Combining predictive capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to predict hemorrhagic shock

摘要

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.

主权项

1. A real-time decision-support system for predicting hemorrhagic shock of a patient comprising:
means for receiving electrocardiogram (ECG) signals from the patient; means for receiving transcranial Doppler (TCD) signals from the patient; first bandpass filter for filtering the ECG signals; second bandpass filter for filtering the TCD signals; first means using Discrete Wavelet Transform (DWT) to decompose filtered ECG signals to generate a first set of wavelet coefficients and selecting significant signal features from the first set of wavelet coefficients; second means using DWT to decompose filtered TCD signals to generate a second set of wavelet coefficients and selecting significant signal features from the second set of wavelength coefficients; data processing means receiving significant signal features and, using machine learning, evaluating and classifying hypovolemia severity based on the selected significant signal features generated from the wavelet coefficients of input ECG and TCD signals from the patient; and a display for displaying a classification of blood loss severity.