One Goal, Many Methods: Understanding Accuracy Differences in HSAT

Dr. Chelsie Rohrscheib, Ph.D.

Millions of Americans are tested for sleep apnea at home. Home sleep apnea tests (HSATs) derive an apnea-hypopnea index (AHI) from different physiologic signals: some measure airflow or respiratory effort directly, while others track secondary signals like heart rate and blood oxygen changes and use algorithms to estimate whether a patient stopped breathing.

Sleep apnea is common worldwide and is linked to heart disease, diabetes, and cognitive decline, so the signals a test uses and how closely its AHI agrees with polysomnography (PSG) for an individual patient both matter. This article explains how those signals differ and how to read validation statistics such as correlation and limits of agreement.

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HSATs may appear operationally similar, but the physiologic signals used to derive AHI influence diagnostic reliability. For clinicians, this distinction matters most in borderline disease, central apnea evaluation, and in patients with common cardiometabolic or autonomic comorbidities, where small differences in AHI can materially alter diagnosis and management.

At a high level, contemporary HSAT technologies fall into two broad categories:

• Direct respiratory sensing, as in Wesper Lab, which measures airflow and thoracic and abdominal respiratory effort, combined with oximetry [1, 2]. Signals are then evaluated based on AASM guidelines to determine severity and type of disorder.
  
  
• Biosignal-based systems, including photoplethysmography (PPG-HSAT) platforms, which estimate respiratory events indirectly using cardiovascular and autonomic proxies such as PPG, ECG-derived features, heart-rate variability, and oxygen desaturation [3].

While both approaches generate an AHI value, they differ in how respiratory events are detected, classified, and interpreted.

How the Two Approaches Differ

What Each Approach Measures

Respiratory events are defined physiologically by reductions in airflow and/or ventilation, often accompanied by characteristic respiratory effort patterns, followed by secondary consequences such as oxygen desaturation and autonomic activation. Systems based on direct respiratory sensing detect events at their physiologic source, enabling:

• Direct identification of apneas and hypopneas
  
  
• Differentiation between obstructive and central mechanisms
  
  
• Event detection that does not depend on intact autonomic signaling
  
  

By contrast, PPG-HSAT and other biosignal-based approaches primarily detect downstream consequences of respiratory events, including:

• Heart-rate variability
  
  
• ECG-derived features
  
  
• Peripheral or autonomic responses
  
  
• Oxygen desaturation
  
  

These signals are informative, but they are proxies for the respiratory event itself, which limits their specificity.

Factors That Affect PPG-Based HSAT Performance

PPG-based HSAT systems rely on optical detection of blood volume changes to estimate respiratory disturbances. PPG can provide useful physiologic information, and its performance depends on signal quality, sensor location, and susceptibility to motion and perfusion artifacts, which can vary in home testing environments.

Sensor Location and PPG Signal Quality

PPG signal fidelity depends on sensor placement. Finger-based PPG sensors benefit from relatively high and stable perfusion, which is why they are widely used for pulse oximetry. Chest-mounted PPG sensors operate in a lower-perfusion environment and can be more susceptible to motion artifact, posture-related signal distortion, and variable tissue contact during sleep.

As a result, chest-based PPG signals can show:

• Lower signal-to-noise ratio
  
  
• Increased susceptibility to motion and positional artifact
  
  
• Greater signal dropout during sleep-related movement
  
  
• Reduced stability in real-world home use
  
  

These signal-level limitations constrain the reliability of downstream features derived from PPG, including oxygen desaturation timing, pulse-rate variability, and cardiopulmonary coupling metrics.

Impact on Respiratory Event Estimation

Because PPG-HSAT systems do not measure airflow or respiratory effort directly, respiratory events are estimated from secondary physiologic responses. When the underlying PPG signal is degraded, the estimate can become less reliable.

In practical terms, degraded PPG signal quality can increase:

• Missed hypopneas without prominent desaturation
  
  
• False event detection driven by motion or non-respiratory autonomic arousals
  
  
• Imprecise event timing relative to true airflow cessation
  
  
• Increased dispersion of individual AHI estimates
  
  

Central Sleep Apnea and Respiratory Effort

Central sleep apnea (CSA) requires documentation of absent respiratory effort during airflow cessation. Without direct airflow and respiratory effort measurements, differentiating between central and obstructive events is difficult.

Biosignal-based systems may detect that a physiologic disturbance occurred (e.g., desaturation or autonomic change), but without an effort signal they cannot show whether airflow cessation occurred in the presence or absence of respiratory effort.

Clinically, event typing matters in patients with:

• Heart failure
  
  
• Opioid exposure
  
  
• Neurologic disease
  
  
• Mixed or complex sleep apnea
  
  

In these populations, accurate event typing is essential for selecting appropriate therapy (e.g., CPAP optimization versus adaptive servo-ventilation or further cardiopulmonary evaluation). Direct measurement of airflow and respiratory effort allows central and obstructive patterns to be evaluated using physiologic criteria.

Common Comorbidities and Biosignal-Based Systems

Because PPG-HSAT platforms rely heavily on cardiovascular and autonomic features, performance may degrade in conditions that alter these systems, including:

Arrhythmias (particularly atrial fibrillation).
Irregular RR intervals distort ECG-derived features and disrupt temporal coupling between respiratory events and autonomic responses, increasing both false positives and false negatives. Large multi-center studies have found that ~34% of patients with AFib have comorbid OSA [4].

Peripheral vascular disease and impaired perfusion.
Reduced or unstable peripheral circulation compromises PPG signal fidelity, particularly during sleep-related vasoconstriction.

