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Electromyography (EMG): Call for Data and Announcement of Yearly ReyLab Award
An electromyogram (EMG) is a common clinical test used to assess function of muscles and the nerves that control them. EMG studies are used to help in the diagnosis and management of disorders such as the muscular dystrophies and neuropathies. Nerve conduction studies that measure how well and how fast the nerves conduct impulses are often performed in conjunction with EMG studies.
Examples of EMG studies (courtesy of Seward Rutkove, MD, Department of Neurology, Beth Israel Deaconess Medical Center/Harvard Medical School) are given here.
Despite the importance of EMG tests to clinicians and researchers, no freely available, large-scale collections of such studies are available on the Internet (aside from EMG signals recorded during sleep studies). Open-source datasets are important because they help promote better diagnosis and also foster the definition and refinement of standards for test performance and evaluation.
Posting of data in digital form may, indeed, lead to new approaches to diagnosis by affording access to communities (e.g., applied mathematicians, engineers, physicists, physiologists) with expertise in signal analysis but no means to explore physiologic data of this type and to propel new hypotheses worthy of future study.
Motivated by these goals, the Margret and H.A. Rey Institute for Nonlinear Dynamics in Medicine, in conjunction with the NIH-sponsored Research Resource for Complex Physiologic Signals, announces a call for digital datasets from EMG and nerve conduction studies. Guidelines for contribution of anonymized data obtained with informed consent, are given at: http://www.physionet.org/guidelines.shtml
To encourage work in this field, the Rey Institute is also offering a $500.00 (USD) award for the best contribution of EMG data, to be determined by a peer-reviewed committee and announced at the Reylab and PhysioNet websites on March 1 of each year, beginning in 2010. To qualify, datasets need to be submitted by January 1 of that year.
For further information about making data available and this Reylab/PhysioNet EMG Award, please contact us at: agoldber@bidmc.harvard.edu.
References:
1. Kimura J, Electrodiagnosis in Diseases of Nerve and Muscle: Principles and Practice, 3rd Edition. New York, Oxford University Press, 2001.
2. Reaz MBI, Hussain MS and Mohd-Yasin F. Techniques of EMG signal analysis: detection, processing, classification and applications. Biol. Proced. Online 2006; 8(1): 11-35.
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