The human ECG - nonlinear deterministic versus stochastic aspects

We discuss aspects of randomness and of determinism in electrocardiographic signals. In particular, we take a critical look at attempts to apply methods of nonlinear time series analysis derived from the theory of deterministic dynamical systems. We will argue that deterministic chaos is not a likely explanation for the short time variablity of the inter-beat interval times, except for certain pathologies. Conversely, densely sampled full ECG recordings possess properties typical of deterministic signals. In the latter case, methods of deterministic nonlinear time series analysis can yield new insights.Comment: 6 pages, 9 PS figure

First evidence that intrinsic fetal heart rate variability exists and is affected by hypoxic pregnancy.

KEY POINTS: We introduce a technique to test whether intrinsic fetal heart rate variability (iFHRV) exists and we show the utility of the technique by testing the hypothesis that iFHRV is affected by chronic fetal hypoxia, one of the most common adverse outcomes of human pregnancy complicated by fetal growth restriction. Using an established late gestation ovine model of fetal development under chronic hypoxic conditions, we identify iFHRV in isolated fetal hearts and show that it is markedly affected by hypoxic pregnancy. Therefore, the isolated fetal heart has intrinsic variability and carries a memory of adverse intrauterine conditions experienced during the last third of pregnancy. ABSTRACT: Fetal heart rate variability (FHRV) emerges from influences of the autonomic nervous system, fetal body and breathing movements, and from baroreflex and circadian processes. We tested whether intrinsic heart rate variability (iHRV), devoid of any external influences, exists in the fetal period and whether it is affected by chronic fetal hypoxia. Chronically catheterized ewes carrying male singleton fetuses were exposed to normoxia (n = 6) or hypoxia (10% inspired O2 , n = 9) for the last third of gestation (105-138 days of gestation (dG); term ∼145 dG) in isobaric chambers. At 138 dG, isolated hearts were studied using a Langendorff preparation. We calculated basal intrinsic FHRV (iFHRV) indices reflecting iFHRV's variability, predictability, temporal symmetry, fractality and chaotic behaviour, from the systolic peaks within 15 min segments in each heart. Significance was assumed at P < 0.05. Hearts of fetuses isolated from hypoxic pregnancy showed approximately 4-fold increases in the Grid transformation as well as the AND similarity index (sgridAND) and a 4-fold reduction in the scale-dependent Lyapunov exponent slope. We also detected a 2-fold reduction in the Recurrence quantification analysis, percentage of laminarity (pL) and recurrences, maximum and average diagonal line (dlmax, dlmean) and the Multiscale time irreversibility asymmetry index. The iHRV measures dlmax, dlmean, pL and sgridAND correlated with left ventricular end-diastolic pressure across both groups (average R2  = 0.38 ± 0.03). This is the first evidence that iHRV originates in fetal life and that chronic fetal hypoxia significantly alters it. Isolated fetal hearts from hypoxic pregnancy exhibit a time scale-dependent higher complexity in iFHRV.British Heart Foundatio

Applications of Signal Analysis to Atrial Fibrillation

This work was supported by projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 from Junta de Comunidades de Castilla-La ManchaRieta Ibañez, JJ.; Alcaraz Martínez, R. (2013). Applications of Signal Analysis to Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 155-180. https://doi.org/10.5772/5340915518

Interdisciplinary application of nonlinear time series methods

This paper reports on the application to field measurements of time series methods developed on the basis of the theory of deterministic chaos. The major difficulties are pointed out that arise when the data cannot be assumed to be purely deterministic and the potential that remains in this situation is discussed. For signals with weakly nonlinear structure, the presence of nonlinearity in a general sense has to be inferred statistically. The paper reviews the relevant methods and discusses the implications for deterministic modeling. Most field measurements yield nonstationary time series, which poses a severe problem for their analysis. Recent progress in the detection and understanding of nonstationarity is reported. If a clear signature of approximate determinism is found, the notions of phase space, attractors, invariant manifolds etc. provide a convenient framework for time series analysis. Although the results have to be interpreted with great care, superior performance can be achieved for typical signal processing tasks. In particular, prediction and filtering of signals are discussed, as well as the classification of system states by means of time series recordings.Comment: 86 pages, 26 figure

