University of Queensland vital signs dataset: development of an accessible repository of anesthesia patient monitoring data for research

Data, Big Data, and Metadata in Anesthesiology

The last decade has seen an explosion in the growth of digital data. Since 2005, the total amount of digital data created or replicated on all platforms and devices has been doubling every 2 years, from an estimated 132 exabytes (132 billion gigabytes) in 2005 to 4.4 zettabytes (4.4 trillion gigabytes) in 2013, and a projected 44 zettabytes (44 trillion gigabytes) in 2020. This growth has been driven in large part by the rise of social media along with more powerful and connected mobile devices, with an estimated 75% of information in the digital universe generated by individuals rather than entities. Transactions and communications including payments, instant messages, Web searches, social media updates, and online posts are all becoming part of a vast pool of data that live "in the cloud" on clusters of servers located in remote data centers. The amount of accumulating data has become so large that it has given rise to the term Big Data. In many ways, Big Data is just a buzzword, a phrase that is often misunderstood and misused to describe any sort of data, no matter the size or complexity. However, there is truth to the assertion that some data sets truly require new management and analysis techniques.

Design of a real-time and continua-based framework for care guideline recommendations

Telehealth is an important issue in the medical and healthcare domains. Although a number of systems have been developed to meet the demands of emerging telehealth services, the following problems still remain to be addressed: (1) most systems do not monitor/predict the vital signs states so that they are able to send alarms to caregivers in real-time; (2) most systems do not focus on reducing the amount of work that caregivers need to do, and provide patients with remote care; and (3) most systems do not recommend guidelines for caregivers. This study thus proposes a framework for a real-time and Continua-based Care Guideline Recommendation System (Cagurs) which utilizes mobile device platforms to provide caregivers of chronic patients with real-time care guideline recommendations, and that enables vital signs data to be transmitted between different devices automatically, using the Continua standard. Moreover, the proposed system adopts the episode mining approach to monitor/predict anomalous conditions of patients, and then offers related recommended care guidelines to caregivers so that they can offer preventive care in a timely manner.

Estimating blood pressure using Windkessel model on Photoplethysmogram

Simple and non-invasive methods to estimate vital signs are very important for preventive healthcare. In this paper, we present a methodology to estimate Blood Pressure (BP) using Photoplethysmography (PPG). Instead of directly relating systolic and diastolic BP values with PPG features, our proposed methodology initially maps PPG features with some person specific intermediate latent parameters and later derives BP values from them. The 2-Element Windkessel model has been considered in the current context to estimate total peripheral resistance and arterial compliance of a person using PPG features, followed by linear regression for simulating arterial blood pressure. Experimental results, performed on a standard hospital dataset yielded absolute errors of 0.78±13.1 mmHg and 0.59 ± 10.23 mmHg for systolic and diastolic BP values respectively. Results also indicate that the methodology is more robust than the standard methodologies that directly estimate BP values from PPG signal.

Two open access, high-quality datasets from anesthetic records

OBJECTIVE

To provide a set of high-quality time-series physiologic and event data from anesthetic cases formatted in an easy-to-use structure.

MATERIALS AND METHODS

With ethics committee approval, data from surgical operations under general anesthesia were collected, including physiologic data, drug administrations, events, and clinicians' comments. These data were de-identified, formatted in a combined CSV/XML structure and made publicly available.

RESULTS

Two separate datasets were collected containing physiologic time-series data and time-stamped events for 34 patients. For 20 patients, the data included 400 physiologic signals collected over 20 h, 274 events, and 597 drug administrations. For 14 patients, the data included 23 physiologic signals collected over 69 h, with 286 time stamped comments.

DISCUSSION

Data reuse potentially saves significant time and financial costs. However, there are few high-quality repositories for accessible physiologic data and clinical interventions from surgical cases. De-identifying records assists with overcoming problems of privacy and storing the data in a format which is easily manipulated with computing resources facilitates access by the wider research community. It is hoped that additional high-quality data will be added. Future work includes developing tools to explore and visualize the data more efficiently, and establishing quality control measures.

CONCLUSION

An approach to collecting and storing high-quality datasets from surgical operations under anesthesia such that they can be easily accessed by others for use in research has been demonstrated.