Electrocardiographic assessment in obstructive sleep apnea: bridging pathophysiology and clinical practice

Obstructive sleep apnea syndrome (OSAS) represents one of the most prevalent sleep-related breathing disorders, affecting approximately 50% of men and 23% of women aged 40 years or older when defined by an apnea-hypopnea index ≥15 events/h (Heinzer et al., 2015).Beyond its impact on sleep architecture and daytime function, OSAS has emerged as a significant cardiovascular risk factor, with mounting evidence demonstrating associations with hypertension, coronary artery disease, heart failure, and cardiac arrhythmias (Javaheri et al., 2024). The repetitive cycles of upper airway obstruction, intermittent hypoxemia, and arousal-mediated sympathetic activation create a unique pathophysiological milieu that profoundly affects cardiac electrophysiology. While polysomnography remains the diagnostic gold standard for OSAS, electrocardiographic (ECG) monitoring offers complementary insights that extend beyond traditional sleep parameters. The heart's electrical signature of OSAS reflects complex autonomic, hemodynamic, oxidative stress and inflammatory processes that contribute to cardiovascular morbidity. Recent technological advances in automated ECG analysis, artificial intelligence (AI), and wearable monitoring have renewed interest in leveraging cardiac electrical signals for OSAS screening, risk stratification, and treatment monitoring.This Opinion article synthesizes current evidence on ECG applications in OSAS management, examines the physiological mechanisms underlying cardiac electrical abnormalities, and proposes an integrated framework for incorporating ECG assessment into clinical practice.We argue that ECG monitoring represents an underused tool that can enhance cardiovascular risk assessment in OSAS patients and provide objective markers of treatment efficacy.[REVISED R1-#1] The evidence presented in this article was identified through a structured narrative review of the literature. We searched PubMed, MEDLINE, and EMBASE databases for publications up to December 2025 using the following key terms in various combinations:These oscillations persist beyond sleep periods, contributing to sustained cardiovascular stress during wakefulness.[REVISED R1-#2] A hallmark electrophysiological feature of OSAS is the cyclical variation of heart rate (CVHR), a distinctive pattern observable on single-lead ECG recordings. During an obstructive apnea, the initial phase is characterized by bradycardia driven by the activation of pulmonary stretch receptors and enhanced vagal tone -the so-called 'diving reflex' response to hypoxemia and hypercapnia. As the apneic episode progresses, accumulating hypoxemia and hypercapnia progressively stimulate peripheral chemoreceptors (carotid bodies), generating a sympatho-excitatory surge that accelerates heart rate. At the moment of arousal and resumption of ventilation, a secondary tachycardia ensues due to sympathetic activation, followed by a vagally-mediated deceleration as breathing resumes. This cyclical pattern -bradycardia during apnea, tachycardia on arousal, then normalization -produces a characteristic oscillation in R-R intervals detectable on standard Holter or 30-second ECG strips. The amplitude and periodicity of CVHR correlate with AHI severity (r ≈ 0.65-0.75) and can achieve a sensitivity of 74% and specificity of 87% for moderate-to-severe OSAS when analyzed algorithmically from a single-lead ECG (Guilleminault et al., 1983;Sankari et al., 2017). From a prognostic standpoint, the persistence of CVHR despite CPAP therapy identifies patients with residual autonomic impairment and incomplete cardiovascular protection, underscoring its value as a dynamic treatment marker beyond simple AHI normalization.Heart rate variability (HRV) analysis provides a broader non-invasive window into autonomic function. OSAS patients consistently demonstrate reduced daily time-domain parameters (SDNN, RMSSD) and altered frequency-domain indices, with decreased high-frequency (HF) power reflecting diminished parasympathetic tone and increased low-frequency to highfrequency (LF/HF) ratio indicating sympathovagal imbalance (Qin et al., 2021). Recent studies have confirmed that the magnitude of HRV reduction correlates with OSAS severity and predicts cardiovascular outcomes, positioning HRV as both a diagnostic marker and potential prognostic tool (Ucak et al., 2024).