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

Intelligent Pattern Analysis of the Foetal Electrocardiogram

The aim of the project on which this thesis is based is to develop reliable techniques for foetal electrocardiogram (ECG) based monitoring, to reduce incidents of unnecessary medical intervention and foetal injury during labour. World-wide electronic foetal monitoring is based almost entirely on the cardiotocogram (CTG), which is a continuous display of the foetal heart rate (FHR) pattern together with the contraction of the womb. Despite the widespread use of the CTG, there is no significant improvement in foetal outcome. In the UK alone it is estimated that birth related negligence claims cost the health authorities over £400M per-annum. An expert system, known as INFANT, has recently been developed to assist CTG interpretation. However, the CTG alone does not always provide all the information required to improve the outcome of labour. The widespread use of ECG analysis has been hindered by the difficulties with poor signal quality and the difficulties in applying the specialised knowledge required for interpreting ECG patterns, in association with other events in labour, in an objective way. A fundamental investigation and development of optimal signal enhancement techniques that maximise the available information in the ECG signal, along with different techniques for detecting individual waveforms from poor quality signals, has been carried out. To automate the visual interpretation of the ECG waveform, novel techniques have been developed that allow reliable extraction of key features and hence allow a detailed ECG waveform analysis. Fuzzy logic is used to automatically classify the ECG waveform shape using these features by using knowledge that was elicited from expert sources and derived from example data. This allows the subtle changes in the ECG waveform to be automatically detected in relation to other events in labour, and thus improve the clinicians position for making an accurate diagnosis. To ensure the interpretation is based on reliable information and takes place in the proper context, a new and sensitive index for assessing the quality of the ECG has been developed. New techniques to capture, for the first time in machine form, the clinical expertise / guidelines for electronic foetal monitoring have been developed based on fuzzy logic and finite state machines, The software model provides a flexible framework to further develop and optimise rules for ECG pattern analysis. The signal enhancement, QRS detection and pattern recognition of important ECG waveform shapes have had extensive testing and results are presented. Results show that no significant loss of information is incurred as a result of the signal enhancement and feature extraction techniques

Uterine contractions clustering based on surface electromyography: an input for pregnancy monitoring

