An improved algorithm for the automatic detection and characterization of slow eye movements

作者：Cona Filippo^*; Pizza Fabio; Provini Federica; Magosso Elisa

来源：Medical Engineering & Physics, 2014, 36(7): 954-961.

DOI：10.1016/j.medengphy.2014.03.019

• Biomedical signal processing
• Slow eye movements (SEMs)
• Electro-oculogram
• Wavelet decomposition
• Bayes classifier
• Template matching

Slow eye movements (SEMs) are typical of drowsy wakefulness and light sleep. SEMs still lack of systematic physical characterization. We present a new algorithm, which substantially improves our previous one, for the automatic detection of SEMs from the electro-oculogram (EOG) and extraction of SEMs physical parameters. The algorithm utilizes discrete wavelet decomposition of the EOG to implement a Bayes classifier that identifies intervals of slow ocular activity; each slow activity interval is segmented into single SEMs via a template matching method. Parameters of amplitude, duration, velocity are automatically extracted from each detected SEM. The algorithm was trained and validated on sleep onsets and offsets of 20 EOG recordings visually inspected by an expert. Performances were assessed in terms of correctly identified slow activity epochs (sensitivity: 85.12%; specificity: 82.81%), correctly segmented single SEMs (89.08%), and time misalignment (0.49 s) between the automatically and visually identified SEMs. The algorithm proved reliable even in whole sleep (sensitivity: 83.40%; specificity: 72.08% in identifying slow activity epochs; correctly segmented SEMs: 93.24%; time misalignment: 0.49 s). The algorithm, being able to objectively characterize single SEMs, may be a valuable tool to improve knowledge of normal and pathological sleep.

• 出版日期2014-7