New technique for time series analysis combining the maximum entropy method and non-linear least squares method: its value in heart rate variability analysis

Abstract

A new technique for time series analysis, which is a combination of the maximum entropy method (MEM) for spectral analysis and the non-linear least squares method (LSM) for fitting analysis, is described. In this technique, the MEM power spectral density (MEMPSD) is calculated using a very large lag that could diminish the lag dependence of dominant periods estimated by the MEM analysis. The validity of this large lag is confirmed by the LSM, given that the ten dominant MEM periods are known quantities. To validate the MEM plus LSM technique, it is compared with autoregressive (AR) modelling, by analysing heart rate variability under pharmacological interventions (phenylephrine and trinitroglycerine), using 16 young males. The results indicate that the MEMPSD, when compared with the ARPSD, has numerous periods that could reproduce the original time series much more accurately, as revealed by the LSM analysis. However, both the low- and high-frequency powers with MEMPSD and ARPSDs shift in the expected directions in accordance with the pharmacological effects on the cardiovascular system. The implications of these results are discussed from the theoretical and practical standpoints of the MEM plus LSM technique, compared with AR modelling.

A Novel EEG Index for Evaluating the Sleep Quality in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome

“…In this study, the Maximum Entropy Method (MEM), one of the methods for frequency analysis, was used for calculation of the entropy. In the present study we used the MemCalc method, which is a combination of the MEM method and the non-linear squares method for fitting analysis (Sawada et al 1997), and is a reliable time-series analysis method (Pardey et al 1996;Terachi and Tanaka 2001;Ohisa et al 2008).…”

Correlation between heart rate variability and haemodynamic fluctuation during induction of general anaesthesia: comparison between linear and non‐linear analysis*

“…The MemCalc method, which is a relatively new method of estimating HRV, may be vulnerable to the criticism that it has not yet been validated. However, Sawada et al [12] have demonstrated the validity of MemCalc method compared with autoregressive modelling. Moreover, there have been reports investigating autonomic function with this new technique in the field of anaesthesia [23,24].…”

“…While frequency-domain measures of HRV, such as spectral analysis, focus on the status of cardiac autonomic activity, it is proposed that non-linear indices may provide insight into the overall structure of the heart rate regulating system. Although it was reported that pre-operative point dimension correlation (PD2), as a non-linear index of HRV, predicted hypotension after spinal anaesthesia [10], there is no previous report examining the correlation between non-linear index of HRV and circulatory response to induction of general anaesthesia.The MemCalc method, which is a combination of the maximum entropy method for spectral analysis and the non-linear least squares method for fitting analysis (Tarawa, Suwa Trust, Tokyo, Japan), has recently been developed [11,12]. This method enables us to estimate reliable HRV from a series of RR intervals over 30 s and recognises the abnormal RR interval of premature beats or artefacts, including noise, and removes it automatically.…”

Effects of olfactory stimulation by α-pinene on autonomic nervous activity

“…This device performs measurements using a 3-lead electrocardiogram (Lead II). The power levels of the low-frequency (LF: 0.04-0.15 Hz) and high-frequency (HF: 0.15-0.40 Hz) components of HRV were calculated using the maximum entropy method (MemCalc/Win; GMS, Japan) [15]. The HF power reflects parasympathetic nervous activity, which increases in the relaxed state.…”