New estimators and guidelines for better use of fetal heart rate estimators with Doppler ultrasound devices

International audienceCharacterizing fetal wellbeing with a Doppler ultrasound device requires computation of a score based on fetal parameters. In order to analyze the parameters derived from the fetal heart rate correctly, an accuracy of 0.25 beats per minute is needed. Simultaneously with the lowest false negative rate and the highest sensitivity, we investigated whether various Doppler techniques ensure this accuracy. We found that the accuracy was ensured if directional Doppler signals and autocorrelation estimation were used. Our best estimator provided sensitivity of 95.5%, corresponding to an improvement of 14% compared to the standard estimator

Pregnancy monitoring

International audienc

Compressive-Sensing-based Multidimensional Doppler signal analysis for fetal activity monitoring

International audienceFetal activity monitoring is an important part of monitoring at-risk pregnancies and labor. Fetal activity parameters (FAP) consist of fetal heart rate (FHR), fetal movements (FM) rate, fetal tone, fetal breathing (FB) and movement. FAP monitoring is to date an open challenge for mainly two reasons. First, the estimation of FAP is highly time consuming and thus cannot be used routinely. Second, part of FAP such as FM estimation is sometimes subjective (mothers are asked to count the fetal movements) and inaccurate. For this purpose, we developed a 2MHz pulsed wave ultrasound Doppler system, consisting of 12 transducers with 5 adjustable gates. The Doppler signals were sampled at 1KHz. Several recent papers have shown the accuracy of our system. However, its counterpart is the huge number of signals necessary to estimate the FAP. Specifically, each millisecond, 60 Doppler samples are acquired. In order to reduce the volume of the acquired data and to accelerate the FAP estimation rate, we propose herein to investigate the interest of compressive sensing (CS) techniques to our application

Coarse-Grained Multifractality Analysis Based on Structure Function Measurements to Discriminate Healthy from Distressed Foetuses

International audienceThis paper proposes a combined coarse-grained multifractal method to discriminate between dis-tressed and normal foetuses. The coarse-graining operation was performed by means of a coarse-grained procedure and the multifractal operation was based on a structure function. The proposed method was evaluated by one hundred recordings including eighty normal foetuses and twenty dis-tressed foetuses. We found that it was possible to discriminate between distressed and normal foetuses using the Hurst exponent, singularity and Holder spectra

Automatic Extraction of Doppler Parameters for the Assessment of Fetal and Maternal Health

In developing countries, the availability of personnel with training in the use of ultrasound devices and the availability of conventional ultrasound equipment may be very limited. It is therefore beneficial to automate the diagnostic features of an ultrasound device.The Umoja project is an initiative by The National Center of Fetal Medicine (NCFM) at St. Olavs Hospital and the Norwegian University of Science and Technology (NTNU). The technical part of this project involves the development of the Umoja ultrasound system, a tablet-based ultrasound machine with an intuitive user interface. An automatic heart rate detection algorithm was developed in order to be implemented on the Umoja system in the future. The algorithm was developed using Doppler IQ data from two patients. Methods using only high frequencies as well as only low (tissue) frequencies were evaluated.Manual heart rate measurements were made in order to verify the accuracy of the algorithm.The automatic calculations differed from the manual measurements on average up to one BPM, with a negative bias. When using only low frequent tissue data, the results were improved for heart data which had not been filtered by the scanner.Most of the rejected calculations were found in segments with low power.Based on the Matlab timing results and the accuracy of the heart rate calculations, the algorithm appears to represent a viable method for heart rate detection on a low-cost ultrasound device

