signal processing approaches to provide an accurate means for the diagnosis of EGMDs as well as to reduce the amount of time spent on the diagnosis process. Specifically, a new technique known as wavelet decomposition (WD) is applied to the filtering of the EGM data. A nonlinear pulse detection technique (NPDT) is applied to the de-noised data leading to extraction of diagnostically important information i.e. esophageal pulses. Such information is used to generate a model using a statistical pulse modeling (SPM) technique, which can classify the EGM patterns. The proposed approaches are applied to the EGM data of 20 patients and compared with those from existing techniques. Such comparisons illustrate the advantages of the proposed approaches in terms of accuracy and efficiency. As part of this thesis, a new circuit-based approach is proposed for the treatment of Gastroesophageal Reflux Disease (GERD), i.e. the most prevalent disease caused by EGMDs. The objective is to provide a framework for further research towards the implementation of the proposed approach for GERD treatment

Estudio espectral del ritmo eléctrico básico del intestino delgado para la monitorización no invasiva del marcapasos intestinal

El aparato digestivo permite que los alimentos se conviertan en nutrientes y proporcionen al organismo las calorías y los elementos fundamentales para la vida, al mismo tiempo que se expulsan y eliminan los productos residuales de forma adecuada. La motilidad intestinal es muy importante para conseguir la segmentación del quimo y el tránsito intestinal y está determinada por la actividad mioeléctrica de las capas musculares intestinales. Dicha actividad también se le denomina electroenterograma (EEnG). La señal mioeléctrica es el resultado de una componente de baja frecuencia que en condiciones fisiológicas está siempre presente llamada onda lenta (OL) o ritmo eléctrico básico (BER) que constituye el marcapasos intestinal; y una componente de alta frecuencia llamada spike bursts o potenciales rápidos de acción que está asociada a las contracciones intestinales. El análisis del EEnG es un paso clave para monitorizar la actividad intestinal. El estudio del BER intestinal no sólo proporciona información acerca del ritmo básico de las contracciones del intestino, sino que puede ayudar a diagnosticar algunas patologías gastrointestinales. Para ofrecer esta herramienta como aplicación clínica, el registro de la señal del EEnG debe ser no invasivo. El objetivo de la presente Tesis Doctoral es detectar la actividad del marcapasos intestinal y caracterizar el ritmo eléctrico básico en el EEnG externo, comparándolo y estudiando su relación con el EEnG interno. Las señales analizadas fueron obtenidas simultáneamente en la superficie abdominal y en la serosa intestinal de perros Beagle en estado de ayuno. Los métodos de estimación autoregresivo (AR), autoregresivo de media móvil (ARMA), Prony y clasificación de señales múltiples (MUSIC), se emplearon para determinar la distribución espectral de potencia asociada a la actividad de la onda lenta, tanto en los registros internos como externos. Por otro lado, para estudiar la relación entre el espectro de la señal captada en superficie y las señales internas, se estimaron las funciones de coherencia utilizando los modelos autoregresivo multivariante (ARM) y MUSIC.Moreno Vázquez, JDJ. (2011). Estudio espectral del ritmo eléctrico básico del intestino delgado para la monitorización no invasiva del marcapasos intestinal [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/14276Palanci