Self-supervised learning reduces label noise in sharp wave ripple classification.

In the field of electrophysiological signal analysis, the classification of time-series datasets is essential. However, these datasets are often compromised by the prevalent issue of incorrect attribution of labels, known as label noise, which may arise due to insufficient information, inappropriate assumptions, specialists' mistakes, and subjectivity, among others. This critically impairs the accuracy and reliability of data classification, presenting significant barriers to extracting meaningful insights. Addressing this challenge, our study innovatively applies self-supervised learning (SSL) for the classification of sharp wave ripples (SWRs), high-frequency oscillations involved in memory processing that were generated before or after the encoding of spatial information. This novel SSL methodology diverges from traditional label correction techniques. By utilizing SSL, we effectively relabel SWR data, leveraging the inherent structural patterns within time-series data to improve label quality without relying on external labeling. The application of SSL to SWR datasets has yielded a 10% increase in classification accuracy. While this improved classification accuracy does not directly enhance our understanding of SWRs, it opens up new pathways for research. The study's findings suggest the transformative capability of SSL in improving data quality across various domains reliant on precise time-series data classification.