1,356 research outputs found

    Estrazione non invasiva del segnale elettrocardiografico fetale da registrazioni con elettrodi posti sull’addome della gestante (Non-invasive extraction of the fetal electrocardiogram from abdominal recordings by positioning electrodes on the pregnant woman’s abdomen)

    Get PDF
    openIl cuore è il primo organo che si sviluppa nel feto, particolarmente nelle primissime settimane di gestazione. Rispetto al cuore adulto, quello fetale ha una fisiologia ed un’anatomia significativamente differenti, a causa della differente circolazione cardiovascolare. Il benessere fetale si valuta monitorando l’attività cardiaca mediante elettrocardiografia fetale (ECGf). L’ECGf invasivo (acquisito posizionando elettrodi allo scalpo fetale) è considerato il gold standard, ma l’invasività che lo caratterizza ne limita la sua applicabilità. Al contrario, l’uso clinico dell’ECGf non invasivo (acquisito posizionando elettrodi sull’addome della gestante) è limitato dalla scarsa qualità del segnale risultante. L’ECGf non invasivo si estrae da registrazioni addominali, che sono corrotte da differenti tipi di rumore, fra i quali l’interferenza primaria è rappresentata dall’ECG materno. Il Segmented-Beat Modulation Method (SBMM) è stato da me recentemente proposto come una nuova procedura di filtraggio basata sul calcolo del template del battito cardiaco. SBMM fornisce una stima ripulita dell’ECG estratto da registrazioni rumorose, preservando la fisiologica variabilità ECG del segnale originale. Questa caratteristica è ottenuta grazie alla segmentazione di ogni battito cardiaco per indentificare i segmenti QRS e TUP, seguito dal processo di modulazione/demodulazione (che include strecciamento e compressione) del segmento TUP, per aggiustarlo in modo adattativo alla morfologia e alla durata di ogni battito originario. Dapprima applicato all’ECG adulto al fine di dimostrare la sua robustezza al rumore, l’SBMM è stato poi applicato al caso fetale. Particolarmente significativi sono i risultati relativi alle applicazioni su ECGf non invasivo, dove l’SBMM fornisce segnali caratterizzati da un rapporto segnale-rumore comparabile a quello caratterizzante l’ECGf invasivo. Tuttavia, l’SBMM può contribuire alla diffusione dell’ECGf non invasiva nella pratica clinica.The heart is the first organ that develops in the fetus, particularly in the very early stages of pregnancy. Compared to the adult heart, the physiology and anatomy of the fetal heart exhibit some significant differences. These differences originate from the fact that the fetal cardiovascular circulation is different from the adult circulation. Fetal well-being evaluation may be accomplished by monitoring cardiac activity through fetal electrocardiography (fECG). Invasive fECG (acquired through scalp electrodes) is the gold standard but its invasiveness limits its clinical applicability. Instead, clinical use of non-invasive fECG (acquired through abdominal electrodes) has so far been limited by its poor signal quality. Non-invasive fECG is extracted from the abdominal recording and is corrupted by different kind of noise, among which maternal ECG is the main interference. The Segmented-Beat Modulation Method (SBMM) was recently proposed by myself as a new template-based filtering procedure able to provide a clean ECG estimation from a noisy recording by preserving physiological ECG variability of the original signal. The former feature is achieved thanks to a segmentation procedure applied to each cardiac beat in order to identify the QRS and TUP segments, followed by a modulation/demodulation process (involving stretching and compression) of the TUP segments to adaptively adjust each estimated cardiac beat to the original beat morphology and duration. SBMM was first applied to adult ECG applications, in order to demonstrate its robustness to noise, and then to fECG applications. Particularly significant are the results relative to the non-invasive applications, where SBMM provided fECG signals characterized by a signal-to-noise ratio comparable to that characterizing invasive fECG. Thus, SBMM may contribute to the spread of this noninvasive fECG technique in the clinical practice.INGEGNERIA DELL'INFORMAZIONEAgostinelli, AngelaAgostinelli, Angel

    A comparative study of ECG-derived respiration in ambulatory monitoring using the single-lead ECG

    Get PDF
    Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems

    Non-invasive techniques for respiratory information extraction based on pulse photoplethysmogram and electrocardiogram

