Predictability of baroreflex sensitivity induced by phenylephrine injection via frequency domain indices computed from heart rate and systolic blood pressure signals during deep breathing

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Abstract

Diminished baroreflex sensitivity (BRS) is related to increased risk of sudden cardiac death in myocardial infarction patients and can be used as an indicator for risk level. The BRS is traditionally estimated invasively using vasoactive drugs, such as phenylephrine injection. This method has been widely accepted as a standard in clinical research. Due to its clinical importance, alternative BRS assessment methods have been investigated over the years to eliminate the use of drugs. In this study, the BRS obtained by drug-based (pharmacological) assessment was predicted from a subset of non-pharmacological indices computed from heart rate and systolic pressure signals. In the first phase of a two-phase experimental paradigm, 16 subjects were asked to perform two deep breathings with a 2-min delay in between. In the second phase, the BRS was measured by phenylephrine injection. Indices computed from the first phase describing the spectral and time domain properties of the heart rate and systolic pressure signals were used as predictors to estimate the pharmacological BRS of each subject. In addition to individual spectrum of beat-to-beat interval and systolic blood pressure, indices from cross-spectrum were also computed and evaluated as predictors. A leave one out method was employed to estimate the generalization capacity of the system and explore subset of indices, which gives the highest correlation between pharmacological and predicted BRS. Two predictors provided the highest correlation (r = 0.87, p = 1.16 × 10−5) with pharmacological BRS. The algorithm selected consistently normalized cross-power about the Mayer frequency and average magnitude square coherence in the high frequency band as predictors. These results indicate that the pharmacological BRS can be estimated from the combination of non-pharmacological spectral indices computed from beat-to-beat interval and systolic blood pressure signals obtained during deep breathing and therefore may eliminate the use of drugs.

Introduction

It is known that baroreflex sensitivity (BRS) is the amount of change in beat-to-beat interval (RR) against a 1 mm Hg systolic blood pressure deviation [1], [2]. BRS is traditionally measured by bolus injection of vasoactive drugs such as phenylephrine which causes increase in systolic blood pressure (SBP). The increase (decrease) in SBP tempts an increase (decrease) in the corresponding RR. A linear regression is build between mutually varying RR and SBP segments right after the drug injection. The slope of the regression line is recognized as BRS [1]. Since it requires drug injection, this method is classified as a pharmacological method. In the rest of the manuscript this method will be referred to as p-BRS. The clinical importance of BRS has been emphasized by several researches and it is reported that BRS indicates increased risk of sudden cardiac death in myocardial infarction patients and can be used for diagnosis and management of the therapy [3], [4], [5], [6].

Alternative BRS assessment methods, which eliminate the use of drugs, have been introduced over the years. These techniques are classified as non-pharmacological methods. A brief summary of these methods can be found in [2]. Let us here shortly summarize recent findings. The researchers have usually studied the individual correlations between p-BRS and non-pharmacological methods. In [7] the correlation between p-BRS and the transfer function (TF) BRS assessment method was investigated. The authors reported a correlation level of 0.94 in eight subjects. In another study, this relation was investigated with a larger subject database and the correlation between p-BRS and TF has been reported as low as 0.55 [8]. The authors concluded that non-pharmacological methods should not be used in clinical practice as a replacement of p-BRS [8]. In [9] it is argued that p-BRS and non-pharmacological methods should be considered as complementary methods rather than alternative methods since they contain distinct information. Apart from these studies, Davies et al. indicate that BRS can be estimated from RR interval oscillation amplitude at about 0.1 Hz, which is achieved by controlled breathing at this frequency [10]. They report a correlation of 0.81 between the RR oscillation amplitude at 0.1 Hz and BRS index.

The traditional approaches generally investigated the predictive power of individual features. To our knowledge the efficiency of combination of indices in estimating p-BRS was not investigated. In this work, the prediction of p-BRS was achieved from a combination of non-pharmacological indices (predictor features). As a first step, a large pool of indices was extracted from electrocardiogram (ECG) and blood pressure (BP) data which were recorded during deep breathing. The non-pharmacological indices were computed in time and frequency domains using traditional techniques proposed in the past. In addition, a novel frequency domain feature was introduced to estimate the p-BRS values of the second phase. This feature was appended to the set of non-pharmacological indices. In order to eliminate model over fitting, a subset of these time and frequency domain indices were selected to predict the p-BRS. The prediction performance was estimated with a leave one subject out procedure.

The rest of the paper is organized as follows. In the next section, we detail our data acquisition system and experimental paradigm that recorded ECG and BP data. In the following section, we describe our predictors and explain how they are computed from time and frequency domain data. Finally, we provide experimental results and conclude.

Section snippets

Acquisition and pre-processing of electrocardiogram and blood pressure data

Electrocardiogram and BP signals were obtained from 21 subjects (10 male, 11 female) who underwent coronary angiography at Balcali Hospital of Çukurova University, Turkey. The ages of subjects ranged between 36 and 62 with a mean of 49.9 and a standard deviation of 6.5. All subjects had normal coronary arteries and provided informed consent for participating in the study. The study was approved by the local ethics committee. The ECG and BP data were acquired simultaneously with a custom system

Results

Two predictors provided the highest prediction accuracy of 81.6%. The prediction accuracy versus the number of predictors is given in Fig. 3. The correlation between predicted BRS and p-BRS was 0.87 (p = 1.16 × 10−5). A scatter plot of the predicted BRS with p-BRS is shown in Fig. 4. During the leave one out steps the algorithm chose the same indices for all subjects consistently: normalized cross-power about Mayer frequency proposed by the authors and the average magnitude square coherence in HF

Conclusions

Diminished BRS is linked to severe cardiac problems in humans including increased risk of sudden cardiac death in myocardial infarction patients and ventricular arrhythmias [3], [4], [5], [6]. Owing to its value, measurement of BRS by internally forcing a variation in BP via drugs (p-BRS) is a widely accepted method in clinical practice. In this study, we showed that two spectral indices computed from SBP and RR time series during deep breathing can be used to predict the p-BRS with a

Acknowledgement

This study was supported by the Turkish Scientific and Research Council (TUBITAK). We thank Meryem Burduroğlu and Maggie Spaniol for proofreading the manuscript.

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