2015PubMedRequires access

[Clinical research on micro-movement sensitive mattress sleep monitoring system in children with obstructive sleep apnea hypopnea syndrome].

Chao Chen, Liu Dabo, Yujing Yao, Xinhua Yi

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Abstract

OBJECTIVE: To evaluate the diagnostic value of micro-movement sensitive mattress sleep monitoring system (MSMSMS) in children with obstructive sleep apnea hypopnea syndrome. METHODS: Sixty-five children with the complaint of sleep disorder were collected and detected by polysomnography and micro-sensitive mattress sleep monitoring system overnight at least seven hours. The date from two instruments were analyzed by computer automatically and then modified by two professional staff double-blind separately. The data about the diagnosis of sleep breath disorder and other sleep physiology information were compared between the two groups. RESULTS: Sixty-one cases finished the study finally. The mean age of these children was (7.0 ± 2.7) and the youngest was 3 years old, maximum was 13 years old, male children 46 cases, female patients 15 cases, body mass index (BMI) median [25 quantile; 75 quantile] 16.00 [14.80;17.5]kg/m2. Mean PSG measured apnea hypopnea index (AHI) was 4.50 [2.30;10.15] times/h, and mean MSMSMS AHI was 3.63 [2.56; 6.43] times/h. There was a significant correlation between PSG AHI and AHI by MSMSMS (r = 0.935, P < 0.01). A Bland-Altamplot of PSG AHI and MSMSMS AHI was also used to assess the accuracy of MSMSMS. 95. 1% of the data was fallen in the 95% consistency areas. For AHI--5 times/h and nighttime minimum oxygen < 91% or obstructive apnea index( OAT) > 1 time/h and nighttime minimum oxygen saturation < 91 % as threshold value, the MSMSMS diagnosing sensitivity and specificity were 82.9% and 92.3%. The misdiagnosis rate and missed diagnosis rate were 7.7% and 17.1%. CONCLUSIONS: The MSMSMS offer a simple and comfortable method to monitor children's sleep. It improves the compliance in the process of sleep monitoring. Besides, as a diagnostic tool in the diagnosis of OSAHS on children has a high credibility in AHI.

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OBJECTIVE: To evaluate the diagnostic value of micro-movement sensitive mattress sleep monitoring system (MSMSMS) in children with obstructive sleep apnea hypopnea syndrome. METHODS: Sixty-five children with the complaint of sleep disorder were collected and detected by polysomnography and micro-sensitive mattress sleep monitoring system overnight at least seven hours. The date from two instruments were analyzed by computer automatically and then modified by two professional staff double-blind separately. The data about the diagnosis of sleep breath disorder and other sleep physiology information were compared between the two groups. RESULTS: Sixty-one cases finished the study finally. The mean age of these children was (7.0 ± 2.7) and the youngest was 3 years old, maximum was 13 years old, male children 46 cases, female patients 15 cases, body mass index (BMI) median [25 quantile; 75 quantile] 16.00 [14.80;17.5]kg/m2. Mean PSG measured apnea hypopnea index (AHI) was 4.50 [2.30;10.15] times/h, and mean MSMSMS AHI was 3.63 [2.56; 6.43] times/h. There was a significant correlation between PSG AHI and AHI by MSMSMS (r = 0.935, P < 0.01). A Bland-Altamplot of PSG AHI and MSMSMS AHI was also used to assess the accuracy of MSMSMS. 95. 1% of the data was fallen in the 95% consistency areas. For AHI--5 times/h and nighttime minimum oxygen < 91% or obstructive apnea index( OAT) > 1 time/h and nighttime minimum oxygen saturation < 91 % as threshold value, the MSMSMS diagnosing sensitivity and specificity were 82.9% and 92.3%. The misdiagnosis rate and missed diagnosis rate were 7.7% and 17.1%. CONCLUSIONS: The MSMSMS offer a simple and comfortable method to monitor children's sleep. It improves the compliance in the process of sleep monitoring. Besides, as a diagnostic tool in the diagnosis of OSAHS on children has a high credibility in AHI.

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Available abstract

OBJECTIVE: To evaluate the diagnostic value of micro-movement sensitive mattress sleep monitoring system (MSMSMS) in children with obstructive sleep apnea hypopnea syndrome. METHODS: Sixty-five children with the complaint of sleep disorder were collected and detected by polysomnography and micro-sensitive mattress sleep monitoring system overnight at least seven hours. The date from two instruments were analyzed by computer automatically and then modified by two professional staff double-blind separately. The data about the diagnosis of sleep breath disorder and other sleep physiology information were compared between the two groups. RESULTS: Sixty-one cases finished the study finally. The mean age of these children was (7.0 ± 2.7) and the youngest was 3 years old, maximum was 13 years old, male children 46 cases, female patients 15 cases, body mass index (BMI) median [25 quantile; 75 quantile] 16.00 [14.80;17.5]kg/m2. Mean PSG measured apnea hypopnea index (AHI) was 4.50 [2.30;10.15] times/h, and mean MSMSMS AHI was 3.63 [2.56; 6.43] times/h. There was a significant correlation between PSG AHI and AHI by MSMSMS (r = 0.935, P < 0.01). A Bland-Altamplot of PSG AHI and MSMSMS AHI was also used to assess the accuracy of MSMSMS. 95. 1% of the data was fallen in the 95% consistency areas. For AHI--5 times/h and nighttime minimum oxygen < 91% or obstructive apnea index( OAT) > 1 time/h and nighttime minimum oxygen saturation < 91 % as threshold value, the MSMSMS diagnosing sensitivity and specificity were 82.9% and 92.3%. The misdiagnosis rate and missed diagnosis rate were 7.7% and 17.1%. CONCLUSIONS: The MSMSMS offer a simple and comfortable method to monitor children's sleep. It improves the compliance in the process of sleep monitoring. Besides, as a diagnostic tool in the diagnosis of OSAHS on children has a high credibility in AHI.

Key concepts: Medicine, Polysomnography, Hypopnea, Obstructive sleep apnea, Apnea, Body mass index, Oxygen saturation, Apnea–hypopnea index

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[Clinical research on micro-movement sensitive mattress sleep monitoring system in children with obstructive sleep apnea hypopnea syndrome]. — Research Paper | ScholarLens