Heart rate variability analysis during central hypovolemia using wavelet transformation

Abstract

Detection of hypovolemia prior to overt hemodynamic decompensation remains an elusive goal in the treatment of critically injured patients in both civilian and combat settings. Monitoring of heart rate variability has been advocated as a potential means to monitor the rapid changes in the physiological state of hemorrhaging patients, with the most popular methods involving calculation of the R-R interval signal's power spectral density (PSD) or use of fractal dimensions (FD). However, the latter method poses technical challenges, while the former is best suited to stationary signals rather than the non-stationary R-R interval. Both approaches are also limited by high inter- and intra-individual variability, a serious issue when applying these indices to the clinical setting. We propose an approach which applies the discrete wavelet transform (DWT) to the R-R interval signal to extract information at both 500 and 125 Hz sampling rates. The utility of machine learning models based on these features were tested in assessing electrocardiogram signals from volunteers subjected to lower body negative pressure induced central hypovolemia as a surrogate of hemorrhage. These machine learning models based on DWT features were compared against those based on the traditional PSD and FD, at both sampling rates and their performance was evaluated based on leave-one-subject-out fold cross-validation. Results demonstrate that the proposed DWT-based model outperforms individual PSD and FD methods as well as the combination of these two traditional methods at both sample rates of 500 Hz (p value <0.0001) and 125 Hz (p value <0.0001) in detecting the degree of hypovolemia. These findings indicate the potential of the proposed DWT approach in monitoring the physiological changes caused by hemorrhage. The speed and relatively low computational costs in deriving these features may make it particularly suited for implementation in portable devices for remote monitoring.

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A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation

“…Central hypovolemia was simulated in the subjects by introducing increasing negative pressure to their lower body, performed using an LBNP chamber [ 9 ]. In this experiment, each subject is positioned such that the lower half of their body is inside the chamber while the upper half of the body is attached to multiple sensors that collect continuous physiological signals during the experiment.…”

Blood loss severity prediction using game theoretic based feature selection

“…Further details on the HRV analysis can be found in one of our previous works [6]. Furthermore, several more features are extracted from the raw ECG signal by transforming it using Dual-Tree-Complex-Wavelet transform, in which in addition to all the statistical features, features such as complexity, mobility as well as information theoretic based KL distance are also extracted.…”

“…There is a need for such a system since available physiological signals from the patient such as heart rate, oxygen saturation, arterial blood pressure and arterial hemoglobin does not reveal any early signs of hemorrhage until the onset of cardiovascular decompensation 1 A. Razi is with the Department of Electrical and Computer Engineering, [5], by which time it could be too late. In our previous work we successfully developed a variety of signal processing and machine learning based systems which extracted features from heart rate variability (HRV) [6], morphology of electrocardiogram (ECG) [7], and from other physiological signals [8]. These systems utilized the variety of features and bio-markers that were extracted from these signals to predict the severity of hemorrhage.…”