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These last months were dedicated to several important things:

  1. Restructuring the roadmap
  2. Refining the main pipeline
  3. Preparing for modeling and parameter tuning
  4. Feasibility trial
  5. And others

Refining the main pipeline

That can also be thought of as “rethinking” the pipeline. What also leads to the roadmap restructuration.

It is essential not only to write a pipeline that can “autoplot” itself for fine-grain inspection but also to design a high-level graph that can explain it “in a glance”. This exercise was helpful both ways: telling the story in a short version also reveals missing things and misleading paths that are not so obvious when thinking “low-level”.

Preparing for modeling and parameter tuning

Although this work has its purpose of being finally deployed on small hardware, this prospective phase will need several hours of computing, tuning, evaluation, and validation of all findings.

Thus it was necessary to revisit the frameworks we are used to working on R: caret and the newest tidymodels collection. For sure, there are other frameworks and opinions1. Notwithstanding, this project will follow the tidymodels road. Two significant arguments 1) constantly improving and constantly being re-checked for bugs; 2) allows to plug in a custom modeling algorithm that, in this case, will be the one needed for developing this work.

Feasibility trial

A side-project called “false.alarm.io” has been derived from this work (an unfortunate mix of “false.alarm” and “PlatformIO”2, the IDE chosen to interface the panoply of embedded systems we can experiment). The current results of this side-project are very enlightening and show that the final algorithm can indeed be used in small hardware. Further data will be available in the future.

And others

After this “step back” to look forward, it was time to define how the regime change algorithm would integrate with the actual decision of triggering or not the alarm. Some hypotheses were thought out: (1) clustering similar patterns, (2) anomaly detection, (3) classification, and (4) forecasting. Among these methods, it was thought to avoid exceeding processor capacity, an initial set of shapelets3 can be sufficient to rule in or out the TRUE/FALSE challenge. Depending on the accuracy of this approach and the resources available, another method can be introduced for both (1) improving the “negative”1 samples and (2) learning more shapelets to improve the TRUE/FALSE alarm discrimination.

Minor update, but also important concerning the FAIR principle “Interoperability”: the dataset stored publicly on Zenodo4 was converted from .mat to .csv.

References

1.
Thompson J. On not using tidymodels. Published October 2020. Accessed January 5, 2022. https://staffblogs.le.ac.uk/teachingr/2020/10/05/on-not-using-tidymodels/
2.
PlatformIO, a professional collaborative platform for embedded development. Accessed January 5, 2022. https://platformio.org/
3.
Rakthanmanon T, Keogh E. Fast shapelets: A scalable algorithm for discovering time series shapelets. In: Proceedings of the 2013 SIAM International Conference on Data Mining. Society for Industrial; Applied Mathematics; 2013:668-676. doi:10.1137/1.9781611972832.74
4.
Reducing False Arrhythmia Alarms in the ICU - The PhysioNet Computing in Cardiology Challenge 2015. Published online March 24, 2021. doi:10.5281/zenodo.4634013

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  1. The term “negative” does not imply that the patient has a “normal” ECG. It means that the “negative” section is not a life-threatening condition that needs to trigger an alarm.↩︎