Abstract

Sleep spindles are among the hallmarks observed in the electroencephalogram (EEG) that occur during non-rapid eye movement sleep precisely in stage 2. They are transient waveforms of biological and clinical interest. In this paper, we present a method to detect spindles in raw EEG recordings during sleep. The method consists of processing the signal through an adaptive autoregressive model whose features are represented by the zeroes of the model polynomial rather than the prediction coefficients. Tracing in time of the zeros’ modulus shows sharp transitions indicating statistical changes due to the occurrence of spindles. The results obtained were compared to those reported by other techniques based on traditional EEG visual reading used by neurophysiologists.

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