AIM: To evaluate the sensitivity of mean absolute amplitudes of the thoracic and the abdominal signals as a prompt indicator of the occurrence of sleep apnoea events. BACKGROUND: To provide symptomatic management of sleep apnoea, a reliable method of detecting sleep apnoea is essential to ensure that the intervention can be applied only when needed. It is also crucial to identify the threshold for the trigger of an intervention using a deployed sensor. DESIGN: Twenty-six subjects aged between 18-65 years who were diagnosed with obstructive or central sleep apnoea underwent an overnight sleep study. METHOD: Signals of nasal and oral airflow, thoracic and abdominal efforts and pulse oximetry level were recorded using a polysomnography device. RESULTS: With a 95% CI, the overall area under the receiver operating characteristic of the thoracic signal, the abdominal signal and the combination of the thoracic and the abdominal signals were 84.56, 87.48 and 90.91%, respectively. Using -20, -25 and -30% as a cut-off point, the sensitivity values of thoracic signal, abdominal signal and combination of the thoracic and the abdominal signals ranged from 70.29-86.25% and the specificity values ranged from 74.82 to 90.09%. CONCLUSIONS: Using mean absolute amplitude analysis, the results of this study showed that combination of the thoracic and the abdominal signals achieved the best overall and individual performances compared with thoracic signal and abdominal signal. Overall, thoracic signal, abdominal signal and combination of the thoracic and the abdominal signals have a good performance with an receiver operating characteristic value higher than 80%. The thoracic and the abdominal signals were good parameters for the identification of the occurrence of sleep apnoea, being as quick as the nasal airflow signal. RELEVANCE TO CLINICAL PRACTICE: These results suggested that sleep apnoea events could be identified through constant monitoring of the patient's thoracic and abdominal signals. Knowledge of these signals could help nurses to manage sleep apnoea in patients.
AIM: To evaluate the sensitivity of mean absolute amplitudes of the thoracic and the abdominal signals as a prompt indicator of the occurrence of sleep apnoea events. BACKGROUND: To provide symptomatic management of sleep apnoea, a reliable method of detecting sleep apnoea is essential to ensure that the intervention can be applied only when needed. It is also crucial to identify the threshold for the trigger of an intervention using a deployed sensor. DESIGN: Twenty-six subjects aged between 18-65 years who were diagnosed with obstructive or central sleep apnoea underwent an overnight sleep study. METHOD: Signals of nasal and oral airflow, thoracic and abdominal efforts and pulse oximetry level were recorded using a polysomnography device. RESULTS: With a 95% CI, the overall area under the receiver operating characteristic of the thoracic signal, the abdominal signal and the combination of the thoracic and the abdominal signals were 84.56, 87.48 and 90.91%, respectively. Using -20, -25 and -30% as a cut-off point, the sensitivity values of thoracic signal, abdominal signal and combination of the thoracic and the abdominal signals ranged from 70.29-86.25% and the specificity values ranged from 74.82 to 90.09%. CONCLUSIONS: Using mean absolute amplitude analysis, the results of this study showed that combination of the thoracic and the abdominal signals achieved the best overall and individual performances compared with thoracic signal and abdominal signal. Overall, thoracic signal, abdominal signal and combination of the thoracic and the abdominal signals have a good performance with an receiver operating characteristic value higher than 80%. The thoracic and the abdominal signals were good parameters for the identification of the occurrence of sleep apnoea, being as quick as the nasal airflow signal. RELEVANCE TO CLINICAL PRACTICE: These results suggested that sleep apnoea events could be identified through constant monitoring of the patient's thoracic and abdominal signals. Knowledge of these signals could help nurses to manage sleep apnoea in patients.