PhysioNet Introduction An introduction to PhysioNet the research resource for complex physiologic signals This slide show introduces PhysioNet, a resource for researchers who study physiologic signals and time series, with attention to PhysioNet's significance to the NIH and its mission. We hope it will help those who need or wish to understand what PhysioNet is. These slides may be revised from time to time, but the most recent version can always be found at http://physionet.org/NIH/.

PhysioNet Introduction What is PhysioNet? A unique web-based resource funded by NIH, intended to support current research and stimulate new investigations in the study of complex biomedical and physiologic signals. Three closely interdependent components: Data repository (PhysioBank) Library of related software (PhysioToolkit) Free-access website (physionet.org) This slide show introduces PhysioNet, a resource for researchers who study physiologic signals and time series, with attention to PhysioNet's significance to the NIH and its mission. We hope it will help those who need or wish to understand what PhysioNet is. These slides may be revised from time to time, but the most recent version can always be found at http://physionet.org/NIH/.

PhysioNet Introduction Why Study Signals? Physiologic signals and time series reveal aspects of health, disease, biotoxicity and aging not captured by static measures. Raw (original) signals are of increasing interest as means of developing new biomarkers, of measuring parameters of known interest, and also for developing new insights into basic mechanisms of human physiology. The signals shown are a two-second snippet of an electrocardiogram (ECG) and a simultaneously recorded continuous blood pressure signal. The blue markers are annotations that mark the heart beats. Even this small excerpt contains a wealth of information that is not usually given attention; for example, the time interval from the electrical activation of the ventricles (marked by the annotations) to the peak of the blood pressure signal varies from beat to beat, reflecting the degree of filling of the ventricles, their contractility, the pulse wave velocity and its modulation by arterial compliance and peripheral resistance, and much more. A thorough analysis of these variables requires more and longer signals.

Resource Established September,1999 PhysioNet Introduction Resource Established September,1999 Founded under auspices of NCRR (1999-2007). Now supported by NIBIB and NIGMS (2007-2012) under Cooperative Agreement U01EB008577 PhysioNet was founded in 1999 under the auspices of the National Center for Research Resources (NCRR) of the NIH, as part of their P41 Biotechnology program. Beginning in 2007, PhysioNet has been jointly funded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the National Institute of General Medical Sciences (NIGMS). PhysioNet’s primary investigators and staff are at Boston’s Beth Israel Deaconess Medical Center/Harvard Medical School and at the Massachusetts Institute of Technology (MIT).

Design of the PhysioNet Website PhysioNet Introduction Design of the PhysioNet Website Scientific Community-at-Large PhysioNet Gateway to the Resource PhysioBank Archive of Physiologic Signals and Time Series PhysioToolkit Open Source Software For Data Analysis The web site comprises PhysioBank, the data archive, PhysioToolkit, the open source software component, and PhysioNet (for which the Resource is named), which is the gateway for the scientific community.

PhysioNet Introduction What is PhysioBank? PhysioBank currently includes: >40 collections of cardiopulmonary, neural, and other biomedical signals from healthy subjects and patients with a variety of conditions with major public health implications, including sudden cardiac death, congestive heart failure, epilepsy, gait disorders, sleep apnea, and aging. In all, PhysioBank's collections contained over 7000 recordings (about 220 GB of digitized signals and time series) as of January 2008. Individual recordings range in length from minutes to weeks, and contain from one to thirty simultaneous signals.

Where Do the Data Collections Come From? PhysioNet Introduction Where Do the Data Collections Come From? PhysioNet research team members Other university-based research teams Other hospital-based research teams Industry PhysioBank was established when members of the PhysioNet research team contributed a set of 9 data collections that they had created during the previous 20 years. Many of their colleagues and research collaborators quickly followed suit. Their contributions made PhysioBank an indispensable tool for researchers seeking physiologic signals and time series, and their example encouraged what has become a steady stream of contributions from researchers in academia, hospitals, and industry worldwide. As of January 2008, members of the PhysioNet team had contributed 17 collections; other university-based researchers, 19; other hospital-based researchers, 5; and industry, 3.

Where Do the Data Collections Come From? PhysioNet Introduction Where Do the Data Collections Come From? PhysioNet research team members Other university-based research teams Other hospital-based research teams Industry You! (email webmaster@physionet.org) Your contributions are welcome and appreciated!

