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

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Monitoring fetal heart rate (FHR) variability plays a fundamental role in fetal state assessment. Reliable FHR signal can be obtained from an invasive direct fetal electrocardiogram (FECG), but this is limited to labour. Alternative abdominal (indirect) FECG signals can be recorded during pregnancy and labour. Quality, however, is much lower and the maternal heart and uterine contractions provide sources of interference. Here, we present ten twenty-minute pregnancy signals and 12 five-minute labour signals. Abdominal FECG and reference direct FECG were recorded simultaneously during labour. Reference pregnancy signal data came from an automated detector and were corrected by clinical experts. The resulting dataset exhibits a large variety of interferences and clinically significant FHR patterns. We thus provide the scientific community with access to bioelectrical fetal heart activity signals that may enable the development of new methods for FECG signals analysis, and may ultimately advance the use and accuracy of abdominal electrocardiography methods.Web of Science71art. no. 20

Computational methods for physiological data

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009.Author is also affiliated with the MIT Dept. of Electrical Engineering and Computer Science. Cataloged from PDF version of thesis.Includes bibliographical references (p. 177-188).Large volumes of continuous waveform data are now collected in hospitals. These datasets provide an opportunity to advance medical care, by capturing rare or subtle phenomena associated with specific medical conditions, and by providing fresh insights into disease dynamics over long time scales. We describe how progress in medicine can be accelerated through the use of sophisticated computational methods for the structured analysis of large multi-patient, multi-signal datasets. We propose two new approaches, morphologic variability (MV) and physiological symbolic analysis, for the analysis of continuous long-term signals. MV studies subtle micro-level variations in the shape of physiological signals over long periods. These variations, which are often widely considered to be noise, can contain important information about the state of the underlying system. Symbolic analysis studies the macro-level information in signals by abstracting them into symbolic sequences. Converting continuous waveforms into symbolic sequences facilitates the development of efficient algorithms to discover high risk patterns and patients who are outliers in a population. We apply our methods to the clinical challenge of identifying patients at high risk of cardiovascular mortality (almost 30% of all deaths worldwide each year). When evaluated on ECG data from over 4,500 patients, high MV was strongly associated with both cardiovascular death and sudden cardiac death. MV was a better predictor of these events than other ECG-based metrics. Furthermore, these results were independent of information in echocardiography, clinical characteristics, and biomarkers.(cont.) Our symbolic analysis techniques also identified groups of patients exhibiting a varying risk of adverse outcomes. One group, with a particular set of symbolic characteristics, showed a 23 fold increased risk of death in the months following a mild heart attack, while another exhibited a 5 fold increased risk of future heart attacks.by Zeeshan Hassan Syed.Ph.D

Shape Theoretic and Machine Learning Based Methods for Automatic Clustering and Classification of Cardiomyocytes Based on Action Potential Morphology

Stem cells have been a hot topic in the cardiology community for the last decade and a half. Ever since we learned how to differentiate cardiomyocytes from embryonic and induced pluripotent stem cells, there has been a lot of research devoted to the potential of utilizing these cardiomyocytes for regenerative medicine, drug model studies, and arrhythmogenesis analysis. However, while cardiomyocyte purification methods have advanced significantly, methods for the identification and isolation of specific types of cardiomyocytes, such as ventricular or pacemaking cells, have not seen the same progress. Among the different avenues for accomplishing this task, the electrophysiological one is of particular interest because every cardiomyocyte type generates a distinct signature known as an action potential. The current standard for analyzing the action potential of a cardiomyocyte is an expert-level subjective thresholding of specific features, such as action potential duration. However this approach does not transfer across datasets and does not scale with the increasing populations of cardiomyocytes. In this thesis, ideas from the machine learning and shape analysis communities are explored to develop new, automated methods for the analysis of cardiomyocytes based on their action potentials. These methods allow us to identify subpopulations of similar cardiomyocytes based on their action potential morphology, hypothesize the eventual chamber-specific fate of newly differentiated cardiomyocytes, and make effective comparisons between cardiomyocytes in drug and cell-line studies. The objective, scalable methods presented in this thesis present a new paradigm in performing analysis in high-throughput applications of cardiomyocytes via action potential morphology, and could be of large benefit to the cardiology and biology communities

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

An overview of clustering methods with guidelines for application in mental health research

Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and librarie

A Process for Extracting Knowledge in Design for the Developing World

The aim of this study was to develop the process necessary to identify design knowledge shared across product classes and contexts in Design for the Developing World. A process for extracting design knowledge in the field of Design for the Developing World was developed based on the Knowledge Discovery in Databases framework. This process was applied to extract knowledge from a sample dataset of 48 products and small-scale technologies. Unsupervised cluster analysis revealed two distinct product groups, cluster X-AA and cluster Z-AC-AD. Unique attributes of cluster XX-AA include local manufacture, local maintenance and service, human-power, distribution by a non-governmental organization, income-generation, and application in water/sanitation or agriculture sectors. The label Locally Oriented Design for the Developing World was assigned to this group based on the dominant features represented. Unique attributes of cluster Z-AC-AD include electric-power, distribution by a private organization, and application in the health or energy/communication sectors. The label Globally Oriented Design for the Developing World was assigned to this group. These findings were corroborated by additional analyses that suggest certain design knowledge is shared across classes and contexts within groups of products. The results suggest that at least two of these groups exist, which can serve as an initial framework for organizing the literature related to inter-context and inter-class design knowledge. Design knowledge was extracted from each group by collecting known approaches, principles, and methods from available literature. This knowledge may be applied as design guidance in future work by identifying a product group corresponding to the design scenario and sourcing the related set of knowledge