Machine Learning-Based Early Prediction of Neural Microelectrode Lifetime Under Accelerated Aging Conditions
- 1University of Connecticut
Abstract
Objective. Chronically implantable medical devices may require functional lifetimes of decades in order to reduce or eliminate the need for their explantation and replacement, which may involve invasive and sometimes risky surgery. However, when devices become exceptionally long-lived, preclinical studies validating their lifetimes become difficult to execute within reasonable timeframes even when accelerated life testing methods are employed. Computational methods capable of accurately and reliably predicting microelectrode lifetime from early-life data could therefore considerably expedite the development of long-lived implantable devices. Approach. Here, inspired by similar studies predicting functional lifetimes of lithium-ion battery cells from early-life data, we developed a machine learning model based on Elastic Net regression and early-life feature engineering to predict impedance-defined microelectrode lifetimes under accelerated aging conditions and evaluated its predictive performance. We also provide a dataset of electrochemical measurements from aging microelectrodes for public use. Main results. Using measurements collected through Day 5, the 10-feature Elastic Net model achieved a root mean squared error of 3.71 accelerated-aging days and a mean absolute percentage error of 13.90% under device-level validation. In an independent fabrication batch, covariance alignment and calibration using five labeled channels from one device in each target group reduced mean absolute percentage errors from 38.1% to 22.5% for Small electrodes and from 56.6% to 18.2% for Medium electrodes. Significance. These results highlight the potential of machine learning-based approaches to accurately predict neural microelectrode lifetimes from early-life data. Such methods may inform the design of future accelerated life studies, providing meaningful longevity estimates from shorter testing periods and potentially accelerating the development of long-lived implantable devices for clinical use.
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BibTeX
@misc{hua2026machine,
title = {{Machine Learning-Based Early Prediction of Neural Microelectrode Lifetime Under Accelerated Aging Conditions}},
author = {H. Hua and M. Niemiec and T. Li and R. Manna and H. Jo and K. Kim and C. Hu},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.29.762829},
url = {https://www.biorxiv.org/content/10.64898/2026.10.29.762829v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Machine Learning-Based Early Prediction of Neural Microelectrode Lifetime Under Accelerated Aging Conditions
AU - H. Hua
AU - M. Niemiec
AU - T. Li
AU - R. Manna
AU - H. Jo
AU - K. Kim
AU - C. Hu
PY - 2026
DA - 2026/10/29
PB - bioRxiv
DO - 10.64898/2026.10.29.762829
UR - https://www.biorxiv.org/content/10.64898/2026.10.29.762829v1
AB - Objective. Chronically implantable medical devices may require functional lifetimes of decades in order to reduce or eliminate the need for their explantation and replacement, which may involve invasive and sometimes risky surgery. However, when devices become exceptionally long-lived, preclinical studies validating their lifetimes become difficult to execute within reasonable timeframes even when accelerated life testing methods are employed. Computational methods capable of accurately and reliably predicting microelectrode lifetime from early-life data could therefore considerably expedite the development of long-lived implantable devices. Approach. Here, inspired by similar studies predicting functional lifetimes of lithium-ion battery cells from early-life data, we developed a machine learning model based on Elastic Net regression and early-life feature engineering to predict impedance-defined microelectrode lifetimes under accelerated aging conditions and evaluated its predictive performance. We also provide a dataset of electrochemical measurements from aging microelectrodes for public use. Main results. Using measurements collected through Day 5, the 10-feature Elastic Net model achieved a root mean squared error of 3.71 accelerated-aging days and a mean absolute percentage error of 13.90% under device-level validation. In an independent fabrication batch, covariance alignment and calibration using five labeled channels from one device in each target group reduced mean absolute percentage errors from 38.1% to 22.5% for Small electrodes and from 56.6% to 18.2% for Medium electrodes. Significance. These results highlight the potential of machine learning-based approaches to accurately predict neural microelectrode lifetimes from early-life data. Such methods may inform the design of future accelerated life studies, providing meaningful longevity estimates from shorter testing periods and potentially accelerating the development of long-lived implantable devices for clinical use.
ER -