7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The use of tracheal sounds for the diagnosis of sleep apnoea

      review-article
      1 , 2 ,
      Breathe
      European Respiratory Society

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Tracheal sounds have been the subject of many research studies. In this review, we describe the state of the art, original work relevant to upper airways obstruction during sleep, and ongoing research concerning the methods used when analysing tracheal sounds. Tracheal sound sensors are a simple and noninvasive means of measurement and are more reliable than other breathing sensors. Developments in acoustic processing techniques and enhancements in tracheal sound signals over the past decade have led to improvements in the accuracy and clinical relevance of diagnoses based on this technology. Past and current research suggests that they may have a significant role in the diagnosis of obstructive sleep apnoea.

          Key points
          • Tracheal sounds are currently a topic of significant interest but are not yet used in most routine sleep study systems.

          • Measured at the suprasternal notch, tracheal sounds can provide reliable information on breathing sounds, snoring sounds and respiratory efforts.

          • Tracheal sounds may be used as a noninvasive method of studying abnormalities of the upper airways during wakefulness.

          Educational aims
          • To understand the principles of tracheal sound measurement and analysis.

          • To highlight the importance of tracheal sounds for the diagnosis of sleep apnoea–hypopnoea syndrome.

          • To present the most relevant clinical studies that have validated the use of tracheal sound sensors and to make future clinical validation studies possible.

          Abstract

          Tracheal sounds analysis may have a significant role in the diagnosis of obstructive sleep apnoea http://ow.ly/f7ax30cAcnP

          Related collections

          Most cited references33

          • Record: found
          • Abstract: found
          • Article: not found

          Methods for obtaining and analyzing unattended polysomnography data for a multicenter study. Sleep Heart Health Research Group.

          This paper reviews the data collection, processing, and analysis approaches developed to obtain comprehensive unattended polysomnographic data for the Sleep Heart Health Study, a multicenter study of the cardiovascular consequences of sleep-disordered breathing. Protocols were developed and implemented to standardize in-home data collection procedures and to perform centralized sleep scoring. Of 7027 studies performed on 6697 participants, 5534 studies were determined to be technically acceptable (failure rate 5.3%). Quality grades varied over time, reflecting the influences of variable technician experience, and equipment aging and modifications. Eighty-seven percent of studies were judged to be of "good" quality or better, and 75% were judged to be of sufficient quality to provide reliable sleep staging and arousal data. Poor submental EMG (electromyogram) accounted for the largest proportion of poor signal grades (9% of studies had <2 hours artifact free EMG signal). These data suggest that with rigorous training and clear protocols for data collection and processing, good-quality multichannel polysomnography data can be obtained for a majority of unattended studies performed in a research setting. Data most susceptible to poor signal quality are sleep staging and arousal data that require clear EEG (electroencephalograph) and EMG signals.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The upper airway in sleep: physiology of the pharynx.

            The upper airway is the primary conduit for passage of air into the lungs. Its physiology has been the subject of intensive study: both passive mechanical and active neural influences contribute to its patency and collapsibility. Different models can be used to explain behavior of the upper airway, including the "balance of forces" (airway suction pressure during inspiration versus upper airway dilator tone) and the Starling resistor mechanical model. As sleep is the primary state change responsible for sleep disordered breathing (SDB) and the obstructive apnea/hypopnea syndrome (OSAHS), understanding its effects on the upper airway is critical. These include changes in upper airway muscle dilator activity and associated changes in mechanics and reflex activity of the muscles. Currently SDB is thought to result from a combination of anatomical upper airway predisposition and changes in neural activation mechanisms intrinsic to sleep. Detection of SDB is based on identifying abnormal (high resistance) breaths and events, but the clinical tools used to detect these events and an understanding of their impact on symptoms is still evolving. Outcomes research to define which events are most important, and a better understanding of how events lead to physiologic consequences of the syndrome, including excessive daytime somnolence (EDS), will allow physiologic testing to objectively differentiate between "normal" subjects and those with disease.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Sleep apnea monitoring and diagnosis based on pulse oximetry and tracheal sound signals.

              Sleep apnea is a common respiratory disorder during sleep, which is described as a cessation of airflow to the lungs that lasts at least for 10 s and is associated with at least 4% drop in blood's oxygen saturation level (S(a)O(2)). The current gold standard method for sleep apnea assessment is full-night polysomnography (PSG). However, its high cost, inconvenience for patients, and immobility have persuaded researchers to seek simple and portable devices to detect sleep apnea. In this article, we report on developing a new method for sleep apnea detection and monitoring, which only requires two data channels: tracheal breathing sounds and the pulse oximetry (S(a)O(2) signal). It includes an automated method that uses the energy of breathing sounds signals to segment the signals into sound and silent segments. Then, the sound segments are classified into breath, snore, and noise segments. The S(a)O(2) signal is analyzed automatically to find its rises and drops. Finally, a weighted average of different features extracted from breath segments, snore segments and S(a)O(2) signal are used to detect apnea and hypopnea events. The performance of the proposed approach was evaluated on the data of 66 patients recorded simultaneously with their full-night PSG study, and the results were compared with those of the PSG. The results show high correlation (0.96, P < 0.0001) between the outcomes of our system and those of the PSG. Also, the proposed method has been found to have sensitivity and specificity values of more than 91% in differentiating simple snorers from obstructive sleep apnea patients.
                Bookmark

                Author and article information

                Journal
                Breathe (Sheff)
                Breathe (Sheff)
                BREATHE
                breathe
                Breathe
                European Respiratory Society
                1810-6838
                2073-4735
                June 2017
                : 13
                : 2
                : e37-e45
                Affiliations
                [1 ]Interdisciplinary Sleep Medicine Center, Charité – Universitätsmedizin Berlin, Berlin, Germany
                [2 ]Research and Development at CIDELEC, Sainte Gemmes, France
                Author notes
                Article
                EDU-0088-2017
                10.1183/20734735.008817
                5702894
                29184596
                92d1016a-e696-499b-9566-b6f3da03a012
                ©ERS 2017

                Breathe articles are open access and distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0.

                History
                Categories
                Reviews
                5

                Comments

                Comment on this article