Wavelet based analysis of physiological signals for prediction of severity of hemorrhagic shock

Soo Yeon Ji, Wenan Chen, Kevin Ward, Caroline Alice Rickards, Kathy Ryan, Victor Convertino, Kayvan Najarian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Rapid detection and treatment of hemorrhagic injuries are important factors in decreasing mortality in the battlefield and civilian trauma settings. In this study, novel features based on discrete wavelet transformation (DWT) were used to analyze physiological signals for prediction of central hypovolemia severity in humans. These features were defined based on approximate and detailed DWT coefficients extracted from physiological signals such as the electrocardiogram (ECG), arterial blood pressure (ABP), and thoracic impedance (IZT and DZT) signals, collected on healthy humans exposed to a hemorrhage model called lower body negative pressure (LBNP). The LBNP protocol consisted of applying 0, -15, -30, -45, -60, -70 mm Hg pressure to the lower half of the body, for 5 minutes at each stage. These LBNP levels were divided into three classes: mild, moderate, and severe. Machine learning algorithms were applied to predict the severity of blood loss based on the features extracted from the physiological signals. One of the objectives of this study was to compare the utility of using multiple physiological signals in prediction of the severity of hypovolemia as opposed to only using ECG. The classification results indicate that SVM has the highest accuracy at 82%. SVM's average precision and recall for all three classes are 79.2% and 79.8%, respectively. This shows that the wavelet-based method using multiple signals has the ability of rapidly determining the degree of volume loss, providing a potential tool for real-time remote triage and decision making in victims of trauma.

Original languageEnglish
Title of host publication2009 ICME International Conference on Complex Medical Engineering, CME 2009
DOIs
StatePublished - 23 Jul 2009
Event2009 ICME International Conference on Complex Medical Engineering, CME 2009 - Tempe, AZ, United States
Duration: 9 Apr 200911 Apr 2009

Publication series

Name2009 ICME International Conference on Complex Medical Engineering, CME 2009

Other

Other2009 ICME International Conference on Complex Medical Engineering, CME 2009
CountryUnited States
CityTempe, AZ
Period9/04/0911/04/09

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    Ji, S. Y., Chen, W., Ward, K., Rickards, C. A., Ryan, K., Convertino, V., & Najarian, K. (2009). Wavelet based analysis of physiological signals for prediction of severity of hemorrhagic shock. In 2009 ICME International Conference on Complex Medical Engineering, CME 2009 [4906672] (2009 ICME International Conference on Complex Medical Engineering, CME 2009). https://doi.org/10.1109/ICCME.2009.4906672