Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage

Abstract

Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock.Method: In 42 (27 female) young [mean (sd): 24 (4) years], healthy subjects central blood volume (CBV) was progressively reduced by application of −50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0), initial phase of CBV reduction (class 1) or advanced CBV reduction (class 2). Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. Model features included: volumetric hemodynamic parameters (stroke volume and cardiac output), BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD) blood flow velocity. Model performance was tested by quantifying the predictions with three methods: sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates.Results: The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73–0.98 and class 2: 0.56–0.96). Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67) and specificity (0.87) was found for the model that contained the TCD cerebral blood flow velocity derived pulse height. The highest combination for class 2 was found for the model with the volumetric features (0.72 and 0.91).Conclusion: The most sensitive models for the detection of advanced CBV reduction comprised data that describe features from volumetric parameters and from cerebral blood flow velocity hemodynamics. In a validated model of hemorrhage in humans these parameters provide the best indication of the progression of central hypovolemia.

Detecting central hypovolemia in simulated hypovolemic shock by automated feature extraction with principal component analysis

“…The novel finding of this study is that automated feature extraction of the blood pressure waveform allows for tracking of cardiovascular events in response to ongoing depletion of the CBV up unto the point of cardiovascular collapse. We created 3 subclasses during LBNP to add additional precision to the modeled response in comparison with previous work (two classes during LBNP; van der Ster et al 2018). The achieved accuracy implies that these subclasses are poorly defined, or may not exist from a physiological point of view respectively vary between subjects.…”

“…These changes in the added components appear sufficiently sensitive to classify hypovolemia and distinguish between the predefined classes. Making use of principal components exposes new sensitive information and improves identification of the degree of central hypovolemia compared to multiparameter monitoring (van der Ster et al 2018).…”

“…The integral response of the cardiovascular system to progressing central hypovolemia from rest to the stage of hemodynamic instability and presyncope was quantified earlier with the use of either a support vector machine or a neural network trained on features assumed having a physiological and/or clinical meaning (Bennis et al 2017;van der Ster et al 2018). By design, the majority of these featuresincluding blood pressure and transcranial Doppler determined cerebral blood flow velocity derivativesare the result of considerable down-sampling of potentially sensitive features of arterial pressure and cerebral blood flow velocity waveforms.…”

“…By design, the majority of these featuresincluding blood pressure and transcranial Doppler determined cerebral blood flow velocity derivativesare the result of considerable down-sampling of potentially sensitive features of arterial pressure and cerebral blood flow velocity waveforms. However, both the arterial pressure and transcranial cerebral blood flow velocity waveforms contain subtle information on the cardio-cerebrovascular condition (van der Ster et al 2018). Principal component analysis (PCA) is a mathematical technique to identify a reduced number of uncorrelated features from a larger dataset.…”

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“…In this case, most commonly injured are articular ligaments, intramuscular tendons, tendon attachments. When applying loads only to the force direction, the process of adaptation by the cardiovascular and respiratory systems (Stoner, Matheson, Perry, Williams, McManus, Holdaway, ... Maiorana, 2017;van der Ster, Bennis, Delhaas, Westerhof, Stok, & van Lieshout, 2018). But these are the main systems for ensuring the normal level of human life.…”