Autonomic dysfunction.
Seen in diabetes, dysautonomia, Parkinsonian syndromes, and post-viral states, autonomic impairment blunts sympathetic responses that biosignal-based algorithms depend on to identify respiratory events.

These conditions are common in real-world sleep clinic populations [5]. Direct respiratory sensing does not depend on intact autonomic signaling to detect airflow limitation.

Correlation and Limits of Agreement

Validation studies of HSAT technologies frequently emphasize correlation coefficients (e.g., Pearson r) to demonstrate agreement with polysomnography (PSG). Correlation reflects population-level tracking, whether PSG-derived apnea-hypopnea index (AHI) values are generally associated with device-derived AHI values, but does not quantify the accuracy of an individual patient’s measurement. Individual-level diagnostic uncertainty is instead captured by Bland-Altman limits of agreement (LoA), which describe the expected dispersion of device-derived AHI values around the PSG reference.

Wider limits of agreement mean that an individual patient's device-reported AHI can differ more from the PSG value. This matters most in the none-to-mild and mild-to-moderate ranges, where small absolute differences in AHI affect diagnosis, payer eligibility, and treatment decisions.

In a Wesper Lab versus PSG comparison using 3% hypopnea criteria (n = 44), Wesper Lab showed over 95% AHI correlation to in-lab PSG, with Bland-Altman limits of agreement of −8.05 to +6.38 events/hour (JCSM 2023) [1]. When the Wesper automated scoring algorithm was evaluated against scoring by sleep physicians (n = 139), the correlation was r = 0.98 [2].

Biosignal-based platforms rely on downstream cardiovascular and autonomic responses to estimate respiratory events, so their performance depends on signal quality, rhythm regularity, autonomic function, and perfusion. Wesper Lab detects respiratory events through airflow and respiratory effort, which reduces reliance on secondary proxies.

From a diagnostic standpoint, limits of agreement have clinical implications. Wider limits of agreement increase the risk of misclassification near AHI thresholds of 5, 15, and 30 events/hour, add measurement variability to physiologic night-to-night variability in longitudinal monitoring, and add uncertainty in patients with borderline disease, REM- or position-dependent OSA, or cardiovascular and autonomic comorbidities.

Limits of agreement matter most in patients with borderline or mild OSA, REM- or position-dependent disease, and in those with cardiovascular or autonomic comorbidities, where small absolute AHI differences can materially alter diagnosis and care pathways.

In simple terms:

• Correlation answers: Does this device trend with PSG across a population?
  
  
• Limits of agreement answer: How far off could this be for my patient?
  
  

Diagnostic Implications for Clinical Practice

Because Wesper Lab measures airflow and thoracic and abdominal respiratory effort, clinicians can evaluate central and obstructive patterns directly.

Wesper Lab can be used for:

• Borderline or mild OSA where small AHI errors change diagnosis
  
  
• Suspected central or complex sleep apnea
  
  
• Patients with arrhythmias, vascular disease, or autonomic dysfunction
  
  
• Longitudinal monitoring where consistent respiratory physiology is required
  
  
• Clinical pathways requiring event classification as well as event detection
  

Conclusion

Correlation with PSG describes how a device tracks AHI across a population, while limits of agreement describe how far an individual result may differ from the PSG value. Both are worth reviewing alongside the signals a test measures.

Wesper Lab measures airflow and thoracic and abdominal respiratory effort, the physiologic signals used to define apneas, hypopneas and central events.

For clinicians, the question to ask of any HSAT is how closely it agrees with PSG for the individual patient as well as on average.

References

1. Raphelson JR, Ahmed IM, Ancoli-Israel S, Ojile J, Pearson S, Bennett N, Uhles ML, Rohrscheib C, Malhotra A. Evaluation of a novel device to assess obstructive sleep apnea and body position. J Clin Sleep Med. 2023 Sep 1;19(9):1643-1649. doi: 10.5664/jcsm.
2. Rohrscheib C, Moura AA, Raphelson J, Orr JE, Patel RP, Malhotra A. Evaluation of an automated sleep apnea scoring algorithm via the Wesper Lab home sleep apnea test. Sleep Med. 2026 Feb 9;141:108828. doi: 10.1016/j.sleep.2026.108828.
3. Goldstein C, Ghanbari H, Sharma S, Collop N, Namen A, Kirsch DB, Drucker M, Khayat R, Pollock M, Torstrick B, Walsh C, Herreshoff E, Frankel DS, Rosen IM. Polysomnography validation of SANSA to detect obstructive sleep apnea. Front Neurol. 2025 Jun 16;16:1592690. doi: 10.3389/fneur.2025.1592690.
4. Zhang D, Ma Y, Xu J, Yi F. Association between obstructive sleep apnea (OSA) and atrial fibrillation (AF): A dose-response meta-analysis. Medicine (Baltimore). 2022 Jul 29;101(30):e29443. doi: 10.1097/MD.0000000000029443.
5. Steinberg R, Spector AR, McVeigh T, Fudim M. Home Sleep Apnoea Testing: Advances, Challenges and Considerations in Heart Failure. Card Fail Rev. 2025 Nov 20;11:e29. doi: 10.15420/cfr.2025.29.
6. Magalang UJ, Chen NH, Cistulli PA, Fedson AC, Gíslason T, Hillman D, Penzel T, Tamisier R, Tufik S, Phillips G, Pack AI; SAGIC Investigators. Agreement in the scoring of respiratory events and sleep among international sleep centers. Sleep. 2013 Apr 1;36(4):591-6. doi: 10.5665/sleep.2552.