Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study
Open accessAbstract
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.Conclusions Although preliminary and in need of replications our results suggest for the first time that psychophysiological states in BD contain information related to the subsequent ones. Such characteristic may be used to improve clinical management and to develop algorithms to predict clinical course and mood switches in individual patients.
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Bipolar Disorder mood states including depression, mixed, mania and euthymia were accurately classified (68-85%) using a support vector machine trained on features of heart rate variability and previous mood (n = 8).
“Bipolar Disorder (BD) mood states including depression, mixed, mania and euthymia were accurately classified (68–85%) using a support vector machine trained on features of heart rate variability and previous mood (n = 8) [97].”
From Wearables and the Medical Revolution (2018) · cites this paper as [97]
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- Authors
- Gentili, C., Valenza, G., Nardelli, M., Lanatà, A., Bertschy, G., Weiner, L., Mauri, M., Scilingo, E. P., & Pietrini, P.
- Affiliations
- University of Padua (IT)
- University of Pisa (IT)
- Inserm (FR)
- IMT School for Advanced Studies Lucca (IT)
- Corresponding author
- Gentili, Claudio
- Pietrini, Pietro
- ORCID
- Venue
- Journal of Affective Disorders
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 209
- Pages
- 30-38
- ISSN
- 0165-0327 (ISSN-L)
- OpenAlex
- W2556190418
- Cited by
- 32
- References
- 91
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 3 · 2022: 3 · 2021: 2 · 2019: 4 · 2018: 4 · 2017: 4
- FWCI
- 3.55
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Journal of Affective Disorders (publishedVersion)
- PubMed (publishedVersion)
- IMT Institutional Repository (IMT School for Advanced Studies Lucca) (acceptedVersion)
- Florence Research (University of Florence) (submittedVersion, other-oa)
- HAL (submittedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- EU Commission (FP7 ICT-247777 Psyche)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- bipolar disorder, heart rate variability, wearable monitoring, mood switching, psychophysiological state, support vector machine classification, clinical course prediction
- MeSH terms
- Adult, Affect, Bipolar Disorder, Electrocardiography, Female, Heart Rate, Humans, Longitudinal Studies, Male, Middle Aged, Pilot Projects, Psychiatric Status Rating Scales, Support Vector Machine
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