PhysioNet Introduction Example of a PhysioBank Dataset Time (hours)‏ RR interval (seconds)‏ Record e001a Physiologic time series, such as this series of cardiac interbeat (RR) intervals measured over 24 hours, can capture some of the information lost in summary statistics. Data from the NHLBI Cardiac Arrhythmia Suppression Trial (CAST) RR Interval Sub-study Database This is one of over 1500 such 24-hour RR interval time series in the CAST RR Interval Sub-Study Database, which was contributed to PhysioNet in 2004. CAST (the Cardiac Arrhythmia Suppression Trial) was a landmark NHLBI- sponsored multi-center study of the late 1980s. Although considerable effort was invested by the CAST study group in manually correcting beat detection errors, it is certain that some remain; since the CAST investigators did not preserve the digitized ECG signals that they analyzed, it is not only impossible to locate these errors (or to validate specific intervals as correct), but it is also impossible to study other features of the ECG, such as QT intervals, ST levels, T-wave alternans, or atrial activity. This database is a unique and valuable resource despite its lack of raw signals. An important part of PhysioNet's mission is to encourage and enable preservation of raw data to permit their reuse in ways that may have been unanticipated when they were collected.

PhysioNet Introduction Another PhysioBank Dataset Many data collections in PhysioBank come from published studies PhysioNet is unique in providing the original (raw) signals upon which published studies are based. Publication of raw data in this way allows other interested researchers not only to confirm the published results, but also to compare them with other analytic methods. Moreover, it enables reuse of the data for other types of analyses that may not have been of interest to the original investigator. Hausdorff et al., J Appl Physiol 86(3)1040-7 (1999)

PhysioNet Introduction Viewing PhysioBank Data Chart-O-Matic allows you to view "chart recording" samples of any PhysioBank record. The web application requires no client-side software other than a web browser. The “Chart-O-Matic” is a web application that provides a traditional chart recorder rendering of digitized signals and annotations in a web browser (no other software required). Other web applications convert signals and annotations into text for exploratory studies. The data shown are from the MIT-BIH Arrhythmia Database (see http://physionet.org/physiobank/database/mitdb/). The signals are two simultaneously recorded ECGs, and the blue markers indicate the locations of the heart beats. Three types of beats are visible in this 5-second excerpt from a 30- minute recording: normal sinus beats (.), ventricular ectopic beats (V), and ventricular fusion beats (F). The “Chart-O- Matic” uses pschart (see the notes for the title slide) to render the signals as a PNG image for display by the web browser.

What Can You Do with PhysioBank Data? PhysioNet Introduction What Can You Do with PhysioBank Data? Download for exploration and research Develop new signal processing algorithms Evaluate algorithms using ‘standard’ data Test physiologic models Develop/test/refine new biomarkers Create “real-world” classroom challenges at undergraduate, graduate and post-graduate levels PhysioBank provides open access data for students and investigators at all levels for exploration, hypothesis generation and testing, and discovery.

PhysioNet Introduction What is PhysioToolkit? Open-source software for physiologic signal processing and analysis: Detection of physiologically significant events using both classical techniques and novel methods Interactive display & characterization of signals; creation of new databases Physiologic signal modelling and for quantitative evaluation and comparison of analysis methods PhysioToolkit is the open-source software component of the Resource.

Where Does the Open-Source Software Come From? PhysioNet Introduction Where Does the Open-Source Software Come From? PhysioNet research team members Contributions from individuals and teams around the world PhysioNet/Computers in Cardiology annual Challenges PhysioToolkit components come from sources inside and outside the Resource.

Where Does the Open-Source Software Come From? PhysioNet Introduction Where Does the Open-Source Software Come From? PhysioNet research team members Contributions from individuals and teams around the world PhysioNet/Computers in Cardiology annual Challenges You! (email webmaster@physionet.org) Again, your contributions are appreciated. The strength of the open-source model of software development is that any motivated reader can learn how any open-source software component works, at any desired level of detail – and, armed with that understanding, can improve it, modify it to suit another application, or use it as a model to create something entirely new and unanticipated by the original author. Take what is already provided in PhysioToolkit and make it better, or make something new, and share it with the PhysioNet community.

