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OSA is a very prevalent condition in the general population, but still many patients remain undiagnosed and untreated. Prolonged, untreated OSA is an independent risk factor for major cardiovascular morbidity and mortality. Therefore, timely diagnosis and treatment are required.
Polysomnography (PSG) remains to this day the gold standard for diagnosing sleep apnea. A standard PSG (type I) is performed in a sleep laboratory in the presence of specialized sleep technicians and utilizes EEG, electrooculogram (EOG), and electromyogram (EMG) to determine sleep stages, oronasal thermal and pressure transducer sensors to monitor airflow, respiratory inductance plethysmography to record respiratory effort, EMG for limb movements, pulse oximetry (PulseOx), ECG, and video or body sensor devices to confirm body position. Rising rates of sleep testing have created demand for an alternative to cumbersome, costly, and resource-intensive in-lab PSGs. As such, home sleep apnea testing (HSAT) has emerged as a simpler, more accessible, and cost-effective alternative diagnostic tool.
In 2007, the American Academy of Sleep Medicine (AASM) endorsed Portable Monitoring (PM) as an alternative to standard PSG, with the caveat that it should be used only in patients with a high pretest probability of sleep apnea, without respiratory or cardiovascular disorders and comorbid sleep disorders. All HSAT devices (type II-IV) are required to have a minimum of an oronasal thermal sensor/nasal pressure transducer, respiratory inductance plethysmography, and PulseOx. A major limitation of most HSAT devices is the lack of EEG, preventing detection of cortical arousals and wake time, forcing the use of total recording time as a surrogate for total sleep time.
Peripheral arterial tonometry (PAT)-based HSAT devices are unique in this respect, as their proprietary algorithms allow estimates of total sleep time by monitoring changes in peripheral vascular tone. Anyone who has seen a PAT-based HSAT may have noticed very different outputs from traditional HSATs.
PAT is based on the concept that airflow obstruction may lead to a surge in sympathetic tone, causing vasoconstriction and reduced blood volume in the peripheral vascular bed. A PAT-based device measures relative changes in blood volume and combines this information with actigraphy signals, PulseOx, and heart rate to diagnose the presence of respiratory events. Sleep apnea severity stratification is accomplished by the use of pAHI or pRDI (PAT-based apnea-hypopnea index and respiratory disturbance index, respectively).
PAT-based technology was first approved by the FDA in 2001 as a diagnostic tool for sleep apnea. The 2 best-known medical devices are WatchPAT® and NightOwl®, both of which have been FDA-approved and studied against PSG. To obtain an accurate and sustainable PAT signal, WatchPAT has a pneumo-optic finger probe designed to generate a uniform, subdiastolic pressure on the finger that minimizes venous blood pooling, prevents uncontrolled venous backflow, and effectively unloads the arterial wall tension without blocking digital arterial flow. NightOwl is a smaller device, with a single fingertip sensor that acquires actigraphy and PPG data to measure heart rate, Pulse Ox, and PAT.
The physiological basis of PAT relies on photoplethysmography (PPG), a noninvasive optical monitoring technique that generates a waveform, which ultimately correlates with the circulatory volume of the respective tissue.1 The PPG technology relies on the fact that when a specific tissue is exposed to light signal of a specific wavelength, its absorbance by tissue fluctuates with arterial pulsations. Pulse oximetry represents the most used application of PPG. Recent advances in PPG signal analysis have fueled its use in clinical and consumer sleep technologies and allowed new capabilities, including capturing heart rate and rhythm, pulse rate variability, arterial stiffness, and even—with somewhat less accuracy—energy expenditure, maximum O2 consumption, and blood pressure. Combining actigraphy monitoring with PPG technology took both consumer and medical-grade sleep technologies further, allowing the estimation of parameters such as sleep stages, sleep times, and respiratory events. With myriad new sleep trackers claiming to assess total sleep time, wake time, light or deep sleep, and even respiratory events, the obvious clinical question is centered on their comparative accuracy, as well against more traditional PM and the gold standard PSG.
