eprintid: 3601
rev_number: 9
eprint_status: archive
userid: 69
dir: disk0/00/00/36/01
datestamp: 2016-11-23 10:26:36
lastmod: 2017-01-09 09:55:38
status_changed: 2017-01-09 09:55:38
type: article
metadata_visibility: show
creators_name: Gentili, Claudio
creators_name: Valenza, Gaetano
creators_name: Nardelli, Mimma
creators_name: Lanatà, Antonio
creators_name: Bertschy, Gilles
creators_name: Weiner, Luisa
creators_name: Mauri, Mauro
creators_name: Scilingo, Enzo Pasquale
creators_name: Pietrini, Pietro
creators_id: 
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creators_id: 
creators_id: 
creators_id: 
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creators_id: pietro.pietrini@imtlucca.it
title: Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study
ispublished: pub
subjects: RC0321
divisions: CSA
full_text_status: restricted
keywords: Bipolar disorders; Heart rate variability; Psychophysiology; Biological psychiatry; Supported vector machine
abstract: 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.
date: 2017
date_type: published
publication: Journal of Affective Disorders
volume: 209
publisher: Elsevier
pagerange: 30-38
id_number: doi:10.1016/j.jad.2016.11.008
refereed: TRUE
issn: 01650327
official_url: http://doi.org/10.1016/j.jad.2016.11.008
citation:   Gentili, Claudio and Valenza, Gaetano and Nardelli, Mimma and Lanatà, Antonio and Bertschy, Gilles and Weiner, Luisa and Mauri, Mauro and Scilingo, Enzo Pasquale and Pietrini, Pietro  Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study.  Journal of Affective Disorders, 209.  pp. 30-38.  ISSN 01650327      (2017)  
document_url: http://eprints.imtlucca.it/3601/1/Gentili_et_al_JAD_2016.pdf