Technical description of the IBIS data library. Improved Monitoring for Brain Dysfunction in Intensive Care and Surgery

Information technology in critical care: review of monitoring and data acquisition systems for patient care and research

There is a broad consensus that 21st century health care will require intensive use of information technology to acquire and analyze data and then manage and disseminate information extracted from the data. No area is more data intensive than the intensive care unit. While there have been major improvements in intensive care monitoring, the medical industry, for the most part, has not incorporated many of the advances in computer science, biomedical engineering, signal processing, and mathematics that many other industries have embraced. Acquiring, synchronizing, integrating, and analyzing patient data remain frustratingly difficult because of incompatibilities among monitoring equipment, proprietary limitations from industry, and the absence of standard data formatting. In this paper, we will review the history of computers in the intensive care unit along with commonly used monitoring and data acquisition systems, both those commercially available and those being developed for research purposes.

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.

Using "off-the-shelf" tools for terabyte-scale waveform recording in intensive care: computer system design, database description and lessons learned

Until now, the creation of massive (long-term and multichannel) waveform databases in intensive care required an interdisciplinary team of clinicians, engineers and informaticians and, in most cases, also design-specific software and hardware development. Recently, several commercial software tools for waveform acquisition became available. Although commercial products and even turnkey systems are now being marketed as simple and effective, the performance of those solutions is not known. The additional expense upfront may be worthwhile if commercial software can eliminate the need for custom software and hardware systems and the associated investment in teams and development. We report the development of a computer system for long-term large-scale recording and storage of multichannel physiologic signals that was built using commercial solutions (software and hardware) and existing hospital IT infrastructure. Both numeric (1 Hz) and waveform (62.5-500 Hz) data were captured from 24 SICU bedside monitors simultaneously and stored in a file-based vital sign data bank (VSDB) during one-year period (total DB size is 4.21TB). In total, vital signs were recorded from 1,175 critically ill patients. Up to six ECG leads, all other monitored waveforms, and all monitored numeric data were recorded in most of the cases. We describe the details of building blocks of our system, provide description of three datasets exported from our VSDB and compare the contents of our VSDB with other available waveform databases. Finally, we summarize lessons learned during recording, storage, and pre-processing of physiologic signals.

Design and implementation of a portable physiologic data acquisition system

OBJECTIVE

To describe and report the reliability of a portable, laptop-based, real-time, continuous physiologic data acquisition system (PDAS) that allows for synchronous recording of physiologic data, clinical events, and event markers at the bedside for physiologic research studies in the intensive care unit.

DESIGN

Descriptive report of new research technology.

SETTING

Adult and pediatric intensive care units in three tertiary care academic hospitals.

PATIENTS

Sixty-four critically ill and injured patients were studied, including 34 adult (22 males and 12 females) and 30 pediatric (19 males and 11 females).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Data transmission errors during bench and field testing were measured. The PDAS was used in three separate research studies, by multiple users, and for repeated recordings of the same set of signals at various intervals for different lengths of time. Both parametric (1 Hz) and waveform (125-500 Hz) signals were recorded and analyzed. Details of the PDAS components are explained and examples are given from the three experimental physiology-based protocols. Waveform data include electrocardiogram, respiration, systemic arterial pressure (invasive and noninvasive), oxygen saturation, central venous pressure, pulmonary arterial pressure, left and right atrial pressures, intracranial pressure, and regional cerebral blood flow. Bench and field testing of the PDAS demonstrated excellent reliability with 100% accuracy and no data transmission errors. The key feature of simultaneously capturing physiologic signal data and clinical events (e.g., changes in mechanical ventilation, drug administration, clinical condition) is emphasized.

CONCLUSIONS

The PDAS provides a reliable tool to record physiologic signals and associated clinical events on a second-to-second basis and may serve as an important adjunctive research tool in designing and performing clinical physiologic studies in critical illness and injury.

