106 research outputs found

    Löwner-Based Tensor Decomposition for Blind Source Separation in Atrial Fibrillation ECGs

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    International audienceThe estimation of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is an important step in the noninvasive analysis of atrial fibrillation (AF), the most common sustained cardiac arrhythmia in clinical practice. Recently, this blind source separation (BSS) problem has been formulated as a tensor factorization, based on the block term decomposition (BTD) of a data tensor built from Hankel matrices of the observed ECG. However, this tensor factorization technique was precisely assessed only in segments with long R-R intervals and with the AA well defined in the TQ segment, where ventricular activity (VA) is absent. Due to the chaotic nature of AA in AF, segments with disorganized or weak AA and with short R-R intervals are quite more common in persistent AF, posing some difficulties to the BSS methods to extract the AA signal, regarding performance and computational cost. In this paper, the BTD built from Löwner matrices is proposed as a method to separate VA from AA in these challenging scenarios. Experimental results obtained in a population of 10 patients show that the Löwner-based BTD outperforms the Hankel-based BTD and two well-known matrix-based methods in terms of atrial signal estimation quality and computational cost

    Block Term Decomposition of ECG Recordings for Atrial Fibrillation Analysis: Temporal and Inter-Patient Variability

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    International audienceResponsible for 25% of strokes and 1/3 of hospitalizations due to cardiac related disturbances, atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in clinical practice, considered as the last great frontier of cardiac electrophysiology. Its mechanisms are not completely understood, and a precise analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary to better understand this challenging cardiac condition. Recently, the block term decomposition (BTD) has been proposed as a powerful tool to noninvasively extract AA in AF ECG signals. However, this tensor factorization technique was performed only in short ECG recordings and illustrated in single patients. To assess its performance and variability through different subjects, BTD is applied to a population of 10 AF patients in this paper. Also, its time variability is evaluated by means of long segments of AF ECG with varying observation window size. Experimental results show the consistency of BTD as an AA extraction tool, outperforming two well-known matrix-based methods in most of the observed cases for long and short AF ECG recordings

    Source Classification in Atrial Fibrillation Using a Machine Learning Approach

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    International audienceA precise analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary for a better understanding of the mechanisms behind atrial fibrillation (AF). Blind source separation (BSS) techniques have proven useful in extracting the AA source from ECG recordings. However, the automated selection of the AA source among the other sources after BSS is still an issue. In this scenario, the present work proposes two contributions: i) the use of the normalized mean square error of the TQ segment (NMSE-TQ) as a new feature to quantify the AA content of a source, and ii) an automated classification of AA and non-AA sources using three well-known machine learning algorithms. The tested classifiers outperform the techniques present in literature. A pattern in the mean and standard deviation of the used features, for AA and non-AA sources, is also observed

    Using the multi-linear rank-(Lr, Lr, 1) decomposition for the detection of the 200 Hz band activity in somatosensory evoked magnetic fields and somatosensory evoked electrical potentials

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    Studies of oscillations in the frequency band between 80 Hz and 250 Hz for EEG (Electroencephalogram) and MEG (Magnetoencephalogram) have achieved fruitful results of detecting and interpreting both normal and pathological activities in the brain. This contribution presents a new description of the 200 Hz band activity in somatosensory evoked electrical potentials (SEPs) and somatosensory evoked magnetic fields (SEFs) with the help of tensor decompositions. The SEPs and SEFs elicited by electrical stimulation of the median nerve were measured in eight healthy volunteers. A time-frequency analysis of the SEPs and SEFs produced the time-dependent spectra of the signals that were arranged into three-dimensional EEG and MEG data tensors, respectively. We then propose a novel multi-way component analysis approach by employing a tensor decomposition known as the multi-linear rank-( LrL_{r} , LrL_{r} , 1) decomposition. Featuring the ability to extract channel-dependent spectral signatures, this method is able to separate the 200 Hz band activity-related signal components in SEPs and SEFs. Via a coupled version of the multi-linear rank-( LrL_{r} , LrL_{r} , 1) decomposition, a joint processing of these simultaneous EEG and MEG recordings has been achieved. The advantages of the joint processing over the separate processing of EEG or MEG alone have been both qualitatively and quantitatively validated in seven out of eight subjects

    A Deep Learning Classifier for Detecting Atrial Fibrillation in Hospital Settings Applicable to Various Sensing Modalities

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    Cardiac signals provide variety of information related to the patient\u27s health. One of the most important is for medical experts to diagnose the functionality of a patient’s heart. This information helps the medical experts monitor heart disease such as atrial fibrillation and heart failure. Atrial fibrillation (AF) is one of the most major diseases that are threatening patients’ health. Medical experts measure cardiac signals usng the Electrocardiogram (ECG or EKG), the Photoplethysmogram (PPG), and more recently the Videoplethysmogram (VPG). Then they can use these measurements to analyze the heart functionality to detect heart diseases. In this study, these three major cardiac signals were used with different classification methodologies such as Basic Thresholding Classifiers (BTC), Machine Learning (SVM) classifiers, and deep learning classifiers based on Convolutional Neural Networks (CNN) to detect AF. To support the work, cardiac signals were acquired from forty-six AF subjects scheduled for cardioversion who were enrolled in a clinical study that was approved by the Internal Review Committees to protect human subjects at the University of Rochester Medical Center (URMC, Rochester, NY), and the Rochester Institute of Technology (RIT, Rochester, NY). The study included synchronized measurements of 5 minutes and 30 seconds of ECG, PPG, VPG 180Hz (High-quality camera), VPG 30 Hz (low quality webcam), taken before and after cardioversion of AF subjects receiving treatment at the AF Clinic of URMC. These data are subjected to BTC, SVM, and CNN classifiers to detect AF and compare the result for each classifier depending on the signal type. We propose a deep learning approach that is applicable to different kinds of cardiac signals to detect AF in a similar manner. By building this technique for different sensors we aim to provide a framework to implement a technique that can be used for most devices, such as, phones, tablets, PCs, ECG devices, and wearable PPG sensors. This conversion of the different sensing platforms provides a single AF detection classifier that can support a complete monitoring cycle that is referring to screen the patient whether at a hospital or home. By using that, the risk factor of heart attack, stroke, or other kind of heart complications can be reduced to a low level to prevent major dangers, since increasing monitoring AF patients helps to predict the disease at an early stage as well as track its progress. We show that the proposed approach provides around 99% accuracy for each type of classifier on the test dataset, thereby helping generalize AF detection by simplifying implementation using a sensor-agnostic deep learning model

