Approximate entropy of symptoms of mood: an effective technique to quantify regularity of mood.

OBJECTIVES Several psychiatric conditions are associated with lability of affect. In this study, we investigated regularity of mood using APEN (Approximate Entropy) on daily subjective ratings using a Visual Analog Scale with 11 items pertaining to mood. METHODS APEN was applied to the data in a double-blind placebo controlled investigation on the effects of fluoxetine (n=19), pemoline (n=18) and placebo (n=20) in normal controls. These subjects rated their subjective feelings daily at the end of each day. We analysed 56 data point time series (each value was obtained on each day) after the three experimental conditions. RESULTS While the mean or the SD of all the 56 points was not significantly different among the three conditions, APEN was highly and significantly lower for pemoline compared with fluoxetine and placebo. There was no significant correlation between average APEN and mean or SD (standard deviation). The one symptom that explained most of this difference among the groups after drug administration was the feeling of 'happiness'. CONCLUSIONS This result indicates that there was a more consistent subjective sense of happiness during the 8-week period of pemoline administration compared with the other two drugs. Though this study was not designed to address the efficacy of mood stabilizing drugs, such daily subjective ratings may be useful in future studies that evaluate the stability of mood. APEN has been used in several different fields of research with small data sets and this study extends its possible use to evaluate changes in mood in certain populations such as patients with bipolar disorders.

[1]  R. Post,et al.  Utility of the daily prospective National Institute of Mental Health Life‐Chart Method (NIMH‐LCM‐p) ratings in clinical trials of bipolar disorder , 2002, Depression and anxiety.

[2]  Chi-Sang Poon,et al.  Decrease of cardiac chaos in congestive heart failure , 1997, Nature.

[3]  Steven M. Pincus Approximating Markov chains. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[4]  V. Yeragani,et al.  Nonlinear measures of heart period variability: Decreased measures of symbolic dynamics in patients with panic disorder , 2000, Depression and anxiety.

[5]  J. Butler,et al.  Effect of convective stretching and folding on aerosol mixing deep in the lung, assessed by approximate entropy. , 1997, Journal of applied physiology.

[6]  V. Yeragani,et al.  Fractal dimension and approximate entropy of heart period and heart rate: awake versus sleep differences and methodological issues. , 1998, Clinical science.

[7]  M. Linden,et al.  Zur Reliabilität und Validität der Stimmungsmessung mit der Visuellen Analog-Skala (VAS) , 1982 .

[8]  H V Huikuri,et al.  Measurement of heart rate variability: a clinical tool or a research toy? , 1999, Journal of the American College of Cardiology.

[9]  Richard Balon,et al.  Heart rate time series: decreased chaos after intravenous lactate and increased non-linearity after isoproterenol in normal subjects , 2002, Psychiatry Research.

[10]  Richard Balon,et al.  Diminished chaos of heart rate time series in patients with major depression , 2002, Biological Psychiatry.

[11]  M. J. Katz,et al.  Fractals and the analysis of waveforms. , 1988, Computers in biology and medicine.

[12]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[13]  C. Peng,et al.  Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. TRACE Investigators. TRAndolapril Cardiac Evaluation. , 1999, The American journal of cardiology.

[14]  A. Rush,et al.  Rate of switch in bipolar patients prospectively treated with second-generation antidepressants as augmentation to mood stabilizers. , 2001, Bipolar disorders.

[15]  M. Fujishima,et al.  Chaos and spectral analyses of heart rate variability during head-up tilting in essential hypertension. , 1999, Journal of the autonomic nervous system.

[16]  R. Glenny,et al.  Applications of fractal analysis to physiology. , 1991, Journal of applied physiology.

[17]  V. Yeragani,et al.  Decreased chaos and increased nonlinearity of heart rate time series in patients with panic disorder , 2001, Autonomic Neuroscience.

[18]  B. Carroll,et al.  Short-term variability of mood ratings in unipolar and bipolar depressed patients. , 1996, Journal of affective disorders.

[19]  S M Pincus,et al.  Irregularity and asynchrony in biologic network signals. , 2000, Methods in enzymology.

[20]  A Schirdewan,et al.  Multiparametric Analysis of Heart Rate Variability Used for Risk Stratification Among Survivors of Acute Myocardial Infarction , 1998, Pacing and clinical electrophysiology : PACE.

[21]  M. Engoren,et al.  Approximate entropy of respiratory rate and tidal volume during weaning from mechanical ventilation. , 1998, Critical care medicine.

[22]  C. Drake,et al.  Double-Blind, Placebo-Controlled Study of Single-Dose Metergoline in Depressed Patients With Seasonal Affective Disorder , 2002, Journal of clinical psychopharmacology.

[23]  J. Kurths,et al.  The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death. , 1996, Cardiovascular research.

[24]  Hans A. Braun,et al.  Consequences of deterministic and random dynamics for the course of affective disorders , 1999, Biological Psychiatry.

[25]  V. Yeragani,et al.  Effect of age on long-term heart rate variability. , 1997, Cardiovascular research.

[26]  M. J. Woyshville,et al.  On the meaning and measurement of affective instability: clues from chaos theory , 1999, Biological Psychiatry.

[27]  R Balon,et al.  Fractal dimension of heart rate time series: an effective measure of autonomic function. , 1993, Journal of applied physiology.