Automatic detection of sleep spindles using Teager energy and spectral edge frequency

Abstract

Abstract-Sleep spindles are the hallmark of N2 stage of sleep. They are transient waveforms observed on sleep electroencephalogram and their identification is required for sleep staging. Due to the large number of sleep spindles appearing on an overnight sleep EEG, automating the detection of sleep spindles would be desirable, not only to save specialist time but also for fully automated sleep staging systems. A simple algorithm for automatic sleep spindle detection is presented in this paper using only one channel of EEG input. This algorithm uses Teager energy and spectral edge frequency to mark sleep spindles and results in a sensitivity of 80% and specificity of about 98%. It is also shown that more than 91% of spindles detected by the algorithm were in N2 and N3 stages combined.

An Ultralow-Power Sleep Spindle Detection System on Chip

“…Out of the total number of 535 spindles present in the database, 383 spindles are correctly detected by the system. As can be seen in Table I, the sensitivity of the system has been degraded in the case of subject The system achieves an overall sensitivity and specificity of 71.5% and 98% as opposed to 80.3% and 97.6% which is achieved by the ideal Matlab model reported in [10]. A summary of system performance including a breakdown of the total power consumed by the hardware blocks has been shown in Table II.…”

“…In the original work of [10], the TEO output should be larger than the averaged and amplified output for the duration of an entire epoch to be considered as a spindle candidate. In the hardware implementation however, two six bit digital counters (clocked at 256 Hz) at the output of the comparator count the number of samples for which the comparator output is 'high'.…”

“…Reference markings from two different experts were also provided together with the database. Similar to [10], the union of the two reference markings was used as the reference marking in this work for system performance evaluation purposes. Artefacts are present in the test database and no effort has been made to select artefact free sections of the database during the testing process.…”

“…In [10], the SEF50 was obtained with the help of an FFT. The signal power at different frequency components was calculated and the SEF50 was compared to a preset threshold (f th ) of 10.7 Hz (chosen empirically).…”

“…This work presents for the first time a hardware implementation of a sleep spindle detection system in the form of an Application Specific Integrated Circuit (ASIC). The system is based on the algorithm previously reported in [10] which utilizes the Teager Energy Operator (TEO) together with the Spectral Edge Frequency -50% (SEF50). The mathematical characteristics of the original algorithm allow for an analog approach to be followed in the design, which results in very low levels of power consumption.…”