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Continuous Monitoring of Physiological Signals Christopher G. Wilson, Ph.D . Departments of Pediatrics and Neurosciences. Critical Care Bioinformatics Workshop Sept 26th, 2009. Disclosures…. Outline. Continuous sampling as a logistical problem Nuts and bolts of sampling
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Continuous Monitoring of Physiological Signals Christopher G. Wilson, Ph.D. Departments of Pediatrics and Neurosciences Critical Care Bioinformatics Workshop Sept 26th, 2009
Outline • Continuous sampling as a logistical problem • Nuts and bolts of sampling • Data takes up space! • On-line versus off-line analysis • Organizing multiple data files from the same patient • Datafarming
Why collect all that data? • Changes in physiological signals indicate patient state (duh!) • Without a sufficient “window” of data, you will miss changes in patient state • Currently, staff only “acquires” charting data once every hour or so… • Retaining a “superset” of patient data allows for more comprehensive post-hoc data mining for pathophysiologies • Potential for improved standard of care
Nyquist-Shannon “Criterion” • The Nyquist–Shannon sampling theorem is a fundamental result in the field of information theory, in particular telecommunications and signal processing. Sampling is the process of converting a signal (for example, a function of continuous time or space) into a numeric sequence (a function of discrete time or space). The theorem states: • If a function x(t) contains no frequencies higher than B hertz, it is completely determined by giving its ordinates at a series of points spaced 1/(2B) seconds apart. • This means that a bandlimited analog signal that has been digitally sampled can be perfectly reconstructed from a sequence of samples if the sampling rate exceeds 2×B samples per second, where B is the highest frequency of interest contained in the original signal.
All that data adds up! • Storage space required = (# of channels) × (sampling rate) × (recording time) • If we record respiration, ECG, and Pulse-Ox at a very slow sampling rate (50 samples per second). • And four channels of EEG (1000 samples per second). • Over 12 hours of continuous monitoring we would collect ~200 Megabytes of data for a single patient!
Long-term Data Storage • Luckily disk storage is now very cheap (approximately $100/Terabyte). • However, with 100s of patients in the hospital per year, even with only a few hours of limited recording per patient, the data will become prohibitive to manage locally. • Computer operating systems that can handle large datasets in memory have only recently become more common (32 bit versus 64 bit).
Neonatal Desaturation Dataset • “High-res” pulse-oximetry data: 2 second average, 0.5 samples/sec. • Desaturation events must < 80% and be ≥ 10 seconds in duration. • We only use 24 hour days that have < 2 hours of missing data. • Missing SaO2 data points are flagged with a “non-event” value. • Values that are clearly “unrealistic” (equipment malfunction, removal of pulse-ox) are flagged and ignored through scripted data filtering. • We use multiple analysis algorithms on the same set of data to extract both linear and non-linear information.
Artifact sources • Patient moves, dislodging the finger cuff • Patient is moved by transport to another location • Equipment malfunction • Movement artifact • These sources of artifact can happen with any signal source!
Integrating the data (II) n + D n
“On-line” versus “Off-line” • Things we can do on-line • Time-series plots which can include: • Raw data over time • Averaged data (“trending”) • Qualitative dynamics • Poincaré return maps • “Windowed” FFTs • Things we will need to do off-line • ApEn, DFA, etc.
Organizing Multiple Data Sources: Our Database • Integrates all data records obtained for each subject/patient. • The backend is MySQL based (Open Source but very well supported with commercial options for “high-level” support). • Available at mysql.org • Using an ODBC (open database connectivity) compatible client (MS Access), we have developed a graphical front-end for data access and management. • The database is easily extended using graphical development tools.
Summary • Long-term patient data acquisition can be done now. • This is possible due to relatively inexpensive data storage and acquisition hardware. • Currently, the majority of our data “digestion” and analysis is done off-line, post-hoc. • Management of collected data using widely available database software allows integration of patient records and high-resolution waveform and imaging data. • A remaining challenge is long-term off-site storage of patient data in secure data centers and “open-access” standards across health care institutions.
Acknowledgements • Kenneth Loparo, PhD • Ryan Foglyano, BME • FarhadKaffashi, PhD • Julie DiFiore, BME • Jordan Holton, BME (major) • Bryan Kehoe, Nihon Kohden, USA Our website: http://www.case.edu/med/bioinformatics/