Estimating pulse wave velocity using mobile phone sensors

Pulse wave velocity has been recognised as an important physiological phenomenon in the human body, and its measurement can aid in the diagnosis and treatment of chronic diseases. It is the gold standard for arterial stiffness measurements, and it also shares a positive relationship with blood pressure and heart rate. There exist several methods and devices via which it can be measured. However, commercially available devices are more geared towards working health professionals and hospital settings, requiring a significant monetary investment and specialised training to operate correctly. Furthermore, most of these devices are not portable and thus generally not feasible for private home use by the common individual. Given its usefulness as an indicator of certain physiological functions, it is expected that having a more portable, affordable, and simple to use solution would present many benefits to both end users and healthcare professionals alike. This study investigated and developed a working model for a new approach to pulse wave velocity measurement, based on existing methods, but making use of novel equipment. The proposed approach made use of a mobile phone video camera and audio input in conjunction with a Doppler ultrasound probe. The underlying principle is that of a two-point measurement system utilising photoplethysmography and electrocardiogram signals, an existing method commonly found in many studies. Data was collected using the mobile phone sensors and processed and analysed on a computer. A custom program was developed in MATLAB that computed pulse wave velocity given the audio and video signals and a measurement of the distance between the two data acquisition sites. Results were compared to the findings of previous studies in the field, and showed similar trends. As the power of mobile smartphones grows, there exists potential for the work and methods presented here to be fully developed into a standalone mobile application, which would bring forth real benefits of portability and cost-effectiveness to the prospective user base

Spectral analysis of embolic signals

Tese de dout., Engenharia Electrónica e Computação, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2005Os parâmetros espectrais do sinal Doppler são usados na caracterização de fluxo sanguíneo. No caso particular do fluxo em artéria cerebral média, a caracterização pode incluir a detecção e classificação de embolias. Para este efeito pretende-se estudar o desempenho de métodos de análise espectral, nomeadamente os que tenham demonstrado bons resultados quando aplicados a sinais Doppler em outras artérias. Para melhor quantificar o desempenho dos estimadores espectrais, é necessário conhecer à priori as características particulares do sinal, facto que se pretende como objectivo final deste estudo. No sentido de disponibilizar sinais-referência para a análise do desempenho dos estimadores espectrais na detecção de embolias, foi desenvolvido um simulador de sinais de artéria cerebral média, com e sem embolias. Como entradas do simulador são utilizadas curvas médias extraídas de sinais clínicos, recorrendo a um algoritmo criado para o efeito, o Sequential Phase Shift Averaging. São também definidas pelo utilizador características dos êmbolos, tais como, velocidade, dimensão efectiva e potência devolvidas pela instrumentação ultra-sónica. Durante este estudo considerou-se o fluxo sanguíneo caracterizado por quatro parâmetros espectrais: frequência máxima, frequência média, raiz quadrada de meia largura de banda, e, variação da potência ultra-sónica ao longo do tempo; este último como sendo o mais relevante para a identificação e diferenciação dos êmbolos. Recorrendo aos sinais simulados, e, analisando os espectros dos sinais de fluxo sem embolias, verifica-se que a Short Time Fourier Transform estima melhor os parâmetros espectrais referidos do que a distribuição tempo-frequência de Choi- Williams ou o método paramétrico tempo-frequência de Covariância Modificada. A análise de espectros de sinais simulados de fluxo com embolias demonstra uma performance idêntica entre os métodos de análise temporal e a Short Time Fourier Transform, esta na versão em que o espectro do ciclo cardíaco é composto por elevada taxa de sobreposição de espectros de segmentos desse ciclo. Esta condicionante associada à constatação de que uma mesma embolia é captada distintamente consoante iii o local do ciclo cardíaco em observação induziu a criação de uma nova representação espectral. A representação proposta, de nome Space-frequency representation, permite a identificação visual da passagem do êmbolo pela janela de observação ultra-sónica. A pesquisa da existência do êmbolo é feita em função da velocidade sanguínea máxima instantânea, e a visualização da potência ultra-sónica por ele retornada é dimensionada adaptativamente de acordo com a relação espaço-frequência instantânea calculada. Esta metodologia permitirá introduzir vantagens significativas no diagnóstico clínico da circulação do fluxo sanguíneo em artéria cerebral média.UNESCO. MAGIAS (Métodos Avanzados de Generación de Imágenes Acústicas)

A deep learning mixed-data type approach for the classification of FHR signals

The Cardiotocography (CTG) is a widely diffused monitoring practice, used in Ob-Gyn Clinic to assess the fetal well-being through the analysis of the Fetal Heart Rate (FHR) and the Uterine contraction signals. Due to the complex dynamics regulating the Fetal Heart Rate, a reliable visual interpretation of the signal is almost impossible and results in significant subjective inter and intra-observer variability. Also, the introduction of few parameters obtained from computer analysis did not solve the problem of a robust antenatal diagnosis. Hence, during the last decade, computer aided diagnosis systems, based on artificial intelligence (AI) machine learning techniques have been developed to assist medical decisions. The present work proposes a hybrid approach based on a neural architecture that receives heterogeneous data in input (a set of quantitative parameters and images) for classifying healthy and pathological fetuses. The quantitative regressors, which are known to represent different aspects of the correct development of the fetus, and thus are related to the fetal healthy status, are combined with features implicitly extracted from various representations of the FHR signal (images), in order to improve the classification performance. This is achieved by setting a neural model with two connected branches, consisting respectively of a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN). The neural architecture was trained on a huge and balanced set of clinical data (14.000 CTG tracings, 7000 healthy and 7000 pathological) recorded during ambulatory non stress tests at the University Hospital Federico II, Napoli, Italy. After hyperparameters tuning and training, the neural network proposed has reached an overall accuracy of 80.1%, which is a promising result, as it has been obtained on a huge dataset

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

Welche Aussage zum autonomen Reifungsstand erlaubt das konventionelle CTG im Vergleich zur fetalen Magnetkardiographie?

Mit der Analyse der fetalen Herzfrequenz (fHR) ist es möglich den fetalen Zustand und die Entwicklung des autonomen Nervensystems (ANS) einzuschätzen. Die fHR lässt sich mit verschiedenen Methoden nicht-invasiv erfassen. Die Einschätzung der autonomen Regulation erfolgt hierbei mittels Herzfrequenzvariabilitätsanalyse (HRV-Analyse). Es wurde eine Vielzahl von Parametern entwickelt, die verschiedene Aspekte der Herzfrequenzvariabilität (HRV) beschreiben können. Das Magnetkardiogramm (MKG) kann die einzelnen Herzschläge genau erfassen und schnelle Änderungen der fHR darstellen. Als elektrophysiologische Methode stellt es den Goldstandard zur fetalen HRV-Analyse dar, ist jedoch weltweit wenig etabliert, methodisch sehr aufwändig und damit klinisch nicht praktikabel. Das Kardiotokogramm (CTG) ist die technisch einfachere, in der Klinik etablierte Methode, geht aber mit einer methodisch bedingten, geringeren zeitlichen Auflösung der Herzschlagreihe einher. In dieser Arbeit soll die Vergleichbarkeit der HRV-Analyse in MKG und CTG geprüft werden, um zu ermitteln welches Potential dem CTG bei der Einschätzung der fetalen autonomen Funktion zukommt. Erwartet werden eine bedingte Vergleichbarkeit und in Teilen direkte Übertragbarkeit, insbesondere in Bezug auf globale und sympathisch dominierte Parameter der HRV. Es wurden intraindividuell bei 80 Patientinnen am selben Schwangerschaftstag aufeinanderfolgend fetale MKG- und CTG-Aufzeichnungen durchgeführt. Zur Vergleichbarkeit wurde der fetale Aktivitätszustand anhand der graphischen Darstellung des Herzfrequenzverlaufes ermittelt. Die Herzschlagreihen wurden aufbereitet und die HRV-Analyse semi-automatisch durchgeführt. Hierbei wurde neben den Ergebnissen von MKG und CTG eine dritte Signalkategorie erstellt, indem die Herzzeitreihe des MKG technisch auf die zeitliche Auflösung des CTG reduziert wurde („downsampling“). ..