摘要
由于鼾声中包含较多的呼吸气流引起的随机信号,采用两次线性预测残差处理得到鼾声的声源信号,再经过倒谱计算,提取鼾声的基频值。按鼾声时域波形的准周期性将其分类,提取鼾声的基频并统计其分布,与睡眠呼吸暂停低通气综合征(Sleep Apnea Hypopnea Syndrome, SAHS)诊断的严重程度做对比,对93名受试者的鼾声做了分析,发现随着SAHS严重程度的加重,鼾声具有的准周期性减少,基频轨迹的抖动有增大的趋势。利用基频与Mel频率倒谱系数(Mel-Frequency Cepstral Coefficients, MFCC)特征对SAHS严重程度的诊断进行估计,正确率为85.8%。鼾声基频相关的统计特性可以作为判断SAHS严重程度的参数之一。研究成果对便携式鼾症检测仪的设计与实现起到了很好的参考作用。
With the random signal caused by breathing airflow in snore,the double linear prediction residual methods are used to get the snore source signal,and then by calculating its cepstrum,the pitch of snore can be extracted.The snores are classified into several types based on the periodicity of the snore waveforms.The extracted pitches and the statistics of their distribution are compared with the severity of sleep apnea hypopnea syndrome (SAHS).The snores from the 93 subjects are analyzed in the experiments.The results show that with the aggravation of SAHS,the quasi-periodicity of snoring decreases and the jitter of the pitch contour increases.The pitch and the features of Mel-Frequency Cepstral Coefficients (MFCC) are used to judge the severity of SAHS,and the accuracy is 85.5%.The statistical characteristics of the snore pitch correlation could be used as one of the parameters to judge the severity of SAHS.This study would play a positive role in design and implementation of the portable SAHS diagnosis instruments.
作者
侯丽敏
施晓宇
童超
刘焕成
HOU Li-min;SHI Xiao-yu;TONG Chao;LIU Huan-cheng(School of Communication and Information Engineering,Shanghai University,Shanghai 200444,China)
出处
《声学技术》
CSCD
北大核心
2019年第2期176-181,共6页
Technical Acoustics
基金
国家自然科学基金(61525203
61572308
U1536108)资助项目
