A novel system to collect dual pulse oximetry data for critical congenital heart disease screening research
Kavish Doshi1, Gregory B Rehm1, Pranjali Vadlaputi2
1Department of Computer Science, University of California, Davis, Davis, CA, USA.
Insights
This study introduces a low-cost system for real-time monitoring of neonatal perfusion and oxygen saturation. The technology enhances early detection of critical congenital heart disease (CCHD), improving diagnostic accuracy for newborns.
Area of Science:
- Biomedical Engineering
- Neonatal Medicine
- Data Analytics
Background:
- Timely diagnosis of critical congenital heart disease (CCHD) in neonates is crucial for reducing mortality and morbidity.
- Current screening methods, primarily oxygen saturation analysis, miss approximately 900 CCHD cases annually in the USA.
- Integrating non-invasive perfusion measurements with oxygen saturation data can significantly improve CCHD diagnostic timeliness and accuracy, but real-time data interpretation remains a challenge.
Purpose of the Study:
- To develop and implement a cost-effective hardware and software system for real-time collection and analysis of neonatal physiological data.
- To enhance the detection and diagnosis of critical congenital heart disease (CCHD) through improved data processing capabilities.
Main Methods:
- A Pi-top™ based system was engineered to collect, visualize, and store dual oxygen saturation, perfusion indices, and photoplethysmography data.
- Automated data aggregation and unique study identifiers were implemented for efficient data management and research facilitation.
Main Results:
- Data from 190 neonates (60 with CCHD, 130 without) were collected, totaling 1665 minutes of physiological information.
- The system successfully extracted non-invasive perfusion features, including perfusion index, radiofemoral delay, and waveform characteristics (slope of systolic rise/diastolic fall).
Conclusions:
- The developed data collection and waveform analysis system is inexpensive and feasible for clinical application.
- This approach offers a promising method to enhance future screening algorithms for critical congenital heart disease (CCHD) in neonates.
Introduction:
Access to patient medical data is critical to building a real-time data analytic pipeline for improving care providers' ability to detect, diagnose, and prognosticate diseases. Critical congenital heart disease (CCHD) is a common group of neonatal life-threatening defects that must be promptly diagnosed to minimize morbidity and mortality. CCHD can be diagnosed both prenatally and postnatally. However, despite current screening practices involving oxygen saturation analysis, timely diagnosis is missed in approximately 900 infants with CCHD annually in the USA and can benefit from increased data processing capabilities. Adding non-invasive perfusion measurements to oxygen saturation data can improve the timeliness and fidelity of CCHD diagnostics. However, real-time monitoring and interpretation of non-invasive perfusion data are currently limited.
Methods:
To address this challenge, we created a hardware and software architecture utilizing a Pi-top™ for collecting, visualizing, and storing dual oxygen saturation, perfusion indices, and photoplethysmography data. Data aggregation in our system is automated and all data files are coded with unique study identifiers to facilitate research purposes.
Results:
Using this system, we have collected data from 190 neonates, 130 presumably without and 60 with congenital heart disease, in total comprising 1665 min of information. From these data, we are able to extract non-invasive perfusion features such as perfusion index, radiofemoral delay, and slope of systolic rise or diastolic fall.
Conclusion:
This data collection and waveform analysis is relatively inexpensive and can be used to enhance future CCHD screening algorithms.
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