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These last months were dedicated to several important things:
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”.
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.
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.
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
.matto.csv.
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──────────────────────────────────────────────────────────────────────────────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.↩︎