Heart period

Data analysis. We use a combination of two movement detection algorithms (23) written in Madab environment (The MathWorks, Inc.) to accurately track movement of the heart edges see Note 10). Measurements of diastolic and systolic diameters as well as diastolic and systolic intervals, % fractional shortening (% PS), arrhythmicity index, and contraction direction and velocity are obtained as output from this analysis program. Heart rate is calculated as the inverse of the heart period where one period corresponds to a single diastolic interval plus subsequent systolic interval. % FS is quantified based on diameter measurements and is calculated as ((diastolic diameter-systolic diameter)/diastolic diameter) x 100 (%). Heart beat rhythmicity is quantified based on the standard deviation of the mean heart period normalized to the median heart period for each fly providing a dimensionless arrhythmicity index. 

Katona P.G., Barnett G.O. and lackson W.D. 1967. Computer simulation of the blood pressure control of the heart period. In P. Kezdi (Ed.), Baroreceptors and Hypertension, pp. 191-199, Oxford, Pergamon Press. 

Metalis, S. A. (1991). Heart period as a useful index of pilot workload in commercial transport aircraft. The IntemationalJoumal of Aviation Psychology, 1, 107-116. 

A multivariate approach of analyzing the psychophysiological measures obtained from heart period has been proposed as a method for obtaining cardiac autonomic information (Backs, 1995,1998). The multivariate approach attempts to improve the sensitivity and diagnosticity of heart rate by identifying the neurogenic activity of the sympathetic and parasympathetic nervous systems responsible for the observed heart rate in a task. Principal components analysis (PCA) was used in the present study to extract information about the sympathetic and parasympathetic nervous systems common to RSA, low-frequency HRV, residual heart period, and heart period. Details of how the components were derived are presented in the Method section. 

PCA was used to extraet information about the sympathetic and parasympathetic nervous systems eommon aeross the four cardiovascular measures obtained with heart period. A correlation matrix for heart period, RSA, low-frequeney HRV, and residual heart period was eomputed for each task and baseline and then pooled to yield a single correlation matrix. PCA was performed on the pooled eorrelation matrix. One eomponent was expeeted to be defined by the marker variable for sympathetic activity (residual heart period) and the other was expeeted to be defined by the marker variable for parasympathetic activity (RSA). The first two components were rotated to simple strueture using varimax. The eomponent seores for a partieular subjeet in a task were computed as the weighted sum of the subjeef s standardized score for each cardiovascular measure multiplied by the standardized seoring eoeffieient. 

Differenee seores from baseline-to-task were computed for the PCA components, PEP, and RSA. A positive difference score means that a task elicited increased heart period (i.e., slower heart rate) that could be caused by sympathetic withdrawal and/or parasympathetic activation. A negative difference score means that a task elieited deereased heart period (i.e., faster heart... 

The Rotated Factor Patterns and Standardized Scoring Coefficients f[Pg.166]

The autonomic space created by the PCA components was consistent with the autonomic space created by PEP and RSA for the exercise tasks (see Fig. 7.1). Both the PCA component (7 [2, 18] = 86.32 and 217.91, p <. 001) and the PEP and RSA P- [2, 18] = 82.82 and 185.74, p <. 001) vectors were significantly different from the origin for low and high intensity exercise. In addition, heart period, the sympathetic and parasympathetic components, PEP, and RSA were each significantly different from baseline for the two exercise tasks (Table 7.3). The vectors indicate that the exercise tasks elicited reciprocally coupled sympathetic activation and parasympathetic withdrawal. This consistency between vectors indicates that the PCA component scores are valid indicators of cardiac autonomic information for the exercise task. 

The autonomic space created by the PCA components was consistent with the autonomic space created by PEP and RSA in the illusion task and in the low memory load condition of the memory task (see Pig. 7.5). For these tasks, however, neither vector was significantly different from the origin, and heart period, the PCA components, PEP, and RSA were not significantly different from base-... 

Mean and Standard Deviation Difference Scores for Heart Period, the Autonomic Components, PEP, and RSA for the 10 Tasks... 

Finally, the only task for which the autonomic spaces created by the PCA components and PEP and RSA were not consistent was the cold pressor (see Fig. 7.6). Although heart period was significantly shorter than baseline (i.e., faster heart rate, see Table 7.3), only the PCA component vector was signifi-... 

The goal of the present study was to examine the validity of PCA of multiple psychophysiological measures computed tiom heart period for use in mental workload assessment. Validity of the PCA components was examined in two ways (a) by comparing the autonomic space created tiom the PCA component scores to that ereated from PEP and RSA and (b) by the correlations between the sympathetic component and PEP and between the parasympathetic component and RSA. Both approaches provided some support for PCA component validity. 

Comparison of the autonomic space derived from the sympathetic and parasympathetic components to that derived fiom PEP and RSA suggests that the PCA components were valid for all task conditions except the cold pressor (Table 7.4). The discrepancy for the cold pressor task illustrates the disadvantage of using residual heart period as the sympathetic marker variable. Residual heart period is the variance in heart period that is unaccounted for by RSA and is affected by all other fectors that determine heart period. Eor cognitive tasks like memory and tracking, where the primary determinants of heart period are probably neurogenic in origin, the PCA components led to the same conclusions as PEP and RSA. However, for tasks like the cold pressor, where heart period may be primarily determined by responses to vasoconstriction, the PCA components dissociate from PEP and RSA. 

Changes in autonomic space must be consistent with changes in heart period. Comparison of the autonomic space derived fiom the sympathetic and parasympathetic components to heart period change fiom resting baseline also suggests that the PCA components were valid (Table 7.4). All task conditions. 

Consistencies (+) and Inconsistencies (—) Between the PCA Components and PEP and RSA, Heart Period, and the Within-Task Correlations... 

The flow is pulsatile, with a time history containing major frequency components up to the eighth harmonic of the heart period. 

The energy in the heart period power spectrum between 0.04 and 0.15 Hz. 

Keywords— heart period, heart rate, heart rate variability,... 

Heart rate variability (HRV) is a measure of variations in heart rate. The time intervals between consecutive heart beats are measured through electrocardiogram (ECG) from the R wave to the next R wave, and they are conventionally named RR intervals (refer to Figure 1). These RR intervals is also known as heart period and the inversed of the intervals is known as the heart rate [1]. Analysis of HRV has become an important tool in detection of cardiac and other diseases, as it is non-invasive and provides prognostic information in patients [2],... 

Transform (FFT) analysis or by autoregressive modern techniques. However, the processing of HRV and their analysis in frequency domain are not straight forward. The RR series must be first submitted to preprocessing procedures to produce a series of equidistantly sampled data suitable for spectral analysis. There are various methods to quantily the HRV and it can be derived from either heart period or heart rate. These signal have the same informative content, but the results obtained from each have shown considerable discrepancies, in as much as the relationship between them is non linear [1]. 

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