Automated rodent sleep spindle detector: MATLAB app using two complementary search algorithms

Abstract

Abstract A Rodent Sleep Spindle Detector (RSSD) application (app) was developed to assist researchers working with high volume studies examining the impact of sleep on neurological function. Our RSSD is a MATLAB-based software program with a user interface that automatically identifies sleep spindles from intracranial EEG (iEEG) recordings of rodents using two novel yet complementary algorithmic approaches, a primary and secondary one. To validate the program, 6,000 copies of real spindles of 5 different types, ranging from 11–17 Hz with a duration of at least 0.3 seconds, were randomly placed within a noisy simulated prefrontal cortex iEEG signal of 50,000 seconds in duration. When compared to the ground truth on a datapoint-by-datapoint basis (individual spindle detection), the program had an accuracy of 98.40 ± 5.62% (mean ± SD) with 95% C.I. [91.93, 100] and 96.90 ± 4.34% (mean ± SD) with 95% C.I. [91.91, 100] for the primary and secondary algorithmic approach, respectively. Evaluating total spindle count, the program had an accuracy of 93.68 ± 13.66% (mean ± SD) with 95% C.I. [81.71, 100], and of 99.85 ± 0.12% (mean ± SD) with 95% C.I. [99.71, 99.96] for the primary and secondary algorithmic approach, respectively. The robustness of the sleep spindle detection was further validated for a range of spindle's duration, amplitude, and frequency by embedding in the iEEG signal respective artificial spindles. Finally, the RSSD app further improves its performance by first processing available video recordings of rodents to identify periods of quiescence and then running the sleep spindle detection algorithms on the iEEG only for those periods.