Abstract P93: Heart Rate Variability Analysis using Wavelet Transform to Predict Hemorrhage Severity
Introduction: The Pulse Initiative on resuscitation identified the need to develop biosensing for detection of critical limitations of blood flow. The ability to rapidly detect the severity of hemorrhage based on heart rate has been limited. Use of heart rate variability (HRV) is problematic. We used a number of new defined ECG features based on discrete wavelet transformation (DWT) that may be used to estimate blood loss severity. The features are defined based on the energy of detail coefficients of Daubecies DWT.
Methods: The performance of DWT was tested using ECG data from a human model of hemorrhage using lower body negative pressure (LBNP). LBNP consisted of a 5-minute rest period (0 mm Hg) followed by 5 minutes of chamber decompression of the lower body to −15, −30, −45, and −60 mm Hg and additional increments of −10 mm Hg every 5 minutes until the onset of cardiovascular collapse. These levels were divided into 3 classes (mild: −15 to −30 mmHg; moderate: −45 to −60 mmHg; severe: over −60 mmHg). These levels correspond to estimated blood losses of 400 –550 cc, 500 –1000 cc and greater than 1000 cc respectively. The ECG DWT features of subjects were used for classification of each ECG recording during volume loss levels. Before classification in order to eliminate redundancy among the features, principal component analysis is applied to the feature set. Machine learning algorithms (SVM, AdaBoost, C4.5) were then applied to analyze the processed features and predict the severity of blood loss.
Results: A 219 sample set was used to classify groups by using machine learning algorithms with 10-fold cross validation. C4.5 outperformed other algorithms with a prediction accuracy of 74.4%. The average precision and recall (sensitivity) for the three classes were 77.4% and 76.1%, respectively. In particular, 30 out of 39 cases in the severe class were correctly classified by C4.5. These results required sampling rates of only 125 Hz.
Conclusion: This is the first reported use of an ECG analysis method to classify volume loss. The DWT method described may have the ability to rapidly determine the degree of volume loss from hemorrhage providing for more rapid triage and decision making. This may be particularly helpful for remote monitoring of war fighters or for mass casualty care.