Numerous studies have evaluated the efficacy of PAT-based devices for diagnosing sleep apnea, with variable findings. Many have shown good correlations between mean AHI and pAHI. Others have highlighted significant discrepancies in the measurements between PSG and PAT, questioning the reliability of PAT-based devices in the diagnosis and severity stratification of OSA. One meta-analysis of 14 studies showed a high degree of correlation between pAHI and AHI.2 Another study reported a concordance of 80% between PAT-based testing and consecutive PSG, with an increase to 86% at a higher AHI (>15/h).3 A subsequent meta-analysis showed that PAT was significantly less sensitive for diagnosing OSA than PSG, particularly for mild or moderate severity disease, emphasizing the need for further confirmation with PSG when faced with inconclusive or negative results.4 A large sleep clinic-based cohort study of 500 patients with OSA showed that WatchPAT devices misclassify OSA in a sizeable proportion of patients (30%-50%), leading to both over- and under-estimation of severity.5 Van Pee, et al, found that their PAT-based HSAT NightOwl performed better, using both the 3% and 4% hypopnea scoring rules and a novel near-border zone labeling.6
Some of the discordance in AHI between PAT and PSG appears to be related to age and sex. In our large sample comparing PAT to PSG, we found that using PAT-based data in concert with demographic (age, gender) and anthropometric (neck circumference, body mass index) variables improved the diagnostic accuracy of PAT-based testing.7 Another study concluded that manual scoring of WatchPAT automated results improved concordance with PSG, particularly in older participants and women. Several studies on WatchPAT recordings have demonstrated significant artifacts and inaccuracies in the PulseOx data. Although WatchPAT employs automated algorithms to remove erroneous data, a thorough visual inspection and manual correction of study data is still essential to derive accurate results.
Recent studies have found that PAT-based tests can also differentiate between central and obstructive respiratory events by using pulse signal upstroke variations caused by changes in intrathoracic pressure and respiratory/chest wall movement recorded by body position sensors, but large-scale studies are needed to confirm these findings. Korkalainen, et al, recently employed a deep-learning model to perform sleep staging on the PPG PulseOx signals from nearly 900 PSGs in patients with suspected OSA.8 The deep learning approach enabled the differentiation of sleep stages and accurate estimation of the total sleep time. Going forward, this could easily enhance the diagnostic yield of PM recordings and enable cost-efficient, long-term monitoring of sleep.
Although PAT-based home sleep tests have emerged as a simple and convenient option for the evaluation of sleep apnea, several studies have highlighted their limited sensitivity as a screening tool for mild and moderate cases of sleep apnea. Furthermore, the scope of these tests remains limited, rendering them rather unsuitable for assessment of more complex sleep disorders like narcolepsy or restless leg syndrome. Therefore, when OSA is suspected, the PAT-based sleep study is a good screening tool, but negative tests should not preclude further investigation. Where a high probability of sleep apnea exists but PAT-based testing shows no or mild OSA, an in-lab sleep study should be performed.
References
1. Ryals S, Chiang A, Schutte-Rodin S, et al. Photoplethysmography -- new applications for an old technology: a sleep technology review. J Clin Sleep Med. 2023;19(1):189-195.
2. Yalamanchali S, Farajian V, Hamilton C, Pott TR, Samuelson CG, Friedman M. Diagnosis of obstructive sleep apnea by peripheral arterial tonometry: meta-analysis. JAMA Otolaryngol Head Neck Surg. 2013;139(12):1343-1350.
3. Röcken J, Schumann DM, Herrmann MJ, et al. Peripheral arterial tonometry versus polysomnography in suspected obstructive sleep apnoea. Eur J Med Res. 2023;28(1):251.
4. Iftikhar IH, Finch CE, Shah AS, Augunstein CA, Ioachimescu OC. A meta-analysis of diagnostic test performance of peripheral arterial tonometry studies. J Clin Sleep Med. 2022;18(4):1093-1102.
5. Ioachimescu OC, Allam JS, Samarghandi A, et al. Performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea in a large sleep clinic cohort. J Clin Sleep Med. 2020;16(10):1663-1674.
6. Van Pee B, Massie F, Vits S, et al. A multicentric validation study of a novel home sleep apnea test based on peripheral arterial tonometry. Sleep. 2022;45(5).
7. Ioachimescu OC, Dholakia SA, Venkateshiah SB, et al. Improving the performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea. J Investig Med. 2020;68(8):1370-1378.
