%0 Journal Article
%@ 01650327
%A Gentili, Claudio
%A Valenza, Gaetano
%A Nardelli, Mimma
%A Lanatà, Antonio
%A Bertschy, Gilles
%A Weiner, Luisa
%A Mauri, Mauro
%A Scilingo, Enzo Pasquale
%A Pietrini, Pietro
%D 2017
%F eprints:3601
%I Elsevier
%J Journal of Affective Disorders
%K Bipolar disorders; Heart rate variability; Psychophysiology; Biological psychiatry; Supported vector machine
%P 30-38
%T Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study
%U http://eprints.imtlucca.it/3601/
%V 209
%X Objectives    Recent research indicates that Heart Rate Variability (HRV) is affected in Bipolar Disorders (BD) patients. To determine whether such alterations are a mere expression of the current mood state or rather contain longitudinal information on BD course, we examined the potential influence of states adjacent in time upon HRV features measured in a target mood state.    Methods    Longitudinal evaluation of HRV was obtained in eight BD patients by using a wearable monitoring system developed within the PSYCHE project. We extracted time-domain, frequency-domain and non-linear HRV-features and trained a Support Vector Machine (SVM) to classify HRV-features according to mood state. To evaluate the influence of adjacent mood states, we trained SVM with different HRV-feature sets: 1) belonging to each mood state considered alone; 2) belonging to each mood state and normalized using information from the preceding mood state; 3) belonging to each mood state and normalized using information from the preceding and subsequent mood states; 4) belonging to each mood state and normalized using information from two randomly chosen states.    Results    SVM classification accuracy within a target state was significantly greater when HRV-features from the previous and subsequent mood states were considered.