Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity

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dc.contributor.authorBalzekas, Irenacs
dc.contributor.authorTrzasko, Joshuacs
dc.contributor.authorYu, Gracecs
dc.contributor.authorRichner, Thomascs
dc.contributor.authorMívalt, Filipcs
dc.contributor.authorSladký, Vladimírcs
dc.contributor.authorGregg, Nicholas M.cs
dc.contributor.authorVan Gompel, Jamie J.cs
dc.contributor.authorMiller, Kai J.cs
dc.contributor.authorCroarkin, Paul E.cs
dc.contributor.authorKřemen, Václavcs
dc.contributor.authorWorrell, Gregorycs
dc.coverage.issue4cs
dc.coverage.volume20cs
dc.date.accessioned2025-06-16T15:57:22Z
dc.date.available2025-06-16T15:57:22Z
dc.date.issued2024-04-25cs
dc.description.abstractNumerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals.Trial Registration: NCT03946618. Circadian and multiday cycles are an important part of many long-term neuro-behavioral phenomena such as pathological inter-ictal epileptiform discharges (IEDs) and seizures in epilepsy. Long-term, ambulatory, neuro-behavioral monitoring in human patients involves complex recording systems that can be subject to intermittent, irregular data loss and storage limitations, resulting in sparse, irregularly sampled data. Cycle identification in sparse data or irregular data using traditional frequency decomposition techniques typically requires interpolation to create a regular timeseries. Using unique, long-term recordings of pathological brain activity in people with epilepsy implanted with an investigational device, we developed a method to identify cycles in sparse, irregular neuro-behavioral data without interpolation. We anticipate this approach will enable retrospective cycle identification in sparse neuro-behavioral timeseries and support prospective sparse sampling in monitoring systems to enable long-term monitoring of patients and to extend storage capacity in a variety of ambulatory monitoring applications.en
dc.formattextcs
dc.format.extent1-25cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationPLoS Computational Biology. 2024, vol. 20, issue 4, p. 1-25.en
dc.identifier.doi10.1371/journal.pcbi.1011152cs
dc.identifier.issn1553-7358cs
dc.identifier.orcid0000-0002-0693-9495cs
dc.identifier.other197363cs
dc.identifier.researcheridAAX-1872-2021cs
dc.identifier.scopus57205062706cs
dc.identifier.urihttps://hdl.handle.net/11012/252557
dc.language.isoencs
dc.publisherPUBLIC LIBRARY SCIENCEcs
dc.relation.ispartofPLoS Computational Biologycs
dc.relation.urihttps://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011152cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1553-7358/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectNONLINEAR LEAST-SQUARESen
dc.subjectFREEDOMen
dc.titleMethod for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activityen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-197363en
sync.item.dbtypeVAVen
sync.item.insts2025.06.16 17:57:22en
sync.item.modts2025.06.16 17:32:50en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
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