Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors

Large deviations estimates for the multiscale analysis of heart rate variability

International audienceIn the realm of multiscale signal analysis, multifractal analysis provides with a natural and rich framework to measure the roughness of a time series. As such, it has drawn special attention of both mathematicians and practitioners, and led them to characterize relevant physiological factors impacting the heart rate variability. Notwithstanding these considerable progresses, multifractal analysis almost exclusively developed around the concept of Legendre singularity spectrum, for which efficient and elaborate estimators exist, but which are structurally blind to subtle features like non-concavity or, to a certain extent, non scaling of the distributions. Large deviations theory allows bypassing these limitations but it is only very recently that performing estimators were proposed to reliably compute the corresponding large deviations singularity spectrum. In this article, we illustrate the relevance of this approach, on both theoretical objects and on human heart rate signals from the Physionet public database. As conjectured, we verify that large deviations principles reveal significant information that otherwise remains hidden with classical approaches, and which can be reminiscent of some physiological characteristics. In particular we quantify the presence/absence of scale invariance of RR signals

Innovative Processing Algorithms for Fetal Magnetoencephalographic Data

Fetale Magnetenzepahalographie (fMEG) ermöglicht die Untersuchung der Entwicklung des zentralen und des autonomen Nervensystems bei Feten ab der 20. Schwangerschaftswoche. Wie normale Magnetenzephalographie bei Erwachsenen und Kindern ist auch fMEG eine nicht-invasive Methode und in der Anwendung vollkommen harmlos für Mutter und Kind. Die magnetischen Sensoren sind hierbei um das Abdomen der schwangeren Frau angeordnet. Die gute räumliche und zeitliche Auflösung erlaubt es, mütterliche und fetale Magnetokardiogramme gleichzeitig mit der fetalen Hirnaktivität zu messen. Die Signale der fetalen Magnetoenzephalographie werden vor allem zur Messung von auditiven und visuellen ereignisbezogenen Hirnreaktionen oder der spontanen Hirnaktivität verwendet. Wichtige Fragen zum Entwicklungsprozess des fetalen Gehirns und des autonomen Nervensystems sowie der mütterliche Einfluss auf den metabolischen und kognitiven Zustand des Neugeborenen können durch die Analyse der fetalen Magnetoenzephalographie-Signale geklärt werden. Die Auswertung der fetalen Hirnaktivität birgt einige Herausforderungen, da die Signale der fetalen und mütterlichen Herzaktivität etwa 10-1000 mal stärker sind als das fetale Hirnsignal. Daher ist es zwingend erforderlich, die Herzaktivität der Mutter und des Fetus zu erkennen und zu entfernen, bevor die fetale Hirnaktivität analysiert wird. Die derzeit verwendeten Methoden für die Erkennung und Entfernung der Herzaktivität funktionieren für die meisten Datensätze zuverlässig, die Verarbeitung enthält jedoch einige manuelle Schritte, was das Ganze sehr zeitaufwändig macht. Darüber hinaus ist die Signal Redistribution beim Entfernen der Herzaktivität ein bekanntes Problem, welches es schwierig macht, die Hirnaktivität später zu identifizieren. Das Ziel dieser Arbeit war es, die Auswertung der fMEG Daten schneller, besser und trotzdem leicht handhabbar zu machen. In dieser Arbeit werden zwei neue vollautomatisierte Methoden zur Erkennung und Entfernung der Herzaktivität vorgestellt. Der vollautomatisierte R-Peak Erkennungsalgorithmus (FLORA) verbessert die R-Peak Erkennung, indem er die Vorteile der zuvor verwendeten Methoden kombiniert und erweitert. Der Algorithmus zur vollautomatisierten Subtraktion der Herzaktivität (FAUNA) verbessert die Signalqualität und vereinfacht die Erkennung der Hirnaktivität, ohne Redistribution. Die Zuverlässigkeit der Daten wird dadurch erhöht, da keine manuelle Auswahl getroffen werden muss. Die Kombination beider Methoden in einem Programm zur vollautomatisierten Verarbeitung für die fetale Magnetoenzephalographie (FAIRY) macht die Datenauswertung nun einfach und schnell. Damit wird die fMEG Datenverarbeitung auf die "Big Data"- und "Automated Science"-Ära vorbereitet. Des Weiteren wurde eine Studie über die autonome und zentralnervöse Reaktion von Feten und Neugeborenen auf die mütterliche Stimme (AURORA) mit den neuen Datenverarbeitungsmethoden durchgeführt. Die Ergebnisse zeigten eine reduzierte Bewegung der Feten zwischen der 26. und 32. Schwangerschaftswoche und eine niedrigere Herzfrequenz während der ersten 20 Sekunden der Stimulation in den letzten Schwangerschaftswochen, als Reaktion auf die mütterliche Stimme. Zusätzlich fanden wir eine höhere Amplitude der Gehirnreaktion als Reaktion auf eine fremde Frauenstimme bei Neugeborenen.Fetal magnetoencephalography (fMEG) facilitates the investigation of both the nature and development of the fetal central and autonomic nervous system, starting at 20 weeks of gestational age. Like magnetoencephalography in children and adults, fetal magnetoencephalography is a noninvasive method and therefore completely harmless for both the mother and the child. Magnetic sensors in fMEG devices are arranged around the abdomen of the pregnant woman. The good spatial and temporal resolution allows to measure maternal and fetal magnetocardiograms simultaneously with fetal brain activity. The fMEG signals are mainly used to measure the auditory and visual event-related brain responses or the spontaneous brain activity. Important questions concerning the developmental process of the fetal brain, as well as the maternal influence on the metabolic and cognitive state of the newborn, can be clarified by the analysis of fMEG signals. The evaluation of the fetal brain activity poses some challenges, as the signals of fetal and maternal heart activity are 10-1000 times stronger than the fetal brain signal. Therefore, it is mandatory to detect and remove the heart activity of both the mother and the fetus before analyzing the fetal brain activity. The currently used methods for this detection and removal work well for most datasets, but the processing includes numerous manual steps and is therefore very time consuming. Furthermore, signal redistribution is a problem with the current methods, which makes later detection of the fetal brain activity challenging. The aim of this work was to make the evaluation of fMEG data faster, better and nevertheless, easy to use. In this thesis two new fully-automated procedures for the detection and removal of the heart activity are presented. The fully automated R-peak detection algorithm (FLORA) improves R-peak detection by combining and extending the advantages of the previously used methods. The algorithm for the fully automated subtraction of heart activity (FAUNA) improves the signal quality and facilitates detection of brain activity without the problem of redistribution. Furthermore these methods lead to a higher reliability of the data analysis since no manual interventions are necessary. Combining both methods in a tool for fully automated processing for fetal magnetoencephalography (FAIRY) makes data evaluation now easy and fast. This prepares the processing of fMEG data for the era of "Big Data" and "Automated Science". Additionally a study about the fetal and neonatal autonomous and central nervous response to maternal voice (AURORA) was performed using the new data processing methods. The results showed a reduced movement of fetuses between 26 and 32 weeks of pregnancy and a lower heart rate during the fist 20 seconds of stimulation in the last weeks of pregnancy as a reaction to maternal voice. We additionally found a higher amplitude of the brain response to voice onset of a stranger female voice in newborns

Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers

Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings

Efficient and accurate detection of subtle motion generated from small objects in noisy environments, as needed for vital sign monitoring, is challenging, but can be substantially improved with magnification. We developed a complex Gabor filter-based decomposition method to amplify phases at different spatial wavelength levels to magnify motion and extract 1D motion signals for fundamental frequency estimation. The phase-based complex Gabor filter outputs are processed and then used to train machine learning models that predict respiration and heart rate with greater accuracy. We show that our proposed technique performs better than the conventional temporal FFT-based method in clinical settings, such as sleep laboratories and emergency departments, as well for a variety of human postures.Comment: Accepted in IEEE Sensors 202

Investigation of the quality of umbilical artery Doppler waveforms

In Doppler systems which automatically calculate the maximum frequency envelope and pulsatility index (PI) of umbilical artery Doppler waveforms there is the possibility of error in these parameters when the technical quality of the acquired waveform is low. Low quality waveforms may arise when there is an inappropriate set of physical parameters or when there are other sources of noise such as overlying vessels signals. In this thesis the effect of physical parameters on the envelope and on PI are investigated, and also methods for the detection of low quality waveforms are described and tested. A flow phantom which is able to produce realistic looking umbilical artery Doppler waveforms is described. This is based upon microcompruter control of a stepping motor / gear pump combination. The statistics of the Doppler spectra produced using artificial blood and human blood in the phantom are found to be identical. The effect of a number of physical parameters on the simulated umbilical artery waveforms produced using the phantom is investigated. The accuracy of estimation of the envelope and the PI is similar over a wide range of physical conditions. A suitable image processing algorithm for speckle reduction of Doppler waveforms is developed and tested using simulated waveforms from the phantom. Using the flow device it was found that both filtering of the envelope and also speckle suppression of the spectrum improved the accuracy of estimation of the envelope and of the PI. A number of quality indices based upon the degree of noise of the envelope are described. Using the flow device there is found to be a high correlation between the quality index values, and the errors in PI and errors in envelope estimation respectively. In a clinical trial the quality index values from umbilical arteries were compared with the waveform quality as assessed by a skilled observer. The clinical results show that quality indices are able to separate high and low quality waveforms when the indices are calculated from the unprocessed envelope, but not when calculated from the filtered envelop

Machine learning algorithms combining slope deceleration and fetal heart rate features to predict acidemia

Electronic fetal monitoring (EFM) is widely used in intrapartum care as the standard method for monitoring fetal well-being. Our objective was to employ machine learning algorithms to predict acidemia by analyzing specific features extracted from the fetal heart signal within a 30 min window, with a focus on the last deceleration occurring closest to delivery. To achieve this, we conducted a case–control study involving 502 infants born at Miguel Servet University Hospital in Spain, maintaining a 1:1 ratio between cases and controls. Neonatal acidemia was defined as a pH level below 7.10 in the umbilical arterial blood. We constructed logistic regression, classification trees, random forest, and neural network models by combining EFM features to predict acidemia. Model validation included assessments of discrimination, calibration, and clinical utility. Our findings revealed that the random forest model achieved the highest area under the receiver characteristic curve (AUC) of 0.971, but logistic regression had the best specificity, 0.879, for a sensitivity of 0.95. In terms of clinical utility, implementing a cutoff point of 31% in the logistic regression model would prevent unnecessary cesarean sections in 51% of cases while missing only 5% of acidotic cases. By combining the extracted variables from EFM recordings, we provide a practical tool to assist in avoiding unnecessary cesarean sections