IEEE J Biomed Health Inform - Automatic detection of atrial fibrillation in cardiac vibration signals.

Tópicos

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Resumo

We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.

Resumo Limpo

present studi feasibl automat detect atrial fibril af cardiac vibrat signal ballistocardiogramsbcg record unobtrus bedmount sensor propos system intend screen monitor tool homehealthcar applic replac ecgbas method use clinic environ base bcg data record studi af patient evalu rank seven popular machin learn algorithm naiv bay linear quadrat discrimin analysi support vector machin random forest well bag boost tree perform separ s long bcg epoch one three class sinus rhythm atrial fibril artifact algorithm featur subset set statist timefrequencydomain timedomain featur select base mutual inform featur class label well first secondord interact among featur classifi evalu set epoch mean fold crossvalid best algorithm random forest achiev matthew correl coeffici mean sensit mean specif respect

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