PhysioNet Introduction Open Source Tools: WFDB Software Projects requiring large amounts of data can process them efficiently using WFDB software. The WFDB library reads and writes annotations and signals in many commonly-used binary formats, providing uniform access to data from local disks and from the web. Both standard and user-written software can use the WFDB library as an interface to PhysioBank. The library includes http client code, allowing applications that use it to read data from web servers via the same interface they use for reading local files, with no need for the application to know the files' locations or format (most binary formats are supported). Since annotations are not embedded in signal files, applications can annotate records without needing to copy the signals to local storage, and without modifying the original files.

PhysioNet Introduction Some PhysioNet Contributions Include Both Data and Software Manuscript Data Software The authors of this study contributed both their open-source software for locating boundaries of ECG waveforms (the onset, local extremum, and end of the P, QRS, and T waveforms), and the reference data they used to evaluate their software. In so doing, they provided what has become a widely-used tool that can be applied to other researchers' ECGs to extract accurate and important information that is difficult to obtain otherwise, and they provided data that can be used to measure and compare the accuracy of their algorithms and others. In this case, there was no generally available reference data set appropriate for evaluating algorithms of this type.

PhysioNet Introduction More Contributions with Data & Software Manuscript Data Software This is another example of a peer-reviewed publication for which the original data and software are available at the Resource.

PhysioNet Provides Tutorials on Complex Signal Analysis PhysioNet Introduction PhysioNet Provides Tutorials on Complex Signal Analysis Downloads since 2004: MSE code 4,208; MSE tutorial 7,432 Method featured in Nature News and Views 2002; 419:263. PhysioNet tutorials explain how to use the software tools for extracting information from complex biologic signals. (Download statistics are as of September 2007.)‏

PhysioNet Fosters Key NIH Priorities PhysioNet Introduction Common infrastructures for clinical research Complex biological systems Computational biology and informatics New interdisciplinary, translational research teams PhysioNet supports a number of key components of the NIH Director’s Roadmap.

PhysioNet Introduction Who Uses PhysioNet / Where? >30,000 researchers, students, manufacturers, educators, each month From all 50 US states and DC Users from >100 other countries PhysioNet is used by individuals and groups throughout the United States and around the world.

Research by PhysioNet Team PhysioNet Introduction Research by PhysioNet Team Three Broad Goals: Relating complex dynamics of physiologic time series to underlying mechanisms in health, disease, and aging Developing diagnostic and prognostic biomarkers of complex dynamics that quantify control system functions and pathologies Detecting and forecasting major events, such as seizures, sudden cardiac arrest, falls, hemodynamic collapse, and apneas, and generating hypotheses about their mechanisms These topics represent the long-term research interests of the team. Research in these areas provides much of the impetus for development of the data and software contributions of the team to PhysioBank and PhysioToolkit. Within the first two broad aims, specific efforts include algorithms that quantify the transient and local properties of non-stationary physiologic signals and the cross-interactions among multi-parameter signals; application of these techniques to detect changes that may precede the onset of catastrophic physiologic events, including epileptic seizures and sudden cardiac death; techniques for quantifying the dynamics of physiologic control; modeling of these control mechanisms; and nonlinear dynamical measures with diagnostic and prognostic value in life-threatening cardiopulmonary pathologies (Madalena Costa, Leon Glass, Ary Goldberger, Jeff Hausdorff, Joe Mietus, CK Peng). In the third broad aim, topics studied include assessment of signal quality and detection of events in weakly correlated multi-parameter data; false alarm reduction in the ICU; multivariate time series analysis and forecasting, with applications in intensive care; cardiovascular system modeling; techniques for recovery of “hidden” information in physiologic signals; web-enabled signal processing, with applications in research and telemedicine; data mining algorithms for efficient searching in very long time series; networked instrumentation for acquisition and remote viewing of real-time physiologic data (Gari Clifford, Roger Mark, George Moody).

Assessing PhysioNet’s Impact PhysioNet Introduction Extensive publications by key personnel Extensive publications by others based on Resource (>400) Contributions to basic mechanisms/clinical medicine Technology transfer The major impact of PhysioNet is assessable by a variety of metrics.

PhysioNet Impact (continued) PhysioNet Introduction International collaborations Incubator for NIH grant development & support NIH-wide influence: model for data/software sharing & multidisciplinary translational research Educational support: PhysioNet in the Classroom Further evidence of the wide impact of this unique NIH resource.

