@article{eprints3601,
          volume = {209},
            year = {2017},
           pages = {30--38},
          author = {Claudio Gentili and Gaetano Valenza and Mimma Nardelli and Antonio Lanat{\`a} and Gilles Bertschy and Luisa Weiner and Mauro Mauri and Enzo Pasquale Scilingo and Pietro Pietrini},
           title = {Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study},
       publisher = {Elsevier},
         journal = {Journal of Affective Disorders},
             url = {http://eprints.imtlucca.it/3601/},
        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.}
}