title: Evaluating the use of line length for automatic sleep spindle detection.
abstract: Sleep spindles are transient waveforms observed on the electroencephalogram (EEG) during the N2 stage of sleep. In this paper we evaluate the use of line length, an efficient and low-complexity time domain feature, for automatic detection of sleep spindles. We use this feature with a simple algorithm to detect spindles achieving sensitivity of 83.6% and specificity of 87.9%. We also present a comparison of these results with other spindle detection methods evaluated on the same dataset. Further, we implemented the algorithm on a MSP430 microcontroller achieving a power consumption of 56.7 μW. The overall detection performance, combined with the low power consumption show that line length could be a useful feature for detecting sleep spindles in wearable and resource-constrained systems.
authors.forename: Syed Anas
authors.initials: SA
authors.lastname: Imtiaz
authors.forename: Esther
authors.initials: E
authors.lastname: Rodriguez-Villegas
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D011211:Electric Power Supplies; D004569:Electroencephalography; D004867:Equipment Design; D005260:Female; D006801:Humans; D008297:Male; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012890:Sleep / Q000502:physiology*; D012894:Sleep Stages / Q000502:physiology*