Development of a platform to manipulate multimodal data to evaluate the hemodynamic state of the patient during cardiac arrest using nonivasive sensors
Creators: name: Zubia, Gorka
name: Irusta, Unai
Publisher: name: ADDI Repository of the University of the Basque Country UPV/EHU, 2021.
Year: 2021
Subjects: subject: Out-of-Hospital Cardiac Arrest
subject: Hemodynamic Monitoring
subject: Ballistocardiography
subject: Electrocardiography
subject: Cardiac Output
subject: Noninvasive Ventilation
subject: Emergency Medicine/instrumentation
subject: Emergency Medicine
subject: Defibrillators

description: Out-of-hospital cardiac arrest (OHCA) is a sudden cardiovascular event that represents a major global public health challenge and ranks among the leading causes of death worldwide. Because survival decreases rapidly with time, early recognition and prompt treatment are critical. While defibrillation and cardiopulmonary resuscitation (CPR) remain the cornerstones of OHCA therapy, current monitoring predominantly relies on the electrocardiogram (ECG) to assess the heart’s electrical activity. However, in many OHCA cases the ECG may appear nearly normal even when the heart’s hemodynamic performance—its blood flow and pressure—is critically impaired.

There is, therefore, a pressing need for a noninvasive, cost-effective, and reliable method for real-time hemodynamic monitoring that can be deployed in out-of-hospital settings. Cardiac output (CO) and stroke volume index (SVI) are key indicators of hemodynamic status, yet their measurement is currently limited to hospital environments using invasive techniques such as blood pressure monitoring and Doppler echocardiography. The primary objective of this thesis is to develop and implement tools that establish correlations between CO and SVI values—obtained via these well-established in-hospital methods—and measurements derived from novel noninvasive modalities, such as ballistocardiography (BCG), which are more readily applicable in OHCA scenarios.

The initial phase of the project involved collecting data from a cohort of healthy subjects using both conventional and innovative measurement technologies. In May 2021, data collection was extended to include real OHCA cases. To support the main objective, several intermediate goals were achieved, including the creation of a multisource, standardized open-format database that integrates raw data from various proprietary devices. This was accomplished through the application of existing data converters as well as the development of custom converters, with preliminary pseudo-synchronization of the data using a custom algorithm based on physicians’ temporal annotations.

Two user-friendly graphical interfaces were developed—one for each data collection site (in-hospital and out-of-hospital)—to facilitate visualization, annotation, and fine-tuning of time alignment within the multisource database. These interfaces incorporate algorithms designed to aid in data analysis and the identification of potential correlations among signals. Their development led to two significant outcomes: the collaborative extraction of analysis windows with clinicians at Oslo University Hospital and the establishment of a generalized method for graphical interface development. This method enabled the creation of a third interface dedicated to the analysis of the extracted windows, integrating an additional data processing algorithm.

In conclusion, this thesis presents a scalable platform—comprising a multisource, standardized open-format database and three integrated graphical interfaces—that supports the visualization, annotation, and processing of hemodynamic data. This framework lays the foundation for future work aimed at correlating traditional and novel monitoring technologies, ultimately enhancing real-time hemodynamic assessment and improving survival outcomes in OHCA patients managed by emergency medical services.