[NEW -Reviewer 3] It is increasingly recognized that OSAS is a heterogeneous syndrome with distinct physiological phenotypes that differentially modulate autonomic and ECG responses.Patients with high hypoxic burden -characterized by prolonged and severe oxygen desaturation per apneic event -demonstrate more pronounced QT prolongation, greater HRV impairment, and higher cardiovascular risk than those with equivalent AHI but lower desaturation profiles. REM-predominant OSAS, which is more common in women and in older patients, generates discrete bursts of sympathetic activation during REM sleep that produce distinctive ECG patterns -including clusters of ventricular ectopy and abrupt HRV oscillations -that differ quantitatively from those of NREM-predominant disease. Sex differences further modulate cardiac electrical responses: women with OSAS exhibit attenuated CVHR amplitude and different sympathovagal adaptation patterns compared to men of equivalent disease severity, which may contribute to the lower diagnostic sensitivity of ECG-based screening algorithms in female populations. These phenotypic distinctions have direct implications for ECG-based risk assessment and partially explain inter-individual variability in cardiac electrical findings at equivalent AHI levels, underscoring the need for phenotypeaware diagnostic and monitoring approaches.Upper airway obstruction generates large negative intrathoracic pressure swings, increasing venous return and cardiac transmural pressure. Simultaneously, intermittent hypoxemia activates chemoreceptors, triggering vasoconstriction and blood pressure surges that increase cardiac afterload. These repetitive hemodynamic stresses contribute to left ventricular hypertrophy and atrial remodeling, creating substrates for arrhythmias (Javaheri et al., 2024). Chronic intermittent hypoxemia also promotes low-grade systemic inflammation and oxidative stress. These mediators accelerate atherosclerosis, promote endothelial dysfunction, and facilitate myocardial fibrosis, all of which influence cardiac conduction and repolarization (Amen et al., 2024).[REVISED R1-#3] Bradyarrhythmias represent an important and often underrecognized manifestation of OSAS. Nocturnal sinus bradycardia, sinus pauses exceeding 2.5 seconds, and varying degrees of atrioventricular (AV) block occur in 18-37% of OSAS patients, predominantly during apneic episodes when coordinated vagal surges and hypoxemia-related conduction slowing converge. The severity and frequency of bradyarrhythmic events scale with AHI and nocturnal oxygen desaturation index. In a subset of patients, high-degree AV block may precipitate syncope, representing a directly treatable consequence of unrecognized OSAS. The 2022 American Heart Association/Heart Rhythm Society Scientific Statement on Sleep-Disordered Breathing and Cardiac Arrhythmias explicitly recommends ECG rhythm assessment -ideally via overnight cardiac monitoring -in patients with OSAS presenting with unexplained syncope, documented bradyarrhythmia, or prior to pacemaker implantation, as CPAP therapy alone may reverse conduction abnormalities and obviate device therapy (Mehra et al., 2022).Atrial fibrillation (AF) demonstrates particularly robust associations with OSAS. Recent evidence confirms that OSAS increases AF risk 2-4 fold, with the Sleep Heart Health Study documenting that severe OSAS (AHI >30) confers a 4-fold increased AF risk (Mehra et al., 2022). A recent study found AF odds 2.5 times higher in OSAS patients (Alshomrani et al., 2024). Proposed mechanisms include atrial stretch from increased venous return, autonomic imbalance, and inflammatory-mediated atrial remodeling (Linz et al., 2023). The bidirectional relationship between OSAS and AF is clinically pivotal: patients referred from cardiology for AF evaluation or catheter ablation planning should be systematically screened for OSAS, given AHA/ACC guideline recommendations that OSAS identification and treatment may reduce AF recurrence (Mehra et al., 2022;Ndakotsu et al., 2024). Conversely, new-onset AF detected on Holter monitoring in a sleep clinic patient should prompt formal cardiac rhythm evaluation and cardiology co-management.Ventricular arrhythmias, while less common than atrial arrhythmias, occur with increased frequency in severe OSAS. Complex ventricular ectopy and non-sustained ventricular tachycardia have been reported in 15-25% of patients during sleep studies, with occurrence linked to hypoxemia severity and sympathetic activation.QT interval abnormalities represent particularly concerning ECG findings in OSAS given their association with ventricular arrhythmias and sudden cardiac death. QT prolongation occurs in 23-41% of patients with moderate-to-severe OSAS. A 2021 