Assessment of sleep quality by pulse wave amplitude and actigraphy in children with sleep-disordered breathing: evaluation at diagnosis and under non-invasive ventilation

Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB.

Chronic hypoventilation syndromes and sleep-related hypoventilation

Chronic hypoventilation affects patients with disorders on any level of the respiratory system. The generation of respiratory impulses can be impaired in congenital disorders, such as central congenital alveolar hypoventilation, in alterations of the brain stem or complex diseases like obesity hypoventilation. The translation of the impulses via spinal cord and nerves to the respiratory muscles can be impaired in neurological diseases. Thoraco-skeletal or muscular diseases may inhibit the execution of the impulses. All hypoventilation disorders are characterized by a reduction of the minute ventilation with an increase of daytime hypercapnia. As sleep reduces minute ventilation substantially in healthy persons and much more pronounced in patients with underlying thoraco-pulmonary diseases, hypoventilation manifests firstly during sleep. Therefore, sleep related hypoventilation may be an early stage of chronic hypoventilation disorders. After treatment of any prevailing underlying disease, symptomatic therapy with non-invasive ventilation (NIV) is required. The adaptation of the treatment should be performed under close medical supervision. Pressure support algorithms have become most frequently used. The most recent devices automatically apply pressure support and vary inspiratory and expiratory pressures and breathing frequency in order to stabilize upper airways, normalize ventilation, achieve best synchronicity between patient and device and aim at optimizing patients' adherence.

Overnight Transcutaneous Carbon Dioxide Monitoring in Eucapnic Patients with Obstructive Sleep Apnea Syndrome

OBJECTIVES

We monitored increases in CO2 levels during sleep by measuring transcutaneous pCO2 (PtcCO2) to determine its relationship with polysomnographic data in normocapnic patients with obstructive sleep apnea syndrome (OSAS).

MATERIAL AND METHODS

Between October 2011 and December 2012, 139 patients underwent PtcCO2 monitoring with polysomnography. All patients were evaluated with arterial blood gas (ABG) measurements and pulmonary function tests (PFTs). We excluded 13 patients with COPD and/or daytime hypercapnia and 29 patients whose PtcCO2 records could not be evaluated.

RESULTS

The patients' mean age was 46.8±10.3 years. Fifty-nine patients (60.8%) were male, and 38 (39.2%) patients were female. The mean overnight PtcCO2 was ≤45 mm Hg in 84 (86.6%) patients and >45 mm Hg in 13 (13.4%) patients. In the group with PtcCO2>45 mm Hg, 10 patients had an apnea-hypopnea index (AHI) >15, and 3 patients had an AHI<15, without a statistically significant difference (p=0.078). The mean apnea and apnea/interapnea periods were similar. The mean PtcCO2 values correlated with time spent when the SpO2 was <90% (r=0.220, p<0.031). When we grouped the patients by AHI, 60 (61.8%) patients had an AHI>15 (moderate to severe OSAS), and 37 (37.2%) had an AHI<15 (mild OSAS). Of the former group, 16.7% had a mean PtcCO2 >45 mm Hg, whereas this ratio was 8.1% in the latter group. The difference was not statistically significant (p=0.359). In the group with an AHI>15, the highest PtcCO2 levels were significantly higher (p<0.05).

CONCLUSION

We conclude that seemingly eucapnic OSAS patients may experience hypercapnia when sleeping, and PtcCO2 monitoring may be useful in the early diagnosis of hypercapnia.