Multi-Dynamics Analysis of QRS Complex for Atrial Fibrillation Diagnosis

International audienceThis paper presents an effective atrial fibrillation (AF) diagnosis algorithm based on multi-dynamics analysis of QRS complex. The idea behind this approach is to produce a variety of heartbeat time series features employing several linear and nonlinear functions via different dynamics of the QRS complex signal. These extracted features from these dynamics will be connected through machine learning based algorithms such as Support Vector Machine (SVM) and Multiple Kernel Learning (MKL), to detect AF episode occurrences. The reported performances of these methods were evaluated on the Long-Term AF Database which includes 84 of 24-hour ECG recording. Thereafter, each record was divided into consecutive intervals of one-minute segments to feed the classifier models. The obtained sensitivity, specificity and positive classification using SVM were 96.54%, 99.69%, and 99.62%, respectively, and for MKL they reached 95.47%, 99.89%, and 99.87%, respectively. Therefore, these medical-oriented detectors can be clinically valuable to healthcare professional for screening AF pathology

A Novel Method to Identify Relevant Features for Automatic Detection of Atrial Fibrillation

International audienceThe selection of an appropriate subset of predictors from a large set of features is a major concern in clinical diagnosis research. The purpose of this study is to demonstrate that the Multiple Kernel Learning (MKL) approach could be successfully applied as a feature selection process for machine learning pipelines. Furthermore, we suggest a multi-dynamic analysis of heartbeat signal to characterize the most common sustained arrhythmia, Atrial Fibrillation (AF). Indeed, we have targeted six different dynamics of QRS time series, where each one will be associated with 12 linear and nonlinear functions to yield a set of 72 features. Afterward, a feature selection process is implemented using the MKL to evaluate the relevant features allowing AF diagnosis. Hence, a subset of only 13 features has been selected. To demonstrate the effectiveness of the proposed approach, Support Vector Classification (SVC) model has been conducted, first, on all features, and then on the features issued from the MKL selection feature process. The obtained results showed that the SVC model trained by 13 features outperformed the one trained by 72 features. This approach has reached 99.77% of success rate in the discrimination between Normal Sinus Rhythm (NSR) and AF. The proposed selection feature method holds several interesting properties in dimensionality reduction which makes it a suitable choice for several applications

Advanced Machine Learning Coupled with Heart-Inter-beat derivatives for Cardiac Arrhythmia Detection

International audienceThis paper presents a novel strategy based on derivatives time series and advanced machine learning for medical decision-support especially for cardiac arrhythmia diagnosis. Most of recent technologies (smartphones, smart watches, etc.) are focusing on a unique source of information extracted from electrocardiography/photoplethysmography (i.e. heat inter-beat (RR) interval time series) coupled with classical pattern recognition methods to build efficient data-driven models. Herein, we demonstrate that the second derivative time series coupled with principal component analysis (PCA) and relevance vector machine (RVM) allow detection of abnormal rhythm. To achieve this aim, four features were extracted from one minute RR time series as well as from their derivatives and were subjected to PCA and RVM. PCA, as explanatory method, has shown that detection of AF arrhythmia became straightforward by targeting the second derivative time series. RVM was optimized through four kernel functions and the best model has reached 99.83% success rate to diagnosis AF and normal rhythm. The proposed approach outperformed several recent studies dealing with automatic AF diagnosis. Therefore, this method, which can be easily embedded in personal monitoring devices for real time cardiac arrhythmia detection, could be adapted for various medical decision-support involving time series recordings