A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation

• Published in: PloS one Vol. 11; no. 2; p. e0148544
• Main Authors: Belle, Ashwin, Ansari, Sardar, Spadafore, Maxwell, Convertino, Victor A., Ward, Kevin R., Derksen, Harm, Najarian, Kayvan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.02.2016; Public Library of Science (PLoS)
• Subjects: Bioindicators; Biology and Life Sciences; Biomarkers; Biomarkers - analysis; Biometrics; Blood; Blood Pressure; Care and treatment; Casualties; Computer and Information Sciences; Computer simulation; Critical care; Data processing; Diagnosis; Early Diagnosis; EKG; Electrocardiography; Electrocardiography - statistics & numerical data; Emergency medical care; Engineering and Technology; Healthy Volunteers; Heart diseases; Heart failure; Heart Failure - diagnosis; Heart Failure - physiopathology; Heart Rate; Hemodynamics; Humans; Hypovolemia; Hypovolemia - diagnosis; Hypovolemia - physiopathology; Illnesses; Information processing; Instability; International conferences; Measurement; Medicine; Medicine and Health Sciences; Monitoring; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; Mortality; Patient-Specific Modeling - statistics & numerical data; Physical Sciences; Physiological aspects; Physiology; R&D; Research & development; Research and Analysis Methods; Signal processing; Stability; Stability analysis; Support Vector Machine; Trauma; Vital signs; Waveforms; Wavelet transforms; United States; Ann Arbor Michigan; United States > US; Michigan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0148544

Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician's disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability.