A real-time decision-support system predicts hemorrhagic shock of a patient by analysis of electrocardiogram (ECG) signals and transcranial Doppler (TCD) signals from the patient. These signals are subject to signal decomposition using Discrete Wavelet Transform (DWT) to sets of wavelet coefficients and selecting significant signal features. Machine learning is applied to the significant features to evaluate and classify hypovolemia severity based on the input ECG and TCD signals from the patient. The classification of blood loss severity is displayed in real-time. An extension of the decision-support system integrates Arterial Blood Pressure (ABP) signals and thoracic electrical bio-impedance (DZT) signals with the ECG and TCD signals from the patient to evaluate severity of hypovolemia.
Combining predictive capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to predict hemorrhagic shock