    Get PDF
    El objetivo principal de esta tesis es el desarrollo de métodos no invasivos para la extracción de información respiratoria a partir de dos señales biomédicas ampliamente utilizadas en la rutina clínica: el electrocardiograma (ECG) y la señal fotopletismográfica de pulso (PPG). La motivación de este estudio es la conveniencia de monitorizar información respiratoria a partir de dispositivos no invasivos que permita sustituir las técnicas actuales que podrían interferir con la respiración natural y que presentan inconvenientes en algunas aplicaciones como la prueba de esfuerzo y los estudios del sueño. Además, si estos dispositivos no invasivos son los ya utilizados en la rutina clínica, la información respiratoria extraída de ellos representa un valor añadido que permite tener una visión más completa del paciente. DESARROLLO TEÓRICO Esta tesis se divide en 6 capítulos. El Capítulo 1 introduce la problemática, motivaciones y objetivos del estudio. También introduce el origen fisiológico de las señales estudiadas ECG y PPG, y cómo y por qué tienen información autonómica y respiratoria que se puede extraer de ellas. El Capítulo 2 aborda la obtención de información respiratoria a partir del ECG. Se han propuesto varios métodos para la obtención de la respiración a partir del ECG (EDR, del inglés ¿ECG derived respiration?). Su rendimiento se suele ver muy afectado en entornos altamente no estacionarios y ruidosos como la prueba de esfuerzo. No obstante, se han propuesto algunas alternativas, como una basada en el ángulo de rotación del eje eléctrico (obtenido del ECG), que es el que mejor funciona en prueba de esfuerzo según nuestros conocimientos. Este método requiere de tres derivaciones ortogonales y es muy dependiente de cada una de ellas, i.e., el método no es aplicable o su rendimiento se reduce significativamente si hay algún problema en alguna de las derivaciones requeridas. En el Capítulo 2 se propone un método EDR nuevo basado en las pendientes del QRS y el ángulo de la onda R. El Capítulo 3 aborda a obtención de información respiratoria a partir de la señal PPG. Se propone un método nuevo para obtener la tasa respiratoria a partir de la señal PPG. Explota una modulación respiratoria en la variabilidad de anchura de pulso (PWV) relacionada con la velocidad y dispersión de la onda de pulso. El Capítulo 4 aborda la extracción de información respiratoria a partir de señales PPG registradas con smarthpones (SCPPG), mediante la adaptación de los métodos basados en la señal PPG presentados en el Capítulo 3. En el Capítulo 5 se propone un método para el diagnóstico del síndrome de apnea obstructiva del sueño (OSAS) en niños basado únicamente en la señal PPG. El OSAS es una disfunción relacionada con la respiración y el sueño que se diagnostica mediante polisomnografía (PSG). La PSG es el registro nocturno de muchas señales durante el sueño, siendo muy difícil de aplicar en entornos ambulatorios. El método que presenta esta tesis está enfocado a diagnosticar el OSAS en niños utilizando únicamente la señal PPG que permitiría considerar un diagnóstico ambulatorio con sus ventajas económicas y sociales. Finalmente, el Capítulo 6 resume las contribuciones originales y las conclusiones principales de esta tesis, y propone posibles extensiones del trabajo. CONCLUSIÓN El método presentado en el Capítulo 2 para estimar la tasa respiratoria a partir de las pendientes del complejo QRS y el ángulo de la onda R en el ECG demostró ser robusto en entornos altamente no estacionarios y ruidosos y por tanto ser aplicable durante ejercicio incluyendo entrenamiento deportivo. Además, es independiente de un conjunto específico de derivaciones y, por tanto, un problema en alguna de ellas no implica una reducción considerable del rendimiento. El método presentado en el Capítulo 3 para estimar la tasa respiratoria a partir de la PWV extraída de la señal PPG está mucho menos afectada por el tono simpático que otros métodos presentados en la literatura que suelen basarse en la amplitud y/o la tasa de pulso. Esto permite una mayor precisión que otros métodos basados en PPG. Además, se propone un método para combinar información de diferentes señales respiratorias, y se utiliza para estimar la tasa respiratoria a partir de la PWV en combinación con otros métodos basados en la señal PPG, mejorando la precisión de la estimación incluso en comparación con otros métodos en la literatura que requieren el ECG o la presión sanguínea. Los métodos propuestos en el Capítulo 4 para estimar la tasa respiratoria mediante señales SCPPG estimaron de forma precisa la tasa respiratoria en sus rangos espontáneos habituales (0.2-0.4 Hz) e incluso a tasas más altas (hasta 0.5 Hz o 0.6 Hz, dependiendo del dispositivo utilizado). El único requerimiento es que el smartphone tenga un luz tipo flash y una cámara para grabar una yema del dedo sobre ella. La popularidad de los smartphones los convierte en dispositivos de acceso y aceptación r¿apidos. Así, para la población general es potencialmente aceptable un método que