8. Korkalainen H, Aakko J, Duce B, et al. Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea. Sleep. 2020;43(11).
OSA is a very prevalent condition in the general population, but still many patients remain undiagnosed and untreated. Prolonged, untreated OSA is an independent risk factor for major cardiovascular morbidity and mortality. Therefore, timely diagnosis and treatment are required.
Polysomnography (PSG) remains to this day the gold standard for diagnosing sleep apnea. A standard PSG (type I) is performed in a sleep laboratory in the presence of specialized sleep technicians and utilizes EEG, electrooculogram (EOG), and electromyogram (EMG) to determine sleep stages, oronasal thermal and pressure transducer sensors to monitor airflow, respiratory inductance plethysmography to record respiratory effort, EMG for limb movements, pulse oximetry (PulseOx), ECG, and video or body sensor devices to confirm body position. Rising rates of sleep testing have created demand for an alternative to cumbersome, costly, and resource-intensive in-lab PSGs. As such, home sleep apnea testing (HSAT) has emerged as a simpler, more accessible, and cost-effective alternative diagnostic tool.
In 2007, the American Academy of Sleep Medicine (AASM) endorsed Portable Monitoring (PM) as an alternative to standard PSG, with the caveat that it should be used only in patients with a high pretest probability of sleep apnea, without respiratory or cardiovascular disorders and comorbid sleep disorders. All HSAT devices (type II-IV) are required to have a minimum of an oronasal thermal sensor/nasal pressure transducer, respiratory inductance plethysmography, and PulseOx. A major limitation of most HSAT devices is the lack of EEG, preventing detection of cortical arousals and wake time, forcing the use of total recording time as a surrogate for total sleep time.
Peripheral arterial tonometry (PAT)-based HSAT devices are unique in this respect, as their proprietary algorithms allow estimates of total sleep time by monitoring changes in peripheral vascular tone. Anyone who has seen a PAT-based HSAT may have noticed very different outputs from traditional HSATs.
PAT is based on the concept that airflow obstruction may lead to a surge in sympathetic tone, causing vasoconstriction and reduced blood volume in the peripheral vascular bed. A PAT-based device measures relative changes in blood volume and combines this information with actigraphy signals, PulseOx, and heart rate to diagnose the presence of respiratory events. Sleep apnea severity stratification is accomplished by the use of pAHI or pRDI (PAT-based apnea-hypopnea index and respiratory disturbance index, respectively).
PAT-based technology was first approved by the FDA in 2001 as a diagnostic tool for sleep apnea. The 2 best-known medical devices are WatchPAT® and NightOwl®, both of which have been FDA-approved and studied against PSG. To obtain an accurate and sustainable PAT signal, WatchPAT has a pneumo-optic finger probe designed to generate a uniform, subdiastolic pressure on the finger that minimizes venous blood pooling, prevents uncontrolled venous backflow, and effectively unloads the arterial wall tension without blocking digital arterial flow. NightOwl is a smaller device, with a single fingertip sensor that acquires actigraphy and PPG data to measure heart rate, Pulse Ox, and PAT.
The physiological basis of PAT relies on photoplethysmography (PPG), a noninvasive optical monitoring technique that generates a waveform, which ultimately correlates with the circulatory volume of the respective tissue.1 The PPG technology relies on the fact that when a specific tissue is exposed to light signal of a specific wavelength, its absorbance by tissue fluctuates with arterial pulsations. Pulse oximetry represents the most used application of PPG. Recent advances in PPG signal analysis have fueled its use in clinical and consumer sleep technologies and allowed new capabilities, including capturing heart rate and rhythm, pulse rate variability, arterial stiffness, and even—with somewhat less accuracy—energy expenditure, maximum O2 consumption, and blood pressure. Combining actigraphy monitoring with PPG technology took both consumer and medical-grade sleep technologies further, allowing the estimation of parameters such as sleep stages, sleep times, and respiratory events. With myriad new sleep trackers claiming to assess total sleep time, wake time, light or deep sleep, and even respiratory events, the obvious clinical question is centered on their comparative accuracy, as well against more traditional PM and the gold standard PSG.