PhysioNet Introduction PhysioNet in the Classroom Increasing use of PhysioNet in undergraduate and graduate level courses in bioengineering and other disciplines Example: “Gait Module for Freshman-Level Introductory Course in Biomedical Engineering”* Part of challenge-based approach developed by University of Memphis in partnership with Vanderbilt-Northwestern-Texas- Harvard/MIT Engineering Research Center (VaNTH ERC) *See: Proc 2005 Am Soc Eng Education Ann Conf PhysioNet data and software are now used at undergraduate, graduate and post-graduate levels (and even by some very talented high school students!)‏

Unofficial Metric of PhysioNet’s Use PhysioNet Introduction Unofficial Metric of PhysioNet’s Use An Internet search confirms “top billing” and the wide usage of PhysioNet. The master PhysioNet server (at MIT) delivered over 20 terabytes of data in response to over 42 million hits during 2007. (These statistics do not include data sent to or from PhysioNet mirrors.)‏

World-wide Network of Mirror Sites PhysioNet Introduction World-wide Network of Mirror Sites Provide distributed access and backup to PhysioNet Established and maintained by volunteers at no cost to the Resource Setup is easy; open source software; upkeep is automated Boston San Antonio Brazil Israel Italy Moscow Slovenia Spain Mirror sites (current or about to be inaugurated) enhance access to the Resource.

PhysioNet Introduction Another PhysioNet Innovation: International Time Series Challenges With the annual Computers in Cardiology conference, PhysioNet hosts challenges, inviting participants to tackle important problems: Detecting Sleep Apnea from the ECG Predicting Paroxysmal Atrial Fibrillation RR Interval Time Series Modeling Distinguishing Ischemic from Non-Ischemic ST Changes Spontaneous Termination of Atrial Fibrillation QT Interval Measurement ECG Imaging of Myocardial Infarction In complementary ways, PhysioNet and Computers in Cardiology (CinC) catalyze and support scientific communication and collaboration between basic and clinical scientists. The annual meetings of CinC are gatherings of researchers from many nations and disciplines, bridging the geographic and specialty chasms that separate understanding from practice, while PhysioNet provides on-line data and software resources for to support collaborations of basic and clinical researchers throughout the year. The annual PhysioNet/CinC Challenges seek to provide stimulating yet friendly competitions, while at the same time offering both specialists and non-specialists alike opportunities to make progress on significant open problems whose solutions may be of profound clinical value. The use of shared data provided via PhysioNet makes it possible for participants to work independently toward a common objective. At CinC, participants can make meaningful results-based comparisons of their methods; lively and well-informed discussions are the norm at scientific sessions dedicated to these challenges. Discovery of the complementary strengths of diverse approaches to a problem when coupled with deep understanding of that problem frequently sparks new collaborations and opportunities for further study.

PhysioNet Introduction Getting Started: Take PhysioTour! Open http://physionet.org in a web browser, and select “Tour” from the Help menu. This opens PhysioTour (http://physionet.org/physiotour/), a self-guided interactive introduction to the web site. > 750,000 visitors!

PhysioNet: Looking Ahead PhysioNet Introduction PhysioNet: Looking Ahead New database and software additions New infrastructures for database development and data sharing (PhysioNet Works) New PhysioNet/Computers in Cardiology Challenge Multiscale analysis & modelling Development of new dynamical biomarkers PhysioNet investigators and their collaborators look forward to new database and software additions, along with a variety of other expansions of current activity and some brand new ones.

PhysioNet Introduction Faces of PhysioNet 4 1 5 6 1-George Moody 2-Roger Mark 3-Gari Clifford 4-Mohammed Saeed 5-Mauricio Villarroel 6-C-K Peng 7-Madalena Costa 8-Joe Mietus 9-Ary Goldberger Key staff members who contribute to PhysioNet: Program Director: Ary L. Goldberger, MD, Professor of Medicine, Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, MA Co-Director: Roger G. Mark, MD, PhD, Distinguished Professor in Health Sciences Technology, Massachusetts Institute of Technology, Cambridge, MA Technical Director and Webmaster: George Moody, Research Engineer, Massachusetts Institute of Technology, Cambridge, MA 7 8 9