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DennisDean

DennisDean
Joined Nov 2013
DennisDean
Joined Nov 2013

Dear Jaspal,

I believe you identified what I believe could be a linch pin for unlocking the power of the data stored on the site. The scored data including apneic events provides a valuable resource for which to conduct research now by alleviating the high cost of manual scoring. With that said, being able to systematically extract common events systematically and objectively could open up sleep medicine. I believe the development of sleep event extraction could mirror what has been done with ECG R wave detection; where open source software with a range of approaches can be downloaded. I would argue that an open source sleep feature extraction toolkit would establish the framework for new measures to be explored.

I have some suggestions on how to approach. Many aspects of feature detection in sleep medicine look for a decrease in signal with a minimum duration that correspond with a value/change of another signal. A method that could be applied in multiple situations could be powerful. I don't believe the specific method is that importants. For example, ECG R wave detection approaches use wavelets, state space modeling, point processing modeling and dynamical systems approaches. Demonstrating an approach is robust to artifacts in a large data sets (plug for the NSRR dataset) would be more important than the methods (as long as it wasn't to slow).

Take a look at the open challenge problems for an example that demonstrates the challenges that arise when extracting features from PSG data.

Good luck!

Best, Dennis

Hi Shaun,

Thank you for your comments and request. An update on NSRR activities and some personal comments follow

Harmonizing EDF labels and identifying aberrant epochs are areas that we are working on directly through NSRR efforts or through signal processing research projects.

We have been working on data consistency issues that arise when doing large scale analyses. Tools for identifying inconsistency have been developed and being tested internally. The data consistency checkers generate EXCEL output that can be used to create a batch analysis file which include parameters that are not consistent in a study (ex include signal labels and sampling rate). We are happy to make these tools available to the general community as they become more robust. We are happy to share the code as is for those willing to jump right in with code underdevelopment.

I have shifted my personal development of large scale signal processing application to not require consistent signal labels nor consistent sampling rates. For example, the spectral analysis pipeline automatically converts the signal units to uV prior to analyses. This has allowed us to reduce data harmonization required during cross study analyses with only an incremental upfront development cost. I have found this approach preferable to creating/maintaining copies of multiple cohort studies.

We have begun identifying EEG studies that are not recommended to be used for EEG analyses as part of NSRR EEG spectral analysis activities. A list of studies included and excluded as part of spectral analysis will be posted as cohort spectral analysis results are posted. Individual subject spectral analyses output files includes a flag identifying epochs as artifacts. Individual subject spectral analysis output files could be made available to the community as requested.

Please feel free to email me for additional details or to request code.

Best, Dennis