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

• Published in: PLoS computational biology Vol. 20; no. 4; p. e1011152
• Main Authors: Balzekas, Irena, Trzasko, Joshua, Yu, Grace, Richner, Thomas J., Mivalt, Filip, Sladky, Vladimir, Gregg, Nicholas M., Van Gompel, Jamie, Miller, Kai, Croarkin, Paul E., Kremen, Vaclav, Worrell, Gregory A.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Circadian rhythms; Computational Biology - methods; Convulsions & seizures; Data integrity; Datasets; Decomposition; Electrocorticography - methods; Electroencephalography; Electroencephalography - methods; Electrophysiological recording; Engineering and Technology; Epilepsy; Epilepsy - diagnosis; Epilepsy - physiopathology; Female; Hippocampus - physiology; Hippocampus - physiopathology; Humans; Irregular sampling; Mean square errors; Medicine and Health Sciences; Methods; Models, Neurological; Narrowband; Noise reduction; Oscillations; Physical Sciences; Physiological aspects; Physiology; Polynomials; Research and Analysis Methods; Sampling; Seizures; Seizures - diagnosis; Seizures - physiopathology; Signal Processing, Computer-Assisted; Sleep and wakefulness; Sparsity; Trends; Wakefulness; United States
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011152

Numerous 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 .