196 research outputs found
Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring
Abnormal heart rhythms (arrhythmias) are a major cause of cardiovascular disease and death in Europe. Sudden cardiac death accounts for 50% of cardiac mortality in developed countries; ventricular tachycardia or ventricular fibrillation is the most common underlying arrhythmia. In the ambulatory population, atrial fibrillation is the most common arrhythmia and is associated with an increased risk of stroke and heart failure, particularly in an aging population. Early detection of arrhythmias allows appropriate intervention, reducing disability and death. However, in the early stages of disease arrhythmias may be transient, lasting only a few seconds, and are thus difficult to detect. This work addresses the problem of extracting the far-field heart electrogram signal from noise components, as recorded in bipolar leads along the left arm, using a data driven ECG (electrocardiogram) denoising algorithm based on ensemble empirical mode decomposition (EEMD) methods to enable continuous non-invasive monitoring of heart rhythm for long periods of time using a wrist or arm wearable device with advanced biopotential sensors. Performance assessment against a control denoising method of signal averaging (SA) was implemented in a pilot study with 34 clinical cases. EEMD was found to be a reliable, low latency, data-driven denoising technique with respect to the control SA method, achieving signal-to-noise ratio (SNR) enhancement to a standard closer to the SA control method, particularly on the upper arm-ECG bipolar leads. Furthermore, the SNR performance of the EEMD was improved when assisted with an FFT (fast Fourier transform ) thresholding algorithm (EEMD-fft)
Quantification of Ventricular Repolarization Dispersion Using Digital Processing of the Surface ECG
Digital processing of electrocardiographic records was one of the first applications of signal processing on medicine. There are many ways to analyze and study electrical cardiac activity using the surface electrocardiogram (ECG) and nowadays a good clinical diagnostic and prevention of cardiac risk are the principal goal to be achieved. One aim of digital processing of ECG signals has been quantification of ventricular repolarization dispersion (VRD), phenomenon which mainly is determined by heterogeneity of action potential durations (APD) in different myocardial regions. The APD differs not only between myocytes of apex and the base of both ventricles, but those of endocardial and epicardial surfaces (transmural dispersion) and between both ventricles. Also, it was demonstrated that several electrophysiologically and functionally different myocardial cells, like epicardial, endocardial and mid-myocardial M cells. The APD inequalities develop global and/or local voltage gradients that play an important role in the inscription of ECG T-wave morphology. In this way, we can assume that T-wave is a direct expression of ventricular repolarization inhomogeneities on surface ECG. Experimental and clinical studies have demonstrated a relationship between VRD and severe ventricular arrhythmias. In addition, patients having increased VRD values have a higher risk of developing reentrant arrhythmias. Frequently the heart answer to several pathological states produced an increase of VRD; this phenomenon may develop into malignant ventricular arrhythmia (MVA) and/or sudden cardiac death (SCD). Moreover, it has been showed that the underlying mechanisms in MVA and/or SCD are cardiac re-entry, increased automation, influence of autonomic nervous system and arrhythmogenic substrates linked with cardiac pathologies. These cardiac alterations could presented ischemia, hypothermia, electrolyte imbalance, long QT syndrome, autonomic system effects and others. Digital processing of ECG has been proved to be useful for cardiac risk assessment, with additional advantages like of being non invasive treatments and applicable to the general population. With the aim to identify high cardiac risk patients, the researchers have been tried to quantify the VRD with different parameters obtained by mathematic-computational processing of the surface ECG. These parameters are based in detecting changes of T-wave intervals and T-wave morphology during cardiac pathologies, linking these changes with VRD. In this chapter, we have presented a review of VRD indexes based on digital processing of ECG signals to quantify cardiac risk. The chapter is organized as follows: Section 2 explains ECG preprocessing and delineation of fiducial points. In Section 3, indexes of VRD quantification, such as: QT interval dispersion, QT interval variability and T-wave duration, are described. In Section 4, different repolarization indexes describing T-wave morphology and energy are examined, including complexity of repolarization, T-wave residuum, angle between the depolarization and repolarization dominant vectors, micro T-wave alternans, T-wave area and amplitude and T-wave spectral variability. Finally, in Section 5 conclusions are presented.Fil: Vinzio Maggio, Ana Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Bonomini, Maria Paula. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería; ArgentinaFil: Arini, Pedro David. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentin
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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
Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition
Title on author’s file: Classification of mechanomyogram signal using wavelet packet transform and singular value decomposition for multifunction prosthesis control2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Classification of EMG signals to control a prosthetic hand using time-frequesncy representations and Support Vector Machines
Myoelectric signals (MES) are viable control signals for externally-powered prosthetic devices. They may improve both the functionality and the cosmetic appearance of these devices. Conventional controllers, based on the signal\u27s amplitude features in the control strategy, lack a large number of controllable states because signals from independent muscles are required for each degree of freedom (DoF) of the device. Myoelectric pattern recognition systems can overcome this problem by discriminating different residual muscle movements instead of contraction levels of individual muscles. However, the lack of long-term robustness in these systems and the design of counter-intuitive control/command interfaces have resulted in low clinical acceptance levels. As a result, the development of robust, easy to use myoelectric pattern recognition-based control systems is the main challenge in the field of prosthetic control.
This dissertation addresses the need to improve the controller\u27s robustness by designing a pattern recognition-based control system that classifies the user\u27s intention to actuate the prosthesis. This system is part of a cost-effective prosthetic hand prototype developed to achieve an acceptable level of functional dexterity using a simple to use interface. A Support Vector Machine (SVM) classifier implemented as a directed acyclic graph (DAG) was created. It used wavelet features from multiple surface EMG channels strategically placed over five forearm muscles. The classifiers were evaluated across seven subjects. They were able to discriminate five wrist motions with an accuracy of 91.5%. Variations of electrode locations were artificially introduced at each recording session as part of the procedure, to obtain data that accounted for the changes in the user\u27s muscle patterns over time. The generalization ability of the SVM was able to capture most of the variability in the data and to maintain an average classification accuracy of 90%.
Two principal component analysis (PCA) frameworks were also evaluated to study the relationship between EMG recording sites and the need for feature space reduction. The dimension of the new feature set was reduced with the goal of improving the classification accuracy and reducing the computation time. The analysis indicated that the projection of the wavelet features into a reduced feature space did not significantly improve the accuracy and the computation time. However, decreasing the number of wavelet decomposition levels did lower the computational load without compromising the average signal classification accuracy.
Based on the results of this work, a myoelectric pattern recognition-based control system that uses an SVM classifier applied to time-frequency features may be used to discriminate muscle contraction patterns for prosthetic applications
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
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