The patient himself was an unlikely candidate for a cardiac scare. He had been a consistent, well-controlled CPAP user for thirteen years, treated for obstructive sleep apnea with a fixed pressure of 7.6 millibars delivered through a nasal mask. He was not obese, his weight was stable, and his only medication was aspirin. A recent medical evaluation had already given him a clean bill of health where it counted: a normal neurological assessment, a normal brain MRI, a normal cardiac echocardiogram and a normal ergospirometry test. So when he noticed residual respiratory events accumulating on his device across several consecutive mornings, and the downloaded SD-card data from his ResMed AirSense10 revealed a total apnea-hypopnea index of 8.7 events per hour with central apneas at 8.5 per hour and Cheyne–Stokes respiration flagged at 11 percent of the record, the finding landed as a genuine surprise. The machine’s leak data, at a median of 1.2 liters per minute and a 95th percentile of 8 liters per minute, ruled out the most common technical culprit: a leaky mask corrupting the flow signal.

With device-detected Cheyne–Stokes respiration on the table, the team followed the logical sequence laid out in sleep medicine practice. The gold standard for confirming the pattern is fully attended polysomnography under American Academy of Sleep Medicine criteria, because it captures brain activity and precise sleep staging. Respiratory polygraphy, which records airflow, respiratory effort and oxygen saturation without EEG, is accepted as a reasonable first screening step when a CPAP device raises the alarm, ideally with a microphone attached to help distinguish obstructive from central events. The physicians withdrew CPAP for five days and ran a 659-minute polygraphy recording. The result reframed everything. The study showed a clear obstructive sleep apnea pattern, with an obstructive apnea-hypopnea index of 21.8 per hour within a total index of 27.8 per hour, an oxygen desaturation index of 29.3 per hour, and prominent snoring. There were no Cheyne–Stokes episodes. The central apnea-hypopnea index of 6.5 per hour was driven largely by mixed apneas carrying an initial central component, and the overall tracings produced airflow patterns that could genuinely mimic the periodic breathing signature of Cheyne–Stokes respiration.

Why would a CPAP algorithm be so easily misled? The answer lies in how these machines measure breathing. The AirSense10 does not watch the chest or abdomen; it infers everything from airflow, using a forced oscillation technique in which small pressure pulses are injected into the circuit. When the measured flow falls, the device checks whether the oscillations dissipate. If they fade away, the upper airway is presumed open, and the event is classified as central. If the oscillations persist, the airway is presumed closed and the event is obstructive. To label Cheyne–Stokes respiration, the software additionally searches for a characteristic flow pattern: a specific crescendo-decrescendo cycle length, defined episode durations, and the presence of central events. The criteria and algorithms involved have never been standardized across manufacturers, and each company’s thresholds remain proprietary. The Belgian team points to earlier work from the AlertApnée study, in which researchers documented that apparent onset of Cheyne–Stokes respiration during CPAP telemonitoring was attributable to obstructive events occurring just before the flagged episodes, and that the AirSense10 software recurrently misclassified typical obstructive apneas as Cheyne–Stokes respiration.

Layered on top of the algorithmic blind spot is a physiological phenomenon that makes the mimicry genuinely convincing: ventilatory control instability. Obstructive sleep apnea emerges from an interplay of pathophysiological mechanisms, one of which is loop gain, a measure of how sensitive the breathing control system is to disturbance. Loop gain has three components, describing the control, the exchange and the connection elements of the ventilatory system. When loop gain exceeds one, the system becomes hypersensitive, overcorrecting every perturbation with an excessive ventilatory response that can manifest as periodic breathing or full-blown Cheyne–Stokes respiration. When loop gain stays below one, breathing remains stable. A high loop gain during non-rapid eye movement sleep in a patient with obstructive sleep apnea can therefore generate airflow patterns that look, to an algorithm reading only the flow channel, exactly like the real thing. The researchers hypothesized that this was precisely what happened in their patient: his recent nasal obstruction had destabilized his ventilatory control, and his CPAP device’s pattern-matching software read the resulting pseudo-periodic airflow as central disease.

