  Objective: Sleep spindles contain crucial brain dynamics information. We
introduce the novel non-linear time-frequency analysis tool 'Concentration of
Frequency and Time' (ConceFT) to create an interpretable automated algorithm
for sleep spindle annotation in EEG data and to measure spindle instantaneous
frequencies (IFs). Methods: ConceFT effectively reduces stochastic EEG
influence, enhancing spindle visibility in the time-frequency representation.
Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is
compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and
MASS benchmark databases. We also quantify spindle IF dynamics. Results:
ConceFT-S achieves F1 scores of 0.749 in Dream and 0.786 in MASS, which is
equivalent to or surpass A7 and SUMO with statistical significance. We reveal
that spindle IF is generally nonlinear. Conclusion: ConceFT offers an accurate,
interpretable EEG-based sleep spindle detection algorithm and enables spindle
IF quantification.