Neurointensive care unit system for continuous electrophysiological monitoring with remote web-based review

There is a need in the neurological intensive care unit for a single integrated bedside monitor for continuously monitoring the function of the patient's central nervous system. In this paper, we demonstrate the feasibility of building such a system and operating it in the intensive care environment. We have developed a fully automated system that samples electrophysiological waveforms of various modalities according to a schedule of predefined intervals along with routinely monitored cardiac and respiratory parameters. The system provides stimulation and acquires responses without requiring supervision. The electrophysiological data include brainstem auditory and somatosensory evoked potentials and epochs of the electroencephalogram. The system applies peak detection and spectral analysis to extract salient parameters from the raw waveforms. The results are made available immediately in real time on the local network for local review and further analysis. A web-based interface makes review by a qualified neurologist possible anywhere within the hospital's secure intranet during and after monitoring. This system could potentially give an early warning of impending herniation, subclinical seizures, and brain or spinal cord ischemia. We demonstrate its application in a few diverse neurological intensive care cases and a case in the interventional neuroradiology suite.

Physiologic data acquisition system and database for the study of disease dynamics in the intensive care unit

OBJECTIVE

To describe a real-time, continuous physiologic data acquisition system for the study of disease dynamics in the intensive care unit.

DESIGN

Descriptive report.

SETTING

A 16-bed pediatric intensive care unit in a tertiary care children's hospital.

PATIENTS

A total of 170 critically ill or injured pediatric patients.

INTERVENTIONS

None.

MAIN OUTCOME MEASURES

None.

RESULTS

We describe a computerized data acquisition and analysis system for the study of critical illness and injury from the perspective of complex dynamic systems. Both parametric (1 Hz) and waveform (125-500 Hz) signals are recorded and analyzed. Waveform data include electrocardiogram, respiration, systemic arterial pressure (invasive and noninvasive), central venous pressure, pulmonary arterial pressure, left and right atrial pressures, intracranial pressure, body temperature, and oxygen saturation. Details of the system components are explained and examples are given from the resultant physiologic database of signal processing algorithms and signal analyses using linear and nonlinear metrics.

CONCLUSIONS

We have successfully developed a real-time, continuous physiologic data acquisition system that can capture, store, and archive data from pediatric intensive care unit patients for subsequent time series analysis of dynamic changes in physiologic state. The physiologic signal database generated from this system is available for analysis of dynamic changes caused by critical illness and injury.

The IBIS project: data collection in London. Improved Monitoring for Brain Dysfunction during Intensive Care and Surgery

The primary aim of the Improved Monitoring for Brain Dysfunction during Intensive Care and Surgery (IBIS) project was to create a unique and comprehensively annotated data library (DL) of multiple physiological, including neurophysiological, signals. Data collection was undertaken in Kuopio, Finland and London, UK, and comparable protocols were used at all the sites. In London, 43 patients were recruited at the Royal Brompton Hospital, followed by nine at St. Bartholomew's Hospital, all of whom underwent cardiac or combined cardiac and carotid artery surgery. Thirty-seven patients underwent a single operation, while 15 underwent two procedures. The protocols and equipment used, problems specific to the electrically hostile environment and preliminary results are described, including those of clinical interest. The DL is being used for the development of clinically applicable neurophysiological monitoring tools.

Exploring the IBIS data library contents: tools for data visualisation, (pre-) processing and screening. Improved Monitoring for Brain Dysfunction in Intensive Care and Surgery

During the IBIS project a high-quality data library of continuous and intermittent physiological signals and variables from patients during intensive care and surgery has been collected. To facilitate exploration of the full content of this data library a data browser was developed, which offers a flexible graphical display of the collection of multivariate data. To supplement the functionality of the display of the 'raw' data, a set of screening and pre-processing tools has been developed. A separate trend analysis tool offers a convenient overview of an entire recording focusing on the slow changes in the general state of the patient and the interaction between different physiological subsystems seen from a long-term perspective. A frequency analysis tool for processing the electroencephalography (EEG) signals has been integrated in the data browser to facilitate a quick screening of the cerebral function. The data library is the foundation of the development and validation of biosignal interpretation methods. This process can potentially be more productive using the described tool for algorithm prototyping based on a graphical network specifying the interaction between data processing primitives.