Literature DB >> 25570609

Temporal trends of neuro-autonomic complexity during severe episodes of bipolar disorders.

Mimma Nardelli, Gaetano Valenza, Claudio Gentili, Antonio Lanata, Enzo Pasquale Scilingo.   

Abstract

Bipolar disorder is a chronic psychiatric condition during which patients experience mood swings among depression, hypomania or mania, mixed state (depression-hypomania) and euthymia, i.e., good affective balance. Nowadays, an objective characterization of the temporal trends of the disease as a response to the pharmacological treatment through physiological signatures, especially during severe episodes, is still missing. In this study we show interesting findings relating neuro-autonomic complexity to severe pathological mood states. More specifically, we studied Sample Entropy (SampEn) measures on Heart Rate Variability series gathered from four bipolar patients recruited within the frame of the European project PSYCHE. Patients were monitored through long term ECG recordings from the first hospital admission until clinical remission, i.e., the euthymic state. We observed that a mood transition from mixed-state to euthymia passing through depression can be characterized by increased SampEn values, i.e. as the patient is going to recover, SampEn increases. These results are in agreement with the current literature reporting on the complexity dynamics of the cardiovascular system and can provide a promising and viable clinical decision support to objectify the diagnosis and improve the management of psychiatric disorders.

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Year:  2014        PMID: 25570609     DOI: 10.1109/EMBC.2014.6944241

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  2 in total

1.  Sex difference in the progression of manic symptoms during acute hospitalization: A prospective pilot study.

Authors:  Osama A Abulseoud; Güliz Şenormancı; Ömer Şenormancı; Oya Güçlü; Brooke Schleyer; Ulas Camsari
Journal:  Brain Behav       Date:  2020-02-13       Impact factor: 2.708

2.  Detecting Manic State of Bipolar Disorder Based on Support Vector Machine and Gaussian Mixture Model Using Spontaneous Speech.

Authors:  Zhongde Pan; Chao Gui; Jing Zhang; Jie Zhu; Donghong Cui
Journal:  Psychiatry Investig       Date:  2018-07-04       Impact factor: 2.505

  2 in total

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