High frequency components of tracheal sound are emphasized during prolonged flow limitation

Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders

Sleep apnea is one of the most common sleep disorders. Here, patients suffer from multiple breathing pauses longer than 10 s during the night which are referred to as apneas. The standard method for the diagnosis of sleep apnea is the attended cardiorespiratory polysomnography (PSG). However, this method is expensive and the extensive recording equipment can have a significant impact on sleep quality falsifying the results. To overcome these problems, a comfortable and novel system for sleep monitoring based on the recording of tracheal sounds and movement data is developed. For apnea detection, a unique signal processing method utilizing both signals is introduced. Additionally, an algorithm for extracting the heart rate from body sounds is developed. For validation, ten subjects underwent a full-night PSG testing, using the developed sleep monitor in concurrence. Considering polysomnography as gold standard the developed instrumentation reached a sensitivity of 92.8% and a specificity of 99.7% for apnea detection. Heart rate measured with the proposed method was strongly correlated with heart rate derived from conventional ECG (r ² = 0.8164). No significant signal losses are reported during the study. In conclusion, we demonstrate a novel approach to reliably and noninvasively detect both apneas and heart rate during sleep.

Evaluation of the different sleep-disordered breathing patterns of the compressed tracheal sound

OBJECTIVE

Suitability of the compressed tracheal sound signal for screening different sleep-disordered breathing patterns was evaluated. The previous results suggest that the plain pattern in the compressed sound signal represents mostly normal, unobstructed breathing, the thick pattern consists of periodic apneas/hypopneas and during the thin pattern, flow limitation in the nasal cannula signal is abundant.

METHODS

Twenty-seven patients underwent a polysomnography with a tracheal sound and oesophageal pressure monitoring. The tracheal sound data was compressed and scored visually into three different breathing patterns. The percentage of oesophageal pressure values under -8cm H2O, the minimum pressure value and the average duration of the breathing cycles were extracted from 10-min episodes of those plain, thick and thin patterns. In addition, the spectral contents of the tracheal sound during the different breathing patterns were evaluated.

RESULTS

The percentage of time when the oesophageal pressure negativity increased was highest during the thin pattern and lowest during the plain pattern. In addition, the thin pattern presented most high frequency components in the 1001-2000Hz frequency band of the tracheal sound.

CONCLUSIONS

The results confirmed our previous findings that both the thick and thin patterns seem to consist of obstructed breathing, whereas during the plain pattern the breathing is normal, unobstructed.

SIGNIFICANCE

Most screening methods for sleep-disordered breathing reveal only periodic apneas/hypopneas, but with the compressed sound signal the sustained partial obstruction can be estimated as well.

Respiratory flow-sound relationship during both wakefulness and sleep and its variation in relation to sleep apnea

Tracheal respiratory sound analysis is a simple and non-invasive way to study the pathophysiology of the upper airway and has recently been used for acoustic estimation of respiratory flow and sleep apnea diagnosis. However in none of the previous studies was the respiratory flow-sound relationship studied in people with obstructive sleep apnea (OSA), nor during sleep. In this study, we recorded tracheal sound, respiratory flow, and head position from eight non-OSA and 10 OSA individuals during sleep and wakefulness. We compared the flow-sound relationship and variations in model parameters from wakefulness to sleep within and between the two groups. The results show that during both wakefulness and sleep, flow-sound relationship follows a power law but with different parameters. Furthermore, the variations in model parameters may be representative of the OSA pathology. The other objective of this study was to examine the accuracy of respiratory flow estimation algorithms during sleep: we investigated two approaches for calibrating the model parameters using the known data recorded during either wakefulness or sleep. The results show that the acoustical respiratory flow estimation parameters change from wakefulness to sleep. Therefore, if the model is calibrated using wakefulness data, although the estimated respiratory flow follows the relative variations of the real flow, the quantitative flow estimation error would be high during sleep. On the other hand, when the calibration parameters are extracted from tracheal sound and respiratory flow recordings during sleep, the respiratory flow estimation error is less than 10%.