Automated Sleep Spindle Detection System using Period-Amplitude Analysis

Panagiotis Rizogiannis, P. Ktonas, H. Tsekou, T. Paparrigopoulos, D. Dikeos, E. Ventouras
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Abstract

Sleep spindles are rhythmic transient waveforms present in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. In the present study a period-amplitude analysis method was applied for the automated detection of sleep spindles in all-night sleep EEG recordings of young healthy subjects. The method relies on the characterization of individual half-waves of the EEG data, by estimating electrographic parameters such as amplitude and duration and by assigning a grade to each half-wave depending on where it lies in the amplitude-frequency plane. The grading is followed by the detection system, checking consecutive half-wave characteristics and implementing a set of rules for determining the start and the end of spindle bursts and for retaining or rejecting sleep spindle indications provided during the various stages of the detection system. The sensitivity and false positive rate across subjects was 78.9% and 10.9%, respectively, providing indication that the method could be successfully applied to larger sets of healthy subjects of various age groups, as well as to patient populations.
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基于周期-振幅分析的自动睡眠主轴检测系统
睡眠纺锤波是在非快速眼动(NREM)睡眠的脑电图(EEG)中出现的有节奏的瞬态波形。本研究采用周期-振幅分析方法对健康青年的睡眠脑电图记录中的睡眠纺锤波进行自动检测。该方法依赖于脑电图数据中单个半波的特征,通过估计振幅和持续时间等电参数,并根据每个半波在幅频平面中的位置为其分配等级。分级之后是检测系统,检查连续的半波特征,并实施一套规则,以确定纺锤波爆发的开始和结束,并保留或拒绝在检测系统的各个阶段提供的睡眠纺锤波指示。受试者的敏感性和假阳性率分别为78.9%和10.9%,表明该方法可以成功地应用于更大范围的不同年龄组的健康受试者以及患者群体。
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