Automatic sleep spindle detection in raw EEG signal of newborn babies
In this study, a novel method for automatically detecting sleep spindles from a given raw EEG (Electroencephalogram) data is proposed. We do not use any feature extraction and learning technique. Rather, we model the visual perception of identifying rhythmic peaks within frequency range 11.5–15 Hz. To achieve the performance close to visual detection, we first use a Gaussian window for smoothening of the signal. Then peak detection method is applied for identifying visually distinguishable peaks. If the frequency of peaks lies within frequency range 11.5–15 Hz, then we declare existence of a sleep spindle. Validity of our process is determined by visual scoring of sleep spindles and comparing it with the automatic scoring. We get a specificity range of 89%–98% for a sensitivity range of 87%–96% which is better that any other automatic detection process.