title: Epoch-based Entropy for Early Screening of Alzheimer's Disease.
abstract: In this paper, we introduce a novel entropy measure, termed epoch-based entropy. This measure quantifies disorder of EEG signals both at the time level and spatial level, using local density estimation by a Hidden Markov Model on inter-channel stationary epochs. The investigation is led on a multi-centric EEG database recorded from patients at an early stage of Alzheimer's disease (AD) and age-matched healthy subjects. We investigate the classification performances of this method, its robustness to noise, and its sensitivity to sampling frequency and to variations of hyperparameters. The measure is compared to two alternative complexity measures, Shannon's entropy and correlation dimension. The classification accuracies for the discrimination of AD patients from healthy subjects were estimated using a linear classifier designed on a development dataset, and subsequently tested on an independent test set. Epoch-based entropy reached a classification accuracy of 83% on the test dataset (specificity = 83.3%, sensitivity = 82.3%), outperforming the two other complexity measures. Furthermore, it was shown to be more stable to hyperparameter variations, and less sensitive to noise and sampling frequency disturbances than the other two complexity measures.
authors.affiliation: * ESPCI ParisTech, PSL Research University, 10 rue Vauquelin, 75005 Paris, France.|† SIGMA (SIGnal processing and MAchine learning) Laboratory, 10 rue Vauquelin, 75231 Paris Cedex 05, France.
authors.forename: N
authors.initials: N
authors.lastname: Houmani
authors.affiliation: * ESPCI ParisTech, PSL Research University, 10 rue Vauquelin, 75005 Paris, France.|† SIGMA (SIGnal processing and MAchine learning) Laboratory, 10 rue Vauquelin, 75231 Paris Cedex 05, France.
authors.forename: G
authors.initials: G
authors.lastname: Dreyfus
authors.affiliation: * ESPCI ParisTech, PSL Research University, 10 rue Vauquelin, 75005 Paris, France.|‡ Brain Plasticity Laboratory, CNRS UMR 8249, 10 rue Vauquelin, 75231 Paris Cedex 05, France.
authors.forename: F B
authors.initials: FB
authors.lastname: Vialatte
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D019540:Area Under Curve; D001921:Brain / Q000503:physiopathology*; D019992:Databases as Topic; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D006801:Humans; D016014:Linear Models; D008390:Markov Chains; D012372:ROC Curve; D012146:Rest; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Alzheimer’s disease; EEG signal; complexity measures; entropy; hidden Markov models; stationary multichannel epochs