Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study

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)

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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.

Item Type:	Article
Identification Number:	https://doi.org/10.1016/j.jad.2016.11.008
Uncontrolled Keywords:	Bipolar disorders; Heart rate variability; Psychophysiology; Biological psychiatry; Supported vector machine
Subjects:	R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Research Area:	Computer Science and Applications
Depositing User:	Caterina Tangheroni
Date Deposited:	23 Nov 2016 10:26
Last Modified:	09 Jan 2017 09:55
URI:	http://eprints.imtlucca.it/id/eprint/3601

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