The treatment course told the story in real time. First, the clinicians raised the fixed CPAP pressure progressively from 7.6 to 11 millibars and switched the patient from a nasal mask to a naso-buccal mask, deliberately bypassing the obstructed nasal passages. The change partially worked. The software stopped flagging Cheyne–Stokes respiration entirely, but residual events persisted, with the flow-based apnea-hypopnea index stuck at 11 per hour. Then came the decisive intervention, and it was almost mundane: nasal corticosteroids for the obstruction itself. One month later, with the CPAP settings completely unchanged, the flow-based index dropped below 5 per hour, the conventional threshold for well-controlled sleep apnea. The breathing instability had not been a new cardiac or neurological disease; it was a mechanical problem in the nose propagating upward into the control of breathing, and once the nose was treated, the entire pseudocentral picture dissolved.

The mechanism the authors propose is a chain of escalating instability. Nasal obstruction increases inspiratory effort, because the patient must pull harder against resistance to move the same volume of air. Greater effort makes arousals from sleep more frequent, and each arousal triggers a burst of hyperventilation. In a system primed with high loop gain, that post-arousal ventilation overshoots, blowing off carbon dioxide below the threshold needed to sustain breathing drive and setting up the next pause in an oscillating loop. Crucially, the authors emphasize, this is not a phenotypic conversion of obstructive sleep apnea into central sleep apnea. The patient’s underlying disease did not change character. Instead, the nasal blockage caused a secondary destabilization of ventilatory control, generating mixed apneas and unstable, pseudo-periodic airflow that the device’s flow-only analysis could not reliably untangle from true Cheyne–Stokes respiration.

The broader implication is a cautionary tale for the era of connected medical devices. CPAP telemonitoring has become a powerful surveillance tool, capable of flagging emergent central apnea and, by extension, serious cardiac disease years before symptoms demand attention. But that power depends on algorithms that, in this case at least, could not see respiratory effort and therefore could not reliably distinguish obstructive or unstable breathing from central periodicity. The authors argue that device-based algorithms relying solely on airflow are known to misclassify obstructive or unstable breathing as Cheyne–Stokes respiration in the absence of effort signals, and they call for two concrete improvements: better detection accuracy and standardized, manufacturer-independent criteria for what counts as device-detected Cheyne–Stokes respiration. Until then, the practical message for clinicians is that a CPAP report announcing new-onset central apnea in a stable, long-treated patient should trigger confirmatory testing rather than reflexive alarm, and that an apparently mundane complaint such as a stuffy nose deserves a place on the differential diagnosis list.

The case, documented with device downloads, polygraphy tracings and a straightforward pharmacological resolution, adds a human-scale illustration to a problem that will only grow as home-based sleep data multiplies. Millions of nightly records flow from CPAP machines into cloud dashboards, screened by software and reviewed by clinicians who may never hear the patient’s own observation that something changed, in this instance the recent onset of nasal obstruction, before the numbers did. The Belgian team’s conclusion is measured but pointed: physicians should be aware of the limits of CPAP data collection reliability. A blocked nose, it turns out, can speak fluent Cheyne–Stokes to an algorithm that listens only to airflow, and only a careful look at the whole patient can tell the difference between a cardiac warning and a treatable case of congestion.

Subject of Research: False detection of Cheyne–Stokes respiration by CPAP devices due to nasal obstruction-induced ventilatory instability

Article Title: False Detection of Cheyne–Stokes Respiration on Continuous Positive Airway Pressure Resolved After Treatment of Nasal Obstruction

Article References: Castermans, E., Impens, D., Libert, W., & Bruyneel, M. (2026). False Detection of Cheyne–Stokes Respiration on Continuous Positive Airway Pressure Resolved After Treatment of Nasal Obstruction. Respirology Case Reports, 14(9), Article e70745. https://doi.org/10.1002/rcr2.70745

Image Credits: AI Generated

DOI: 10.1002/rcr2.70745

Keywords: sleep apnea, CPAP, Cheyne–Stokes respiration, nasal obstruction, ventilatory instability, loop gain, respiratory polygraphy, polysomnography, central sleep apnea, telemonitoring, case report, False