Generalized recursive algorithm for fetal electrocardiogram isolation from non-invasive maternal electrocardiogram

Non-invasive maternal electrocardiogram recording is the least unpleasant method to record a weak fetal electrocardiogram signal. The importance of this recording lies in the fact that it reveals crucial information about the fetal health state, especially during the last four weeks of pregnancy. This paper will be concerned with a new adaptive algorithm, namely the generalized recursive algorithm, to isolate and get the fetal electrocardiogram from the abdominal maternal electrocardiogram. This is achieved using a non-invasive method for bi-channel maternal electrocardiogram recordings i.e., with the thoracic maternal electrocardiogram as a reference signal, and the abdominal maternal electrocardiogram as a primary signal. Prior to this procedure, the discrete wavelet transform (DWT) method is applied to the abdominal electrocardiogram signal to clean it from any additive noise and the baseline wandering that is generally present on the raw recordings. The proposed new adaptive filter is shown to deliver improved characteristics through simulations. These simulations were performed on both synthetic and actual signals. This work was compared with the normalized least mean square algorithm

Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research

This PhD thesis presents the development of a novel open multi-modal dataset for advanced studies on fetal cardiological assessment, along with a set of signal processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography (ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological recordings characterized by high sampling frequency and digital resolution, maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave Doppler (PWD) recordings and clinical annotations provided by expert clinicians at the time of the signal collection. To the best of our knowledge, there are no similar dataset available. The signal processing tools targeted both the PWD and the non-invasive fetal ECG, exploiting the recorded dataset. About the former, the study focuses on the processing aimed at the preparation of the signal for the automatic measurement of relevant morphological features, already adopted in the clinical practice for cardiac assessment. To this aim, a relevant step is the automatic identification of the complete and measurable cardiac cycles in the PWD videos: a rigorous methodology was deployed for the analysis of the different processing steps involved in the automatic delineation of the PWD envelope, then implementing different approaches for the supervised classification of the cardiac cycles, discriminating between complete and measurable vs. malformed or incomplete ones. Finally, preliminary measurement algorithms were also developed in order to extract clinically relevant parameters from the PWD. About the fetal ECG, this thesis concentrated on the systematic analysis of the adaptive filters performance for non-invasive fetal ECG extraction processing, identified as the reference tool throughout the thesis. Then, two studies are reported: one on the wavelet-based denoising of the extracted fetal ECG and another one on the fetal ECG quality assessment from the analysis of the raw abdominal recordings. Overall, the thesis represents an important milestone in the field, by promoting the open-data approach and introducing automated analysis tools that could be easily integrated in future medical devices

Framework para el desarrollo y entrenamiento de sistemas de indeferencia difusa siguiendo métodos de desarrollo dirigido por modelos

224 p.Este trabajo de tesis doctoral presenta un modelo independiente de la computación de un Diagnóstico Diferencial (DD), así como un modelo independiente de la plataforma de un Sistema de Inferencia Difusa. Se han utilizado los Métodos de Desarrollo Dirigido por Modelos (MDDM) en la concepción de los modelos, los cuales, además de facilitar la definición de los modelos, ofrecen herramientas para la realización de transformaciones entre ellos. Así, en el presente trabajo también se exponen las transformaciones entre los modelos de DD y SID y las transformaciones para la generación automática de SID expresados en lenguajes concretos a partir de los modelos de SID independientes de la plataforma. Los SID dependientes de la plataforma pueden ser incluidos en el formalismo de representación de Guías Clínicas Informatizadas (GCI) Aide. Así mismo, en la tesis también se incluye una descripción de las herramientas que facilitan la definición de modelos de DD y SID, así como la generación automática de SID en lenguajes concretos utilizables en distintos motores de razonamiento. Es de reseñar la adición de un módulo de aprendizaje automático mediante un Algoritmo Genético que permite adaptar algunas características de los modelos de SID a los datos reales de entrenamiento. Las herramientas y modelos se han validado en dos ámbitos. Por un lado, se han utilizado en el cribado neonatal, una prueba diagnóstica dirigida a la identificación presintomática de enfermedades graves con el fin de tratarlas precozmente y así prevenir y minimizar minusvalías neurológicas, orgánicas y psíquicas. Por otro lado, se han utilizado en el diagnóstico de la hiperamonemia, una Enfermedad Rara que se debe tratar de forma urgente para evitar graves secuelas neurológicas e incluso la muerte. En ambos casos, los SID creados se han integrado en unas GCI para ser evaluados