    Spatial Characterization and Estimation of Intracardiac Propagation Patterns During Atrial Fibrillation

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    This doctoral thesis is in the field of biomedical signal processing with focus on methods for the analysis of atrial fibrillation (AF). Paper I of the present thesis addresses the challenge of extracting spatial properties of AF from body surface signals. Different parameters are extracted to estimate the preferred direction of atrial activation and the complexity of the atrial activation pattern. In addition, the relation of the spatial properties to AF organization, which is quantified by AF frequency, is evaluated. While no significant correlation between the preferred direction of atrial activation and AF frequency could be observed, the complexity of the atrial activation pattern was found to increase with AF frequency. The remaining three papers deal with the analysis of the propagation of the electrical activity in the atria during AF based on intracardiac signals. In Paper II, a time-domain method to quantify propagation patterns along a linear catheter based on the detected atrial activation times is developed. Taking aspects on intra-atrial signal organization into account, the detected activation times are combined into wavefronts, and parameters related to the consistency of the wavefronts over time and the activation order along the catheter are extracted. Furthermore, the potential relationship of the extracted parameters to established measures from body surface signals is investigated. While the degree of wavefront consistency was not reflected by the applied body surface measures, AF frequency could distinguish between recordings with different degrees of intra-atrial signal organization. This supports the role of AF frequency as an organization measure of AF. In Paper III, a novel method to analyze intracardiac propagation patterns based on causality analysis in the frequency domain is introduced. In particular, the approach is based on the partial directed coherence (PDC), which evaluates directional coupling between multiple signals in the frequency domain. The potential of the method is illustrated with simulation scenarios based on a detailed ionic model of the human atrial cell as well as with real data recordings, selected to present typical propagation mechanisms and recording situations in atrial tachyarrhythmias. For simulated data, the PDC is correctly reflecting the direction of coupling and thus the propagation between all recording sites. For real data, clear propagation patterns are identified which agree with previous clinical observations. Thus, the results illustrate the ability of the novel approach to identify propagation patterns from intracardiac signals during AF which can provide important information about the underlying AF mechanisms, potentially improving the planning and outcome of ablation. However, spurious couplings over long distances can be observed when analyzing real data comprised by a large number of simultaneously recorded signals, which gives room for further improvement of the method. The derivation of the PDC is entirely based on the fit of a multivariate autoregressive (MVAR) model, commonly estimated by the least-squares (LS) method. In Paper IV, the adaptive group least absolute selection and shrinkage operator (LASSO) is introduced in order to avoid overfitting of the MVAR model and to incorporate prior information such as sparsity of the solution. The sparsity can be motivated by the observation that direct couplings over longer distances are likely to be zero during AF; an information which has been further incorporated by proposing distance-adaptive group LASSO. In simulations, adaptive and distance-adaptive group LASSO are found to be superior to LS estimation in terms of both detection and estimation accuracy. In addition, the results of both simulations and real data analysis indicate that further improvements can be achieved when the distance between the recording sites is known or can be estimated. This further promotes the PDC as a method for analysis of AF propagation patterns, which may contribute to a better understanding of AF mechanisms as well as improved AF treatment

    The Application of Computer Techniques to ECG Interpretation

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    This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

    Computer-Assisted Electroanatomical Guidance for Cardiac Electrophysiology Procedures

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    Cardiac arrhythmias are serious life-threatening episodes affecting both the aging population and younger patients with pre-existing heart conditions. One of the most effective therapeutic procedures is the minimally-invasive catheter-driven endovascular electrophysiology study, whereby electrical potentials and activation patterns in the affected cardiac chambers are measured and subsequent ablation of arrhythmogenic tissue is performed. Despite emerging technologies such as electroanatomical mapping and remote intraoperative navigation systems for improved catheter manipulation and stability, successful ablation of arrhythmias is still highly-dependent on the operator’s skills and experience. This thesis proposes a framework towards standardisation in the electroanatomical mapping and ablation planning by merging knowledge transfer from previous cases and patient-specific data. In particular, contributions towards four different procedural aspects were made: optimal electroanatomical mapping, arrhythmia path computation, catheter tip stability analysis, and ablation simulation and optimisation. In order to improve the intraoperative electroanatomical map, anatomical areas of high mapping interest were proposed, as learned from previous electrophysiology studies. Subsequently, the arrhythmic wave propagation on the endocardial surface and potential ablation points were computed. The ablation planning is further enhanced, firstly by the analysis of the catheter tip stability and the probability of slippage at sparse locations on the endocardium and, secondly, by the simulation of the ablation result from the computation of convolutional matrices which model mathematically the ablation process. The methods proposed by this thesis were validated on data from patients with complex congenital heart disease, who present unusual cardiac anatomy and consequently atypical arrhythmias. The proposed methods also build a generic framework for computer guidance of electrophysiology, with results showing complementary information that can be easily integrated into the clinical workflow.Open Acces

    Automatic analysis and classification of cardiac acoustic signals for long term monitoring

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    Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces
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