Time series segmentation for recognition of epileptiform patterns recorded via microelectrode arrays in vitro

• Published in: PloS one Vol. 20; no. 1; p. e0309550
• Main Authors: Galeote-Checa, Gabriel, Panuccio, Gabriella, Canal-Alonso, Angel, Linares-Barranco, Bernabe, Serrano-Gotarredona, Teresa
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.01.2025; Public Library of Science (PLoS)
• Subjects: 4-Aminopyridine; Algorithms; Analysis; Animals; Arrays; Automation; Biology and Life Sciences; Biomarkers; Brain; Brain stimulation; Care and treatment; Closed loops; Computer and Information Sciences; Convulsions & seizures; Datasets; Deep Brain Stimulation; Design; Diagnosis; Electrodes; Engineering and Technology; Epilepsy; Epilepsy - diagnosis; Epilepsy - physiopathology; Evaluation; Health aspects; Hippocampus - drug effects; Hippocampus - physiopathology; Information processing; Medicine and Health Sciences; Methods; Mice; Mice, Inbred C57BL; Microelectrodes; Neurological diseases; Optimization; Patient outcomes; Pattern recognition; Physical Sciences; Power consumption; Quality of life; Recall; Research and Analysis Methods; Risk factors; Segmentation; Seizures; Seizures - physiopathology; Stimulation; Time series; Wavelet transforms; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0309550

Epilepsy is a prevalent neurological disorder that affects approximately 1% of the global population. Approximately 30-40% of patients respond poorly to antiepileptic medications, leading to a significant negative impact on their quality of life. Closed-loop deep brain stimulation (DBS) is a promising treatment for individuals who do not respond to medical therapy. To achieve effective seizure control, algorithms play an important role in identifying relevant electrographic biomarkers from local field potentials (LFPs) to determine the optimal stimulation timing. In this regard, the detection and classification of events from ongoing brain activity, while achieving low power consumption through computationally inexpensive implementations, represents a major challenge in the field. To address this challenge, we here present two algorithms, the ZdensityRODE and the AMPDE, for identifying relevant events from LFPs by utilizing time series segmentation (TSS), which involves extracting different levels of information from the LFP and relevant events from it. The algorithms were validated validated against epileptiform activity induced by 4-aminopyridine in mouse hippocampus-cortex (CTX) slices and recorded via microelectrode array, as a case study. The ZdensityRODE algorithm showcased a precision and recall of 93% for ictal event detection and 42% precision for interictal event detection, while the AMPDE algorithm attained a precision of 96% and recall of 90% for ictal event detection and 54% precision for interictal event detection. While initially trained specifically for detecting ictal activity, these algorithms can be fine-tuned for improved interictal detection, aiming at seizure prediction. Our results suggest that these algorithms can effectively capture epileptiform activity, supporting seizure detection and, possibly, seizure prediction and control. This opens the opportunity to design new algorithms based on this approach for closed-loop stimulation devices using more elaborate decisions and more accurate clinical guidelines.