Numerous studies have evaluated the efficacy of PAT-based devices for diagnosing sleep apnea, with variable findings. Many have shown good correlations between mean AHI and pAHI. Others have highlighted significant discrepancies in the measurements between PSG and PAT, questioning the reliability of PAT-based devices in the diagnosis and severity stratification of OSA. One meta-analysis of 14 studies showed a high degree of correlation between pAHI and AHI.2 Another study reported a concordance of 80% between PAT-based testing and consecutive PSG, with an increase to 86% at a higher AHI (>15/h).3 A subsequent meta-analysis showed that PAT was significantly less sensitive for diagnosing OSA than PSG, particularly for mild or moderate severity disease, emphasizing the need for further confirmation with PSG when faced with inconclusive or negative results.4 A large sleep clinic-based cohort study of 500 patients with OSA showed that WatchPAT devices misclassify OSA in a sizeable proportion of patients (30%-50%), leading to both over- and under-estimation of severity.5 Van Pee, et al, found that their PAT-based HSAT NightOwl performed better, using both the 3% and 4% hypopnea scoring rules and a novel near-border zone labeling.6
Some of the discordance in AHI between PAT and PSG appears to be related to age and sex. In our large sample comparing PAT to PSG, we found that using PAT-based data in concert with demographic (age, gender) and anthropometric (neck circumference, body mass index) variables improved the diagnostic accuracy of PAT-based testing.7 Another study concluded that manual scoring of WatchPAT automated results improved concordance with PSG, particularly in older participants and women. Several studies on WatchPAT recordings have demonstrated significant artifacts and inaccuracies in the PulseOx data. Although WatchPAT employs automated algorithms to remove erroneous data, a thorough visual inspection and manual correction of study data is still essential to derive accurate results.
Recent studies have found that PAT-based tests can also differentiate between central and obstructive respiratory events by using pulse signal upstroke variations caused by changes in intrathoracic pressure and respiratory/chest wall movement recorded by body position sensors, but large-scale studies are needed to confirm these findings. Korkalainen, et al, recently employed a deep-learning model to perform sleep staging on the PPG PulseOx signals from nearly 900 PSGs in patients with suspected OSA.8 The deep learning approach enabled the differentiation of sleep stages and accurate estimation of the total sleep time. Going forward, this could easily enhance the diagnostic yield of PM recordings and enable cost-efficient, long-term monitoring of sleep.
Although PAT-based home sleep tests have emerged as a simple and convenient option for the evaluation of sleep apnea, several studies have highlighted their limited sensitivity as a screening tool for mild and moderate cases of sleep apnea. Furthermore, the scope of these tests remains limited, rendering them rather unsuitable for assessment of more complex sleep disorders like narcolepsy or restless leg syndrome. Therefore, when OSA is suspected, the PAT-based sleep study is a good screening tool, but negative tests should not preclude further investigation. Where a high probability of sleep apnea exists but PAT-based testing shows no or mild OSA, an in-lab sleep study should be performed.
References
1. Ryals S, Chiang A, Schutte-Rodin S, et al. Photoplethysmography -- new applications for an old technology: a sleep technology review. J Clin Sleep Med. 2023;19(1):189-195.
2. Yalamanchali S, Farajian V, Hamilton C, Pott TR, Samuelson CG, Friedman M. Diagnosis of obstructive sleep apnea by peripheral arterial tonometry: meta-analysis. JAMA Otolaryngol Head Neck Surg. 2013;139(12):1343-1350.
3. Röcken J, Schumann DM, Herrmann MJ, et al. Peripheral arterial tonometry versus polysomnography in suspected obstructive sleep apnoea. Eur J Med Res. 2023;28(1):251.
4. Iftikhar IH, Finch CE, Shah AS, Augunstein CA, Ioachimescu OC. A meta-analysis of diagnostic test performance of peripheral arterial tonometry studies. J Clin Sleep Med. 2022;18(4):1093-1102.
5. Ioachimescu OC, Allam JS, Samarghandi A, et al. Performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea in a large sleep clinic cohort. J Clin Sleep Med. 2020;16(10):1663-1674.
6. Van Pee B, Massie F, Vits S, et al. A multicentric validation study of a novel home sleep apnea test based on peripheral arterial tonometry. Sleep. 2022;45(5).
7. Ioachimescu OC, Dholakia SA, Venkateshiah SB, et al. Improving the performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea. J Investig Med. 2020;68(8):1370-1378.