Tese de mestrado em Bioestatística, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, em 2018Inicialmente a investigação da contratilidade uterina recorria à utilização de dois métodos: o tocograma externo e o cateter de pressão intrauterino. Ambos os métodos apresentam limitações ao nível da avaliação do risco de parto prematuro e na monitorização da gravidez. O EHG (Electrohisterograma) é um método alternativo ao tocograma externo e ao cateter de pressão intrauterino. Este método pode ser aplicado de forma invasiva no músculo uterino, ou de forma não invasiva através de elétrodos colocados no abdómen. O EHG tem sido considerado uma ferramenta adequada para a monitorização da gravidez e do parto. O índice de massa corporal tem um impacto quase impercetível no EHG, sendo esta uma das principais características deste método. O EHG pode também ser utilizado para identificar as mulheres que vão entrar em trabalho de parto e ainda auxiliar na tomada de decisão médica quanto à utilização da terapia tocolítica (antagonista da oxitocina), evitando deste modo a ingestão de medicação desnecessária e os consequentes efeitos secundários. Na literatura existem apenas cinco casos publicados em que foi realizada uma separação dos principais eventos do sinal EHG: contrações, movimentos fetais, ondas Alvarez e ondas LDBF (Longue Durée Basse Fréquence). Em três das publicações a separação dos eventos foi feita manualmente e nos restantes casos algoritmos, como redes neuronais, foram aplicados ao EHG. As ondas Alvarez e as Braxton-Hicks são as mais reconhecidas. As ondas Alvarez foram descritas pela primeira vez nos anos cinquenta e as Braxton-Hicks foram descritas pela primeira vez em 1872 sendo detetadas através de palpação. As ondas Alvarez são ocasionalmente sentidas pela mulher. Estas ondas estão localizadas numa pequena área do tecido uterino sem propagação e podem levar a contrações com maior intensidade e, consequentemente, ao parto pré-termo. As Braxton-Hicks são contrações ineficientes registadas a partir da 20ª semana de gravidez que se tornam mais frequentes e intensas com o decorrer da gravidez. Estas contrações são menos localizadas que as ondas Alvarez e, durante o parto, propagam-se por todo o tecido uterino num curto período de tempo. As Braxton-Hicks estão associadas a uma diminuição do ritmo cardíaco fetal. As ondas LDBF são contrações de longa duração associadas a hipertonia uterina, quando há contração do tecido uterino sem retorno ao relaxamento muscular, o que representa um risco na gravidez. Neste trabalho foram utilizadas duas bases de dados. Na base de dados da Islândia existem 122 registos de 45 mulheres, dos quais apenas 4 correspondem a partos pré-termo. Na base de dados TPEHG (Term-Preterm EHG) existem 300 registos, dos quais 38 correspondem a partos pré-termo. Neste trabalho foram escolhidos canais bipolares, visto que estes reduzem o ruído idêntico, como o ECG (Eletrocardiograma) materno ou movimentos respiratórios. Para ambas as bases de dados os sinais originais de EHG foram processados e filtrados. Na estimação espetral foram considerados dois métodos: paramétricos e não paramétricos. O método Welch foi escolhido pois representa um bom compromisso entre ambos. Este método foi utilizado para calcular o espectro de cada evento detetado no sinal EHG. Para detetar os eventos no sinal EHG foram considerados cinco métodos baseados na energia ou amplitude. O método Wavelet foi o escolhido pois após uma inspeção visual, este era o método que delineava melhor as contrações. Na base de dados da Islândia foram identificadas 3136 contrações e na TPEHG foram encontradas 4622 contrações. O objetivo principal desta tese é obter clusters de contrações detetadas no sinal EHG. No entanto, as contrações são séries temporais não estacionárias, e a sua classificação visual é inviável a longo termo e também difícil de aplicar na prática clínica. Existem vários parâmetros que podem ser extraídos do sinal EHG, mas o espectro das contrações foi o método escolhido visto que este representa o sinal EHG e tem sempre a mesma dimensão, independentemente da duração da contração. As distâncias espetrais têm sido utilizadas com sucesso no reconhecimento áudio. Neste trabalho foi realizada uma aplicação desse método ao processamento do EHG, no qual foram realizados os ajustes necessários. Para comparar os espectros foram estudadas 8 distâncias diferentes: Itakura-Saito, COSH, Itakura, Itakura simétrica, Kullback-Leibler, Jeffrey, Rényi e Jensen-Rényi. Apenas as distâncias simétricas foram selecionadas para um estudo mais detalhado visto que estas são, segundo a literatura, as distâncias mais adequadas aquando do clustering. Após comparação das distâncias simétricas, a divergência de Jeffrey foi a selecionada para a comparação dos espectros. Nesta tese foram avaliados três métodos diferentes de clustering: o linkage, o K-means e o K-medoids. O linkage é um método hierárquico. Os clusters que resultam do agrupamento hierárquico estão organizados numa estrutura chamada dendrograma. No agrupamento hierárquico, não é necessário predeterminar o número de clusters, o que torna este um método ideal na exploração dos dados. O K-means e o K-medoids são métodos de partição, nos quais os dados são separados em k clusters decididos previamente. Os clusters são