Framework para el desarrollo y entrenamiento de sistemas de indeferencia difusa siguiendo métodos de desarrollo dirigido por modelos

224 p.Este trabajo de tesis doctoral presenta un modelo independiente de la computación de un Diagnóstico Diferencial (DD), así como un modelo independiente de la plataforma de un Sistema de Inferencia Difusa. Se han utilizado los Métodos de Desarrollo Dirigido por Modelos (MDDM) en la concepción de los modelos, los cuales, además de facilitar la definición de los modelos, ofrecen herramientas para la realización de transformaciones entre ellos. Así, en el presente trabajo también se exponen las transformaciones entre los modelos de DD y SID y las transformaciones para la generación automática de SID expresados en lenguajes concretos a partir de los modelos de SID independientes de la plataforma. Los SID dependientes de la plataforma pueden ser incluidos en el formalismo de representación de Guías Clínicas Informatizadas (GCI) Aide. Así mismo, en la tesis también se incluye una descripción de las herramientas que facilitan la definición de modelos de DD y SID, así como la generación automática de SID en lenguajes concretos utilizables en distintos motores de razonamiento. Es de reseñar la adición de un módulo de aprendizaje automático mediante un Algoritmo Genético que permite adaptar algunas características de los modelos de SID a los datos reales de entrenamiento. Las herramientas y modelos se han validado en dos ámbitos. Por un lado, se han utilizado en el cribado neonatal, una prueba diagnóstica dirigida a la identificación presintomática de enfermedades graves con el fin de tratarlas precozmente y así prevenir y minimizar minusvalías neurológicas, orgánicas y psíquicas. Por otro lado, se han utilizado en el diagnóstico de la hiperamonemia, una Enfermedad Rara que se debe tratar de forma urgente para evitar graves secuelas neurológicas e incluso la muerte. En ambos casos, los SID creados se han integrado en unas GCI para ser evaluados

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Advanced robust non-invasive foetal heart detection techniques during active labour using one pair of transabdominal electrodes

The thesis proposes and evaluates three state-of-the-art signal processing techniques to detect fetal heartbeats within each maternal cardiac cycle, during labour contractions, using only a pair of transabdominal electrodes. The first and second techniques are, namely, the structured third- order cumulant-slice-template matching and the bispectral-contours-template matching for fetal QRS identification, respectively. The third technique is based on the modified and appropriately weighted spectral multiple signal classification (MUSIC) with incorporated covariance matrix for uterine contraction noise-like interfering signals also contaminated with noise. Essentially, two modifications to the standard MUSIC have been developed in order to enhance the performance of the spectral estimator in our applied work. The first modification involves the introduction of an optimised weighting function to the segmented ECG covariance matrix, and is chiefly aimed at enhancing the fetal QRS major spectral peak which occurs at around 30 Hz against the mother QRS major spectral peak usually occurring around 17 Hz and all other noise contributions. Additional optional pseudo-bispectral enhancement to sharpen the maternal and fetal spectral peaks, in particular when the mother and fetal R-waves are temporally coincident, have been achieved. The second modification to the spectral MUSIC is the removal of the unjustified assumption that only white Gaussian noise is present and the incorporation of the actual measured labour uterine contraction covariance matrix in reconfigured subspace analysis. This inevitably leads to the generalised eigenvectors - eigenvalues decomposition modern signal processing. This is now coined the modified, interference incorporated pseudo-spectral MUSIC. The above mentioned first and second techniques are higher-order statistics-based (HOS) and hybrid involving both signal processing and NN classifiers. The third technique is second-order statistics-based (SOS). In all techniques, the removal of signal non-linearity with the aid of non-linear Volterra synthesisers plays a crucial part in the fetal detection integrity. Accurately assessed fetal heart classification rates as high as 95% have been achieved during labour, thus helping to provide non-invasive transparency to fetal intrapartum welfare. Performance analysis and evaluation processes involved more than 30 critical cases classified as “fetal under stress in labour” recorded in a London hospital database and used both transbadominal ECG electrodes and fetal scalp electrodes. The latter facilitates detection of the instantaneous fetal heart rate which is then used as the Reference Fetal Heart Rate in the assessment of the classification rate of each of the above mentioned techniques. It will be shown that the fetal heartbeats are completely masked by uterine activity and noise artefacts in all the recorded transabdominal maternal ECG signals. The fetal scalp electrode was, therefore, deemed necessary to provide the highest accurate measure of fetal heart functionality (from the hospital viewpoint), and in the assessment of the three non-invasive techniques presented in this thesis. The techniques may also be used during gestation and as early as 10 weeks