%J Journal of Affective Disorders
%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.
%R doi:10.1016/j.jad.2016.11.008
%P 30-38
%A Claudio Gentili
%A Gaetano Valenza
%A Mimma Nardelli
%A Antonio Lanatà
%A Gilles Bertschy
%A Luisa Weiner
%A Mauro Mauri
%A Enzo Pasquale Scilingo
%A Pietro Pietrini
%L eprints3601
%T Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study
%K Bipolar disorders; Heart rate variability; Psychophysiology; Biological psychiatry; Supported vector machine
%V 209
%I Elsevier
%D 2017