definidos de forma a otimizar a função da distância. No algoritmo K-means, os clusters baseiam-se na proximidade entre si de acordo com uma distância predeterminada. A diferença entre o K-medoids e o K-means é que o K-medoids escolhe pontos de dados como centros, chamados de medoides, enquanto K-means usa centróides. Após uma comparação dos diferentes métodos de clustering foi escolhido neste trabalho foi o average linkage, visto que este apresentava melhores resultados quer na separação dos espectros quer na silhueta. É então apresentado um método inovador no qual se utiliza todo o espectro das contrações detetadas automaticamente no EHG para o clustering não supervisionado. Esta técnica é uma contribuição para a classificação automática das diferentes contrações, especialmente aquelas mais reconhecidas na literatura: Alvarez e Braxton-Hicks. Era expectável encontrar um cluster isolado com as ondas LDBF, visto que estas representam um risco para o feto. O principal objetivo era juntar num cluster os espectros semelhantes das contrações, e relacioná-lo com o respetivo tipo de contração. Essa tarefa foi concluída através da identificação positiva de Alvarez e Braxton-Hicks. O clustering forneceu ainda algumas pistas sobre ondas Alvarez que não foram encontradas com o algoritmo de deteção de contrações, situação para a qual um método alternativo é apresentado. É sugerido que as ondas Alvarez sejam detetadas com métodos baseados na frequência, como, por exemplo, a frequência instantânea, no entanto este método não foi desenvolvido neste trabalho. Em relação às ondas LDBF, estas foram encontradas no cluster das Braxton-Hicks. É sugerido que a deteção das ondas LDBF seja baseada na sua caraterística mais distinta: a longa duração. Verificou-se que os casos pré-termo e os registos pré-parto não ficaram isolados num cluster, não se tendo encontrado uma relação entre a idade gestacional e o tipo de contração. Conclui-se que as contrações mais curtas apresentam maior amplitude do que as contrações com maior duração. Baseado em estudos anteriores sobre a eletrofisiologia do útero, supõem-se que o início do trabalho de parto pré-termo e termo esteja associado a sequências específicas de diferentes tipos de contrações, nas quais as ondas Alvares desempenham um papel importante. As contrações identificadas como Alvarez e Braxton-Hicks não são usadas como tal na prática clínica apesar de a maioria das contrações detetadas pelo tocograma serem Braxton-Hicks. O interesse pelas ondas Alvarez diminuiu rapidamente visto que estas ondas são praticamente indetetáveis pelo método de referência de deteção de contrações: o tocograma. As capacidades e a resolução do EHG levaram à renovação do estudo das contrações mais subtis, incluindo as Alvarez. Este trabalho é uma contribuição para a investigação nesta área.An innovative technique is introduced wherein where an unsupervised clustering method using as feature the whole spectrum of automatically detected contractions on the EHG (Electrohysterogram) is presented as a contribution to the automatic classification of the different uterine contractions, at least those that have been most recognized in the literature: Alvarez and Braxton-Hicks. It was expected to also be able to cluster the LDBF (Longue Durée Basse Fréquence) components, as these pose a fetal risk. The main task was to have the spectral contractions descriptions clustered and linked to the respective contraction type. That task was completed with positive identification of the Alvarez and Braxton-Hicks. The clustering process also provided clues regarding the missed Alvarez waves in the contraction detection algorithm, for which an alternative technique is suggested but not developed in this work. Regarding the LDBF they were found in the Braxton-Hicks cluster. It is suggested the LDBF´s to be detected based in their most prominent feature: the long duration. It is presented the rationale behind the selection of a cost function to be used in the spectral distance’s algorithm. Spectral distances have been successfully used in audio recognition and this works represents an application to the EHG processing, for which the necessary adjustments have to be implemented. It was found that no single cluster pointed to the preterm cases, or indeed to the pre-labor subject recordings. It is hypothesized, based on previous studies in uterine electrophysiology, that the initiation of pre-term or term labor should be associated with triggering contraction sequences of different types, where the Alvarez waves play a major role. Alvarez and Braxton-Hicks, labeled as such, are not typically used in the clinical environment despite most of the Tocogram detected contractions being the latter. Alvarez waves are not usually detectable by the Tocogram. Alvarez were firstly detected invasively in the early fifties, and Braxton-Hicks in 1872 using routine palpation techniques. The interest in Alvarez components declined rapidly since being practically undetectable by the de facto reference in the contraction detection: the Tocogram. The EHG capabilities and resolution made it possible to revive the research on the most subtle uterine contractions, Alvarez included and this work is a contribution in this research area

Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis