TY  - JOUR
KW  - Bipolar disorders; Heart rate variability; Psychophysiology; Biological psychiatry; Supported vector machine
VL  - 209
PB  - Elsevier
Y1  - 2017///
UR  - http://doi.org/10.1016/j.jad.2016.11.008
JF  - Journal of Affective Disorders
A1  - Gentili, Claudio
A1  - Valenza, Gaetano
A1  - Nardelli, Mimma
A1  - Lanatà, Antonio
A1  - Bertschy, Gilles
A1  - Weiner, Luisa
A1  - Mauri, Mauro
A1  - Scilingo, Enzo Pasquale
A1  - Pietrini, Pietro
N2  - 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.
SP  - 30
TI  - Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study
EP  - 38
ID  - eprints3601
SN  - 01650327
AV  - restricted
ER  -