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DOI: 10.3414/ME13-02-0036
Point-process Nonlinear Autonomic Assessment of Depressive States in Bipolar Patients
Publication History
received:14 October 2013
accepted:14 May 2014
Publication Date:
20 January 2018 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00035037/201404/lookinside/thumbnails/10-3414-me-13-02-0036-1.jpg)
Summary
Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.
Objectives: The goal of this work is to apply a computational methodology able to characterize mood states in bipolar patients through instantaneous analysis of heartbeat dynamics.
Methods: A Point-Process-based Nonlinear Autoregressive Integrative (NARI) model is applied to analyze data collected from five bipolar patients (two males and three females, age 42.4 ± 10.5 range 32−56) undergoing a dedicated affective elicitation protocol using images from the International Affective Picture System (IAPS) and Thematic Apperception Test (TAT). The study was designed within the European project PSYCHE (Personalised monitoring SYstems for Care in mental HEalth).
Results: Results demonstrate that the inclusion of instantaneous higher order spectral (HOS) features estimated from the NARI nonlinear assessment significantly improves the accuracy in successfully recognizing specific mood states such as euthymia and depression with respect to results using only linear indices. In particular, a specificity of 74.44% using the instantaneous linear features set, and 99.56% using also the nonlinear feature set were achieved. Moreover, IAPS emotional elicitation resulted in a more discriminant procedure with respect to the TAT elicitation protocol.
Conclusions: A significant pattern of instantaneous heartbeat features was found in depressive and euthymic states despite the inter-subject variability. The presented point-process Heart Rate Variability (HRV) nonlinear methodology provides a promising application in the field of mood assessment in bipolar patients.
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References
- 1 Kessler RC. et al Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Archives of general psychiatry 1994; 51 (01) 8
- 2 Phillips ML. et al Identifying functional neuroimaging biomarkers of bipolar disorder: toward DSM-V. Schizophrenia bulletin 2007; 33: 04
- 3 Valenza G, Gentili C, Lanatà A, Scilingo EP. Mood recognition in bipolar patients through the PSYCHE platform: Preliminary evaluations and perspectives. Artificial intelligence in medicine 2013; 57 (01) 49-58.
- 4 Bauer MS, Vojta C, Kinosian B, Altshuler L, Glick H. The Internal State Scale: replication of its discriminating abilities in a multisite, public sector sample. Bipolar Disorders 2000; 2 (04) 340-346.
- 5 Taillard J. et al Sleep and heart rate circadian rhythm in depression: The necessity to separate. Chronobiology International 1993; 10 (01) 63-72.
- 6 Valenza G, Citi L, Scilingo E, Barbieri R. Instantaneous Bispectral Characterization of the Autonomic Nervous System through a Point-Process Nonlinear Model. In: World Congress on Medical Physics and Biomedical Engineering May 26-31;2012,. Beijing, China: Springer; 2013: 530-533.
- 7 Valenza G, Citi L, Scilingo E, Barbieri R. Point-Process Nonlinear Models with Laguerre and Volterra Expansions: Instantaneous Assessment of Heartbeat Dynamics. Signal Processing, IEEE Transactions On 2013; 61 (11) 2914-2926.
- 8 Valenza G, Citi L, Scilingo EP, Barbieri R. Using Laguerre expansion within point-process models of heartbeat dynamics: A comparative study. In: Engineering in Medicine and Biology Society (EMBC), 2012. Annual International Conference of the IEEE. 2012: 29-32.
- 9 Barbieri R. et al A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. American Journal of Physiology - Heart and Circulatory Physiology 2005; 288 (01) H424
- 10 Citi L, Valenza G, Barbieri R. Instantaneous estimation of high-order nonlinear heartbeat dynamics by Lyapunov exponents. In: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. IEEE. 2012: 13-16.
- 11 Valenza G, Citi L, Barbieri R. Instantaneous nonlinear assessment of complex cardiovascular dynamics by Laguerre-Volterra point process models. In: Engineering in Medicine and Biology Society (EMBC), 2013. 35th Annual International Conference of the IEEE. 2013: 6131-6134.
- 12 Chua KC. et al Application of higher order statistics/spectra in biomedical signals - A review. Medical engineering & physics 2010; 32 (07) 679-689.
- 13 Granger CWJ. et al An introduction to long-memory time series models and fractional differencing. Journal of time series analysis 1980; 1 (01) 15-29.
- 14 Le Caillec JM, Garello R. Nonlinear system identification using autoregressive quadratic models. Signal processing 2001; 81 (02) 357-379.
- 15 Task Force of the European Society of Cardiology, the North American Society of Pacing, Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 1996; 93 (05) 1043-1065.
- 16 Nichols JM. et al The bispectrum and bicoherence for quadratically nonlinear systems subject to non-gaussian inputs. Signal Processing, IEEE Transactions on 2009; 57 (10) 3879-3890.
- 17 Reyes del Paso GA, Langewitz W, Mulder LJ, Roon A, Duschek S. The utility of low frequency heart rate variability as an index of sympathetic cardiac tone: A review with emphasis on a reanalysis of previous studies. Psychophysiology 2013; 50 (05) 477-487.
- 18 Lang P, Bradley M, Cuthbert B. International affective picture system (IAPS): Technical manual and affective ratings. Gainesville, FL: The Center for Research in Psychophysiology, University of Florida; 1999
- 19 KinneBrock W. Neural Networks. München: Oldenburg Verlag; 1992
- 20 Kohavi R. et al A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 1995; 14: 1137-1145.
- 21 Greco A, Valenza G, Lanata A, Rota G, Scilingo EP. Electrodermal Activity in Bipolar Patients during Affective Elicitation. IEEE Journal of Biomedical and Health Informatics. 2014
- 22 Valenza G, Citi L, Gentili C, Lanata A, Scilingo EP, Barbieri R. Characterization of Depressive States in Bipolar Patients using Wearable Textile Technology and Instantaneous Heart Rate Variability Assessment. IEEE Journal of Biomedical and Health Informatics. 2014
- 23 Valenza G, Nardelli M, Lanata A, Gentili C, Bertschy G, Paradiso R, Scilingo EP. Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE Journal of Biomedical and Health Informatics. 2014
- 24 Greco A, Lanatà A, Valenza G, Rota G. Vanello N, Scilingo E P. On the deconvolution analysis of electrodermal activity in bipolar patients. In: Engineering in Medicine and Biology Society (EMBC). Annual International Conference of the IEEE. 2012
- 25 Valenza G, Citi L, Lanatá A, Scilingo E.P, Barbieri R. Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics. Nature Scientific reports. 2014: 4