Performance Evaluation of a Smart Bed Technology against Polysomnography
The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PS...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/7/2605 |
| _version_ | 1797437687383195648 |
|---|---|
| author | Farzad Siyahjani Gary Garcia Molina Shawn Barr Faisal Mushtaq |
| author_facet | Farzad Siyahjani Gary Garcia Molina Shawn Barr Faisal Mushtaq |
| author_sort | Farzad Siyahjani |
| collection | DOAJ |
| description | The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen’s kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions. |
| first_indexed | 2024-03-09T11:26:13Z |
| format | Article |
| id | doaj.art-0eefc41861d14a4e95c34edbbb8282e1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:26:13Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0eefc41861d14a4e95c34edbbb8282e12023-12-01T00:01:51ZengMDPI AGSensors1424-82202022-03-01227260510.3390/s22072605Performance Evaluation of a Smart Bed Technology against PolysomnographyFarzad Siyahjani0Gary Garcia Molina1Shawn Barr2Faisal Mushtaq3Sleep Number® Labs, San Jose, CA 95113, USASleep Number® Labs, San Jose, CA 95113, USASleep Number® Labs, San Jose, CA 95113, USASleep Number® Labs, San Jose, CA 95113, USAThe Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen’s kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions.https://www.mdpi.com/1424-8220/22/7/2605ballistocardiographybreathing rateheart rate |
| spellingShingle | Farzad Siyahjani Gary Garcia Molina Shawn Barr Faisal Mushtaq Performance Evaluation of a Smart Bed Technology against Polysomnography Sensors ballistocardiography breathing rate heart rate |
| title | Performance Evaluation of a Smart Bed Technology against Polysomnography |
| title_full | Performance Evaluation of a Smart Bed Technology against Polysomnography |
| title_fullStr | Performance Evaluation of a Smart Bed Technology against Polysomnography |
| title_full_unstemmed | Performance Evaluation of a Smart Bed Technology against Polysomnography |
| title_short | Performance Evaluation of a Smart Bed Technology against Polysomnography |
| title_sort | performance evaluation of a smart bed technology against polysomnography |
| topic | ballistocardiography breathing rate heart rate |
| url | https://www.mdpi.com/1424-8220/22/7/2605 |
| work_keys_str_mv | AT farzadsiyahjani performanceevaluationofasmartbedtechnologyagainstpolysomnography AT garygarciamolina performanceevaluationofasmartbedtechnologyagainstpolysomnography AT shawnbarr performanceevaluationofasmartbedtechnologyagainstpolysomnography AT faisalmushtaq performanceevaluationofasmartbedtechnologyagainstpolysomnography |