8. Korkalainen H, Aakko J, Duce B, et al. Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea. Sleep. 2020;43(11).
OSA is a very prevalent condition in the general population, but still many patients remain undiagnosed and untreated. Prolonged, untreated OSA is an independent risk factor for major cardiovascular morbidity and mortality. Therefore, timely diagnosis and treatment are required.
Polysomnography (PSG) remains to this day the gold standard for diagnosing sleep apnea. A standard PSG (type I) is performed in a sleep laboratory in the presence of specialized sleep technicians and utilizes EEG, electrooculogram (EOG), and electromyogram (EMG) to determine sleep stages, oronasal thermal and pressure transducer sensors to monitor airflow, respiratory inductance plethysmography to record respiratory effort, EMG for limb movements, pulse oximetry (PulseOx), ECG, and video or body sensor devices to confirm body position. Rising rates of sleep testing have created demand for an alternative to cumbersome, costly, and resource-intensive in-lab PSGs. As such, home sleep apnea testing (HSAT) has emerged as a simpler, more accessible, and cost-effective alternative diagnostic tool.
In 2007, the American Academy of Sleep Medicine (AASM) endorsed Portable Monitoring (PM) as an alternative to standard PSG, with the caveat that it should be used only in patients with a high pretest probability of sleep apnea, without respiratory or cardiovascular disorders and comorbid sleep disorders. All HSAT devices (type II-IV) are required to have a minimum of an oronasal thermal sensor/nasal pressure transducer, respiratory inductance plethysmography, and PulseOx. A major limitation of most HSAT devices is the lack of EEG, preventing detection of cortical arousals and wake time, forcing the use of total recording time as a surrogate for total sleep time.
Peripheral arterial tonometry (PAT)-based HSAT devices are unique in this respect, as their proprietary algorithms allow estimates of total sleep time by monitoring changes in peripheral vascular tone. Anyone who has seen a PAT-based HSAT may have noticed very different outputs from traditional HSATs.
PAT is based on the concept that airflow obstruction may lead to a surge in sympathetic tone, causing vasoconstriction and reduced blood volume in the peripheral vascular bed. A PAT-based device measures relative changes in blood volume and combines this information with actigraphy signals, PulseOx, and heart rate to diagnose the presence of respiratory events. Sleep apnea severity stratification is accomplished by the use of pAHI or pRDI (PAT-based apnea-hypopnea index and respiratory disturbance index, respectively).
PAT-based technology was first approved by the FDA in 2001 as a diagnostic tool for sleep apnea. The 2 best-known medical devices are WatchPAT® and NightOwl®, both of which have been FDA-approved and studied against PSG. To obtain an accurate and sustainable PAT signal, WatchPAT has a pneumo-optic finger probe designed to generate a uniform, subdiastolic pressure on the finger that minimizes venous blood pooling, prevents uncontrolled venous backflow, and effectively unloads the arterial wall tension without blocking digital arterial flow. NightOwl is a smaller device, with a single fingertip sensor that acquires actigraphy and PPG data to measure heart rate, Pulse Ox, and PAT.
The physiological basis of PAT relies on photoplethysmography (PPG), a noninvasive optical monitoring technique that generates a waveform, which ultimately correlates with the circulatory volume of the respective tissue.1 The PPG technology relies on the fact that when a specific tissue is exposed to light signal of a specific wavelength, its absorbance by tissue fluctuates with arterial pulsations. Pulse oximetry represents the most used application of PPG. Recent advances in PPG signal analysis have fueled its use in clinical and consumer sleep technologies and allowed new capabilities, including capturing heart rate and rhythm, pulse rate variability, arterial stiffness, and even—with somewhat less accuracy—energy expenditure, maximum O2 consumption, and blood pressure. Combining actigraphy monitoring with PPG technology took both consumer and medical-grade sleep technologies further, allowing the estimation of parameters such as sleep stages, sleep times, and respiratory events. With myriad new sleep trackers claiming to assess total sleep time, wake time, light or deep sleep, and even respiratory events, the obvious clinical question is centered on their comparative accuracy, as well against more traditional PM and the gold standard PSG.
