SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation.

ABSTRACT: The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a st

SUBMITTER: Mikhaylets E 

PROVIDER: S-EPMC10859925 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature