Ghahjaverestan Nasim Montazeri, Saha Shumit, Gavrilovic Bojan, Yadollahi Azadeh
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:764-767. doi: 10.1109/EMBC44109.2020.9176630.
Tracheal sounds represent information about the upper airway and respiratory airflow, however, they can be contaminated by the snoring sounds. The sound of snoring has spectral content in a wide range that overlaps with that of breathing sounds during sleep. For assessing respiratory airflow using tracheal breathing sound, it is essential to remove the effect of snoring. In this paper, an automatic and unsupervised wavelet-based snoring removal algorithm is presented. Simultaneously with full-night polysomnography, the tracheal sound signals of 9 subjects with different levels of airway obstruction were recorded by a microphone placed over the trachea during sleep. The segments of tracheal sounds that were contaminated by snoring were manually identified through listening to the recordings. The selected segments were automatically categorized based on including discrete or continuous snoring pattern. Segments with discrete snoring were analyzed by an iterative wave-based filtering optimized to separate large spectral components related to snoring from smaller ones corresponded to breathing. Those with continuous snoring were first segmented into shorter segments. Then, each short segments were similarly analyzed along with a segment of normal breathing extracted from the recordings during wakefulness. The algorithm was evaluated by visual inspection of the denoised sound energy and comparison of the spectral densities before and after removing snores, where the overall rate of detectability of snoring was less than 2%.Clinical Relevance- The algorithm provides a way of separating snoring pattern from the tracheal breathing sounds. Therefore, each of them can be analyzed separately to assess respiratory airflow and the pathophysiology of the upper airway during sleep.
气管声音代表有关上呼吸道和呼吸气流的信息，然而，它们可能会被打鼾声干扰。打鼾声的频谱内容范围很广，与睡眠期间呼吸声的频谱范围重叠。为了使用气管呼吸声评估呼吸气流，去除打鼾的影响至关重要。本文提出了一种基于小波的自动无监督打鼾去除算法。在进行全夜多导睡眠图监测的同时，通过睡眠期间放置在气管上方的麦克风记录了9名气道阻塞程度不同的受试者的气管声音信号。通过听录音手动识别被打鼾污染的气管声音片段。根据包含离散或连续打鼾模式对所选片段进行自动分类。对具有离散打鼾的片段进行基于迭代小波的滤波分析，该滤波经过优化，以将与打鼾相关的大频谱成分与对应于呼吸的较小频谱成分分离。对具有连续打鼾的片段，首先将其分割成较短的片段。然后，对每个短片段进行类似分析，并与从清醒期间的录音中提取的一段正常呼吸进行分析。通过对去噪后的声能进行目视检查以及比较去除打鼾前后的频谱密度来评估该算法，其中打鼾的总体可检测率低于2%。临床意义——该算法提供了一种将打鼾模式与气管呼吸声分离的方法。因此，可以分别对它们进行分析，以评估睡眠期间的呼吸气流和上呼吸道的病理生理学。
