Multivariate analysis for the diagnosis of cardiac arrhythmia

Les MCV sont parties des préoccupations sanitaires les plus pressantes et présentent la première cause de décès dans le monde. Selon l'OMS, les MCV sont à l'origine de 17,9 millions de décès dans le monde chaque année, soit 31% de l'ensemble des décès. En France, les MCV sont la deuxième cause de décès après le cancer, avec environ 150 000 décès par an. L'infarctus du myocarde, également appelé crise cardiaque, est la forme de MCV la plus meurtrière au monde. Il provoque 18 000 décès par an en France, soit 10% de la mortalité totale. Dans cette thèse, nous nous intéressons aux MCV, et plus précisément à l'une de ses principales causes, à savoir les arythmies cardiaques. Les recherches académiques et les industriels s'appuient sur les avancées technologiques pour développer des outils informatiques pour la détection de l'arythmie. Dans ce travail, nous discutons cette problématique en proposant une nouvelle stratégie de diagnostic qui permet de distinguer les sujets sains en présence de battements ectopiques des sujets atteints de la fibrillation auriculaire. Cette stratégie est basée sur l'analyse de dérivés complémentaires extraits de la série chronologique des intervalles R-R. Pour construire le modèle de diagnostic, nous avons appliqué différents algorithmes de classification, notamment les séparateurs à vaste marge et l'apprentissage multinoyaux. En outre, nous avons développé un algorithme de sélection de variables très performant, basé sur la programmation multinoyaux. L'approche développée a été validée sur différentes bases de données d'arythmies cardiaques. Les résultats obtenus démontrent l'efficacité et la robustesse de la méthode développéeCardiovascular disease (CVD) is one of the most pressing health concerns and the leading cause of death worldwide. According to the World Health Organization (WHO), CVD is responsible for 17.9 million deaths worldwide each year, or 31\% of all deaths. In France, CVD is the second leading cause of death after cancer, with approximately 150,000 deaths per year. Myocardial infarction also called a heart attack, is the most deadly form of CVD in the world. It causes 18,000 deaths per year in France, i.e., 10\% of total mortality. In this thesis, we are interested in CVD, and more precisely in one of its leading causes, namely cardiac arrhythmias. Academic research and industry rely on technological advances to develop IT tools for arrhythmia detection. In this work, we address this issue by proposing a new diagnostic strategy to distinguish healthy subjects in the presence of ectopic beats from subjects with atrial fibrillation. This strategy is based on the analysis of additional derivatives extracted from the R-R interval time series. This approach is composed of a process of derivative calculation and feature extraction. We applied different classification algorithms to build the diagnostic model, including support vector machine and multi-kernel learning. Also, we have developed a high-performance variable selection algorithm based on multi-kernel programming. The developed approach has been validated on different cardiac arrhythmia databases. The results obtained demonstrate the efficiency and robustness of the developed metho

An Efficient Pattern Recognition Kernel-Based Method for Atrial Fibrillation Diagnosis

International audienceThe aim of this work is to develop an efficient diagnosis method for atrial fibrillation (AF) arrhythmia based on inter-beat interval time series analysis and relevance vector machine (RVM) classifier. Automatic and fast AF diagnosis is still a major concern for the healthcare professional. Several algorithms based on univariate and multivariate analysis have been developed to detect AF. The published results do not show satisfactory detection accuracy especially for brief duration as short as one minute. Although RVM has been applied on tasks such as computer vision, natural language processing, speech recognition etc., this is the first attempt to adopt RVM for AF diagnosis. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR interval window and then three specific features were calculated. The RVM classifier was trained on 2000 randomly selected samples from the merged database. The results showed that the RVM model performed better than do existing algorithms, with 99.20% for both sensitivity and specificity

Relevance Vector Machine as Data-Driven Method for Medical Decision Making

International audienceThe aim of this work is to develop an efficient data-driven method for automatic medical decision making, especially for cardiac arrhythmia diagnosis. To achieve this goal, we have targeted the most common arrhythmia worldwide -atrial fibrillation (AF). Most of reported studies are dealing with inter-beat interval time series analysis coupled with univariate and/or multivariate data-driven methods. The state of the art of this subject revealed that although satisfactory detection findings have been achieved for long AF durations, there is still scope for improvement which needs to be addressed for brief episodes which is highly desired by healthcare professionals. Relevance vector machine (RVM) has been developed to address this issue. Several kernel functions and parameters have been tested to optimize RVM. Five geometrical and nonlinear features were extracted from 30s inter-beat time series. The RVM classifier was trained on 3000 randomly selected samples from four publicly-accessible sets of clinical data and tested on 1000 samples. The performance of the diagnosis model was evaluated by 10-fold cross-validation method. The results showed that the RVM model performed better than do existing algorithms, with 96.58% success rate. The automatic diagnosis on another dataset of 118985 samples of AF and Normal Sinus Rhythm (NSR) has yield 96.64% of classification accuracy. This automated data-driven decision making approach can be exploited for medical diagnosis of other arrhythmias