Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants

Epilepsy affects over 50 million people worldwide, posing a significant clinical challenge, particularly for patients unresponsive to conventional treatments. Advances in neural implants with on-device algorithms are revolutionizing epilepsy management by enabling precise, real-time seizure detectio...

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Detalles Bibliográficos
Autores: Galeote Checa, Gabriel, Panuccio, Gabriella, Linares Barranco, Bernabé, Serrano Gotarredona, María Teresa
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:dnet:idus________::78b0bcda0e969aea06d33000d999a5e7
Acceso en línea:https://hdl.handle.net/11441/186083
https://doi.org/10.1109/TBCAS.2025.3622493
Access Level:acceso abierto
Palabra clave:Outlier detection
time-series segmentation
local field potentials
hardware-efficient
real-time
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spelling Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical ImplantsGaleote Checa, GabrielPanuccio, GabriellaLinares Barranco, BernabéSerrano Gotarredona, María TeresaOutlier detectiontime-series segmentationlocal field potentialshardware-efficientreal-timeEpilepsy affects over 50 million people worldwide, posing a significant clinical challenge, particularly for patients unresponsive to conventional treatments. Advances in neural implants with on-device algorithms are revolutionizing epilepsy management by enabling precise, real-time seizure detection and reducing the technical and financial burden of data transmission. The current trend advances towards the integration of a larger number of electrodes in neural implants, enhancing spatial resolution and broadening brain coverage. Consequently, the increasing data demands necessitate highly efficient processing to minimize transmission bandwidth and power consumption, ensuring the long term viability of implantable systems. This work presents a novel approach using time-series segmentation (TSS) to extract labeled information from raw recordings. The algorithm explores multiple outlier detection methods with a heuristic low-complexity event classifier, and employs a multichannel consensus strategy to improve detection accuracy through multichannel agreement. This system enables high-performance seizure detection and segments local field potentials (LFP) into clinically relevant labels for interpretation and post-processing. Tested on microelectrode array (MEA) recordings from mouse hippocampus-cortex slices treated with 4-aminopyridine, the system demonstrated robust reliability. Implemented on a Pynq-Z2 board with a Zynq 7020 System-on-Chip, the algorithm requires minimal calibration, achieving 95% accuracy, 94% sensitivity, and a 0.03% FPR with a low power consumption of 128 mW for the best-performing outlier detector. By demonstrating the application of TSS to implantable device algorithms for ondevice processing, this work advances towards more effective, personalized epilepsy treatments.IEEE-Instutue of Electrical and Electronics EngineersArquitectura y Tecnología de ComputadoresSpanish National Project2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/186083https://doi.org/10.1109/TBCAS.2025.3622493reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Transactions on Biomedical Circuits and Systems, 20, 153 p.-164 p.. PID2023-149071NB-C51 EUPHORIChttps://ieeexplore.ieee.org/document/11205852info:eu-repo/semantics/openAccessoai:dnet:idus________::78b0bcda0e969aea06d33000d999a5e72026-06-17T12:51:07Z
dc.title.none.fl_str_mv Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants
title Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants
spellingShingle Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants
Galeote Checa, Gabriel
Outlier detection
time-series segmentation
local field potentials
hardware-efficient
real-time
title_short Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants
title_full Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants
title_fullStr Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants
title_full_unstemmed Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants
title_sort Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants
dc.creator.none.fl_str_mv Galeote Checa, Gabriel
Panuccio, Gabriella
Linares Barranco, Bernabé
Serrano Gotarredona, María Teresa
author Galeote Checa, Gabriel
author_facet Galeote Checa, Gabriel
Panuccio, Gabriella
Linares Barranco, Bernabé
Serrano Gotarredona, María Teresa
author_role author
author2 Panuccio, Gabriella
Linares Barranco, Bernabé
Serrano Gotarredona, María Teresa
author2_role author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
Spanish National Project
dc.subject.none.fl_str_mv Outlier detection
time-series segmentation
local field potentials
hardware-efficient
real-time
topic Outlier detection
time-series segmentation
local field potentials
hardware-efficient
real-time
description Epilepsy affects over 50 million people worldwide, posing a significant clinical challenge, particularly for patients unresponsive to conventional treatments. Advances in neural implants with on-device algorithms are revolutionizing epilepsy management by enabling precise, real-time seizure detection and reducing the technical and financial burden of data transmission. The current trend advances towards the integration of a larger number of electrodes in neural implants, enhancing spatial resolution and broadening brain coverage. Consequently, the increasing data demands necessitate highly efficient processing to minimize transmission bandwidth and power consumption, ensuring the long term viability of implantable systems. This work presents a novel approach using time-series segmentation (TSS) to extract labeled information from raw recordings. The algorithm explores multiple outlier detection methods with a heuristic low-complexity event classifier, and employs a multichannel consensus strategy to improve detection accuracy through multichannel agreement. This system enables high-performance seizure detection and segments local field potentials (LFP) into clinically relevant labels for interpretation and post-processing. Tested on microelectrode array (MEA) recordings from mouse hippocampus-cortex slices treated with 4-aminopyridine, the system demonstrated robust reliability. Implemented on a Pynq-Z2 board with a Zynq 7020 System-on-Chip, the algorithm requires minimal calibration, achieving 95% accuracy, 94% sensitivity, and a 0.03% FPR with a low power consumption of 128 mW for the best-performing outlier detector. By demonstrating the application of TSS to implantable device algorithms for ondevice processing, this work advances towards more effective, personalized epilepsy treatments.
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/186083
https://doi.org/10.1109/TBCAS.2025.3622493
url https://hdl.handle.net/11441/186083
https://doi.org/10.1109/TBCAS.2025.3622493
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Biomedical Circuits and Systems, 20, 153 p.-164 p..
PID2023-149071NB-C51 EUPHORIC
https://ieeexplore.ieee.org/document/11205852
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE-Instutue of Electrical and Electronics Engineers
publisher.none.fl_str_mv IEEE-Instutue of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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