Advanced bioimpedance signal processing techniques for hemodynamic monitoring during anesthesia
- Autores: Jesús Escrivá Muñoz
- Directores de la Tesis: Montserrat Vallverdú Ferrer (dir. tes.), Erik Weber (dir. tes.)
- Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2018
- Idioma: español
- Tribunal Calificador de la Tesis: Pere Caminal Magrans (presid.), Francisco Clarià Sancho (secret.), Xavier Borrat Frigola (voc.)
- Programa de doctorado: Programa de Doctorado en Ingeniería Biomédica por la Universidad Politécnica de Catalunya y la Universidad de Zaragoza
- Materias:
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- Resumen
Cardiac output (CO) defines the blood flow arriving from the heart to the different organs in the body and it is thus a primary determinant of global 02 transport. Cardiac output has traditionally been measured using invasive methods, whose risk sometimes exceeds the advantages of a cardiac output monitoring.
In this context, the minimization of risk in new noninvasive technologies for CO monitoring could translate into major advantages for clinicians, hospitals and patients: ease of usage and availability, reduced recovery time, and improved patient outcome. Impedance Cardiography (ICG) is a promising noninvasive technology for cardiac output monitoring but available information on the ICG signals is more scare than other physiological signals such as the electrocardiogram (ECG).
The present Doctoral Thesis contributes to the development of signal treatment techniques for the ICG in order to create an innovative hemodynamic monitor.
First, an extensive literature review is provided regarding the basics of the clinical background in which cardiac output monitoring is used and concerning the state of the art of cardiac output monitors on the market. This Doctoral Thesis has produced a considerable amount of clinical data which is also explained in detail. These clinical data are also useful to complement the theoretical explanation of patient indices such as heart rate variability, blood flow and blood pressure. In addition, a new method to create synthetic biomedical signals with known time-frequency characteristics is introduced.
One of the first analysis in this Doctoral Thesis studies the time difference between peak points of the heart beats in the ECG and the ICG: the RC segment. This RC segment is a measure of the time delay between electrical and mechanical activity of the heart.
The relationship of the RC segment with blood pressure and heart interval is analyzed. The concordance of beat durations of both the electrocardiogram and the impedance cardiogram is one of the key results to develop new artefact detection algorithms and the RC could also have an impact in describing the hemodynamics of a patient.
Time-frequency distributions (TFDs) are also used to characterize how the frequency content in impedance cardiography signals change with time. Since TFDs are calculated using concrete kernels, a new method to select the best kernel by using synthetic signals is presented. Optimized TFDs of ICG signals are then calculated to extract severa! features which are used to discriminate between different anesthesia states in patients undergoing surgery.
TFD-derived features are also used to describe the whole surgical operations. Relationships between TFD-derived features are analyzed and prediction models for cardiac output are designed. These prediction models prove that the TFD-derived features are related to the patients' cardiac output.
Finally, a validation study for the qCO monitor is presented. The qCO monitor has been designed using sorne of the techniques which are consequence of this Doctoral Thesis. The main outputs of this work have been protected with a patent which has already been filed.
As a conclusion, this Doctoral Thesis has produced a considerable amount of clinical data and a variety of analysis and processing techniques of impedance cardiography signals which have been included into commercial medical devices already available on the market.