funciona en smartphones, pudiendo facilitar la medida de algunas constantes vitales utilizando solo la yema del dedo. El método presentado en el Capítulo 5 para el diagnóstico del OSAS en niños a partir de la PPG obtuvo una precisión suficiente para la clínica, aunque antes de ser aplicado en dicho entorno, el método debería ser validado en una base de datos más grande.The main objective of this thesis is to develop non-invasive methods for respiration information extraction from two biomedical signals which are widely adopted in clinical routine: the electrocardiogram (ECG) and the pulse photoplethysmographic (PPG) signal. This study is motivated by the desirability of monitoring respiratory information from non-invasive devices allowing to substitute the current respiration-monitoring techniques which may interfere with natural breathing and which are unmanageable in some applications such as stress test or sleep studies. Furthermore, if these noninvasive devices are those already used in the clinical routine, the respiratory information obtained from them represents an added value which allows a more complete overview of the patient status. This thesis is divided into 6 chapters. Chapter 1 of this thesis introduces the problematic, motivations and objectives of this study. It also introduces the physiological origin of studied ECG and PPG signals, and why and how they carry autonomic- and respiration-related information which can be extracted from them. Chapter 2 of this thesis addresses the derivation of respiratory information from ECG signal. Several ECG derived respiration (EDR) methods have been presented in literature. Their performance usually decrease considerably in highly non-stationary and noisy environments such as stress test. However, some alternatives aimed to this kind of environments have been presented, such as one based on electrical axis rotation angles (obtained from the ECG), which to the best of our knowledge was the best suited for stress test. This method requires three orthogonal leads, and it is very dependent on each one of those leads, i.e., the performance of the method is significantly decreased if there is any problem at any one of the required leads. A novel EDR method based on QRS slopes and R-wave angle is presented in this thesis. The proposed method demonstrated to be robust in highly non-stationary and noisy environments and so to be applicable to exercise conditions including sports training. Furthermore, it is independent on a specific lead set, and so, a problem at any lead do not imply a significantly reduction of the performance. Chapter 3 addresses the derivation of respiratory information from PPG signals. A novel method for deriving respiratory rate from PPG signal is presented. It exploits respiration-related modulations in pulse width variability (PWV) which is related to pulse wave velocity and dispersion. The proposed method is much less affected by the sympathetic tone than other methods in literature which are usually based on pulses amplitude and/or rate. This leads to highest accuracy than other PPG-based method. Furthermore, a method for combining information from several respiratory signals was developed and used to obtain a respiratory rate estimation from the proposed PWV-based in combination with other known PPG-based methods, improving the accuracy of the estimation and outperforming other methods in literature which involve ECG or BP recording. Chapter 4 addresses the derivation of respiratory information from smartphone- camera-acquired-PPG (SCPPG) signals by adapting the methods for deriving respiratory rate from PPG signal presented in Chapter 3. The proposed method accurately estimates respiratory rate from SCPPG signals at its normal spontaneous ranges (0.2-0.4 Hz) and even at higher rates (up to 0.5 Hz or 0.6 Hz, depending on the used device). The only requirement is that these smartphones and tablets contain a flashlight and a video camera to image a fingertip pressed to it. As smartphones and tablets have become common, they meet the criteria of ready access and acceptance. Hence, a mobile phone/tablet approach has the potential to be widely-accepted by the general population and can facilitate the capability to measure some of the vital signs using only fingertip of the subject. Chapter 5 of this thesis proposes a methodology for obstructive sleep apnea syndrome (OSAS) screening in children just based on PPG signal. OSAS is a sleep-respiration-related dysfunction for which polysomnography (PSG) is the gold standard for diagnosis. PSG consists of overnight recording of many signals during sleep, therefore, it is quite involved and difficult to use in ambulatory scenario. The method presented in this thesis is aimed to diagnose the OSAS in children based just on PPG signal which would allow us to consider an ambulatory diagnosis with both its social and economic advantages. Finally, Chapter 6 summarizes the original contributions and main conclusions of the thesis, and proposes possible extensions of the work