Numerous studies have evaluated the efficacy of PAT-based devices for diagnosing sleep apnea, with variable findings. Many have shown good correlations between mean AHI and pAHI. Others have highlighted significant discrepancies in the measurements between PSG and PAT, questioning the reliability of PAT-based devices in the diagnosis and severity stratification of OSA. One meta-analysis of 14 studies showed a high degree of correlation between pAHI and AHI.2 Another study reported a concordance of 80% between PAT-based testing and consecutive PSG, with an increase to 86% at a higher AHI (>15/h).3 A subsequent meta-analysis showed that PAT was significantly less sensitive for diagnosing OSA than PSG, particularly for mild or moderate severity disease, emphasizing the need for further confirmation with PSG when faced with inconclusive or negative results.4 A large sleep clinic-based cohort study of 500 patients with OSA showed that WatchPAT devices misclassify OSA in a sizeable proportion of patients (30%-50%), leading to both over- and under-estimation of severity.5 Van Pee, et al, found that their PAT-based HSAT NightOwl performed better, using both the 3% and 4% hypopnea scoring rules and a novel near-border zone labeling.6
Some of the discordance in AHI between PAT and PSG appears to be related to age and sex. In our large sample comparing PAT to PSG, we found that using PAT-based data in concert with demographic (age, gender) and anthropometric (neck circumference, body mass index) variables improved the diagnostic accuracy of PAT-based testing.7 Another study concluded that manual scoring of WatchPAT automated results improved concordance with PSG, particularly in older participants and women. Several studies on WatchPAT recordings have demonstrated significant artifacts and inaccuracies in the PulseOx data. Although WatchPAT employs automated algorithms to remove erroneous data, a thorough visual inspection and manual correction of study data is still essential to derive accurate results.
Recent studies have found that PAT-based tests can also differentiate between central and obstructive respiratory events by using pulse signal upstroke variations caused by changes in intrathoracic pressure and respiratory/chest wall movement recorded by body position sensors, but large-scale studies are needed to confirm these findings. Korkalainen, et al, recently employed a deep-learning model to perform sleep staging on the PPG PulseOx signals from nearly 900 PSGs in patients with suspected OSA.8 The deep learning approach enabled the differentiation of sleep stages and accurate estimation of the total sleep time. Going forward, this could easily enhance the diagnostic yield of PM recordings and enable cost-efficient, long-term monitoring of sleep.
Although PAT-based home sleep tests have emerged as a simple and convenient option for the evaluation of sleep apnea, several studies have highlighted their limited sensitivity as a screening tool for mild and moderate cases of sleep apnea. Furthermore, the scope of these tests remains limited, rendering them rather unsuitable for assessment of more complex sleep disorders like narcolepsy or restless leg syndrome. Therefore, when OSA is suspected, the PAT-based sleep study is a good screening tool, but negative tests should not preclude further investigation. Where a high probability of sleep apnea exists but PAT-based testing shows no or mild OSA, an in-lab sleep study should be performed.
References
1. Ryals S, Chiang A, Schutte-Rodin S, et al. Photoplethysmography -- new applications for an old technology: a sleep technology review. J Clin Sleep Med. 2023;19(1):189-195.
2. Yalamanchali S, Farajian V, Hamilton C, Pott TR, Samuelson CG, Friedman M. Diagnosis of obstructive sleep apnea by peripheral arterial tonometry: meta-analysis. JAMA Otolaryngol Head Neck Surg. 2013;139(12):1343-1350.
3. Röcken J, Schumann DM, Herrmann MJ, et al. Peripheral arterial tonometry versus polysomnography in suspected obstructive sleep apnoea. Eur J Med Res. 2023;28(1):251.
4. Iftikhar IH, Finch CE, Shah AS, Augunstein CA, Ioachimescu OC. A meta-analysis of diagnostic test performance of peripheral arterial tonometry studies. J Clin Sleep Med. 2022;18(4):1093-1102.
5. Ioachimescu OC, Allam JS, Samarghandi A, et al. Performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea in a large sleep clinic cohort. J Clin Sleep Med. 2020;16(10):1663-1674.
6. Van Pee B, Massie F, Vits S, et al. A multicentric validation study of a novel home sleep apnea test based on peripheral arterial tonometry. Sleep. 2022;45(5).
7. Ioachimescu OC, Dholakia SA, Venkateshiah SB, et al. Improving the performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea. J Investig Med. 2020;68(8):1370-1378.
8. Korkalainen H, Aakko J, Duce B, et al. Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea. Sleep. 2020;43(11).