A signal processing approach for detection of hemodynamic instability before decompensation

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Abstract

Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician's disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability.

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Belle, A., Ansari, S., Spadafore, M., Convertino, V. A., Ward, K. R., Derksen, H., & Najarian, K. (2016). A signal processing approach for detection of hemodynamic instability before decompensation. PLoS ONE, 11(2). https://doi.org/10.1371/journal.pone.0148544

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