    Detection and Processing Techniques of FECG Signal for Fetal Monitoring

    Get PDF
    Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system

    Quality Assessment of Ambulatory Electrocardiogram Signals by Noise Detection using Optimal Binary Classification

    Get PDF
    In order to improve the diagnostic capability in Ambulatory Electrocardiogram signal and to reduce the noise signal impacts, there is a need for more robust models in place. In terms of improvising to the existing solutions, this article explores a novel binary classifier that learns from the features optimized by fusion of diversity assessment measures, which performs Quality Assessment of Ambulatory Electrocardiogram Signals (QAAES) by Noise Detection. The performance of the proposed model QAAES has been scaled by comparing it with contemporary models. Concerning performance analysis, the 10-fold cross-validation has been carried on a benchmark dataset. The results obtained from experiments carried on proposed and other contemporary models for cross-validation metrics have been compared to signify the sensitivity, specificity, and noise detection accuracy

    Novel Approaches to Pervasive and Remote Sensing in Cardiovascular Disease Assessment

    Get PDF
    Cardiovascular diseases (CVDs) are the leading cause of death worldwide, responsible for 45% of all deaths. Nevertheless, their mortality is decreasing in the last decade due to better prevention, diagnosis, and treatment resources. An important medical instrument for the latter processes is the Electrocardiogram (ECG). The ECG is a versatile technique used worldwide for its ease of use, low cost, and accessibility, having evolved from devices that filled up a room, to small patches or wrist- worn devices. Such evolution allowed for more pervasive and near-continuous recordings. The analysis of an ECG allows for studying the functioning of other physiological systems of the body. One such is the Autonomic Nervous System (ANS), responsible for controlling key bodily functions. The ANS can be studied by analyzing the characteristic inter-beat variations, known as Heart Rate Variability (HRV). Leveraging this relation, a pilot study was developed, where HRV was used to quantify the contribution of the ANS in modulating cardioprotection offered by an experimental medical procedure called Remote Ischemic Conditioning (RIC), offering a more objective perspective. To record an ECG, electrodes are responsible for converting the ion-propagated action potential to electrons, needed to record it. They are produced from different materials, including metal, carbon-based, or polymers. Also, they can be divided into wet (if an elec- trolyte gel is used) or dry (if no added electrolyte is used). Electrodes can be positioned either inside the body (in-the-person), attached to the skin (on-the-body), or embedded in daily life objects (off-the-person), with the latter allowing for more pervasive recordings. To this effect, a novel mobile acquisition device for recording ECG rhythm strips was developed, where polymer-based embedded electrodes are used to record ECG signals similar to a medical-grade device. One drawback of off-the-person solutions is the increased noise, mainly caused by the intermittent contact with the recording surfaces. A new signal quality metric was developed based on delayed phase mapping, a technique that maps time series to a two-dimensional space, which is then used to classify a segment into good or noisy. Two different approaches were developed, one using a popular image descriptor, the Hu image moments; and the other using a Convolutional Neural Network, both with promising results for their usage as signal quality index classifiers.As doenças cardiovasculares (DCVs) são a principal causa de morte no mundo, res- ponsáveis por 45% de todas estas. No entanto, a sua mortalidade tem vindo a diminuir na última década, devido a melhores recursos na prevenção, diagnóstico e tratamento. Um instrumento médico importante para estes recursos é o Eletrocardiograma (ECG). O ECG é uma técnica versátil utilizada em todo o mundo pela sua facilidade de uso, baixo custo e acessibilidade, tendo evoluído de dispositivos que ocupavam uma sala inteira para pequenos adesivos ou dispositivos de pulso. Tal evolução permitiu aquisições mais pervasivas e quase contínuas. A análise de um ECG permite estudar o funcionamento de outros sistemas fisiológi- cos do corpo. Um deles é o Sistema Nervoso Autônomo (SNA), responsável por controlar as principais funções corporais. O SNA pode ser estudado analisando as variações inter- batidas, conhecidas como Variabilidade da Frequência Cardíaca (VFC). Aproveitando essa relação, foi desenvolvido um estudo piloto, onde a VFC foi utilizada para quantificar a contribuição do SNA na modulação da cardioproteção oferecida por um procedimento mé- dico experimental, denominado Condicionamento Isquêmico Remoto (CIR), oferecendo uma perspectiva mais objetiva. Na aquisição de um ECG, os elétrodos são os responsáveis por converter o potencial de ação propagado por iões em eletrões, necessários para a sua recolha. Estes podem ser produzidos a partir de diferentes materiais, incluindo metal, à base de carbono ou polímeros. Além disso, os elétrodos podem ser classificados em húmidos (se for usado um gel eletrolítico) ou secos (se não for usado um eletrólito adicional). Os elétrodos podem ser posicionados dentro do corpo (dentro-da-pessoa), colocados em contacto com a pele (na-pessoa) ou embutidos em objetos da vida quotidiana (fora-da-pessoa), sendo que este último permite gravações mais pervasivas . Para este efeito, foi desenvolvido um novo dispositivo de aquisição móvel para gravar sinal de ECG, onde elétrodos embutidos à base de polímeros são usados para recolher sinais de ECG semelhantes a um dispositivo de grau médico. Uma desvantagem das soluções onde os elétrodos estão embutidos é o aumento do ruído, causado principalmente pelo contato intermitente com as superfícies de aquisição. Uma nova métrica de qualidade de sinal foi desenvolvida com base no mapeamento de fase atrasada, uma técnica que mapeia séries temporais para um espaço bidimensional, que é então usado para classificar um segmento em bom ou ruidoso. Duas abordagens diferentes foram desenvolvidas, uma usando um popular descritor de imagem, e outra utilizando uma Rede Neural Convolucional, com resultados promissores para o seu uso como classificadores de qualidade de sinal

    Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants.

    Get PDF
    OBJECTIVE: Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. APPROACH: Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. MAIN RESULTS: Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of  <250 Hz and  <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender. SIGNIFICANCE: Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available

    Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

    Get PDF
    This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature
    corecore