study demonstrated significant prolongation of Tp-e interval, Tp-e/QT ratio, and Tp-e/QTc ratio across increasing OSAS severity categories, with strong positive correlations between AHI and these repolarization parameters (Karacop and Karacop, 2021). Recent evidence shows that QTc prolongation in OSAS patients is associated with multiple cardiovascular risk factors including older age, female gender, higher BMI, and comorbidities such as diabetes mellitus and hypertension (Shi and Jiang, 2020). A pro-QTc risk score for mortality stratification demonstrated that higher scores independently predicted increased mortality risk across all OSAS severity categories (Memon et al., 2023). QT variability, reflecting beat-to-beat variations in ventricular repolarization, shows significant elevation during apneic episodes and correlates strongly with respiratory disturbance index severity. The mechanisms underlying QT abnormalities likely involve autonomic imbalance, intermittent hypoxemia-induced ion channel dysfunction, electrolyte disturbances, and sympathoadrenal activation with elevated catecholamines. Notably, CPAP therapy has been shown to improve QT parameters, suggesting reversibility of these repolarization abnormalities with effective treatment. T-wave abnormalities and ST-segment depression during sleep occur in 20-35% and 15-25% of OSAS patients, respectively, typically coinciding with apneic episodes and oxygen desaturation.The accessibility and low cost of ECG monitoring make it an attractive screening tool for OSAS, particularly in settings where polysomnography access is limited. Recent developments in automated ECG analysis leverage machine learning and deep learning algorithms to detect OSAS-related patterns in heart rate dynamics, HRV parameters, and respiratory-related cardiac oscillations. A comprehensive meta-analysis evaluated the diagnostic accuracy of machine learning and deep learning algorithms for detecting sleep apnea from single-lead ECG data (Hosseini et al., 2025). The analysis demonstrated that current ECG-based screening algorithms achieve pooled sensitivity and specificity of 75-85% for detecting moderate-tosevere OSAS. Recent deep learning models have achieved even higher performance, with convolutional neural network architectures demonstrating accuracy exceeding 90-97% (Bazoukis et al., 2023;Liu et al., 2024). Multi-modal approaches combining ECG with oxygen saturation signals achieve per-segment detection accuracy of 91.38% and per-recording accuracy of 96.08% (Gao et al., 2025). A recent study from Mayo Clinic developed a deep convolutional neural network model achieving an AUC of 0.80 for OSAS identification, with particularly strong performance in females (AUC: 0.82) compared to males (AUC: 0.73) (Covassin et al., 2025). Multi-night ECG recordings enhance diagnostic yield by capturing night-to-night variability. Patch-based monitors and wearable devices worn for 7-14 days demonstrate excellent correlation with polysomnography while offering improved patient acceptability and real-world representativeness.[NEW -Reviewer 3] These performance metrics, however, warrant critical appraisal before clinical translation. The majority of published AI models were developed and evaluated on retrospective, single-center or consortium datasets, raising substantial concerns about external validity and overfitting. Dataset heterogeneity -encompassing differences in ECG acquisition protocols, patient demographics, OSAS severity distributions, and polysomnographic scoring criteria (notably hypopnea definitions) -complicates direct performance comparisons across studies. Many models have been trained and tested on overlapping or non-independent cohorts, which artificially inflates reported accuracy.Prospective, large-scale external validation in heterogeneous real-world populationsparticularly in women, elderly patients, and those with significant comorbidities or polypharmacy -remains sparse, and published benchmark accuracies are rarely replicated outside their training environments. Until such validation is available, ECG-based AI algorithms should be regarded as promising research tools requiring further prospective validation rather than ready-to-deploy clinical diagnostics.[NEW -Reviewer 2] It is equally important to emphasize that ECG-based assessment is strictly complementary to polysomnography and does not replace it. Current major guidelinesincluding those from the American Academy of Sleep Medicine, the European Respiratory Society, and the American Heart Association -do not recommend ECG analysis as a standalone diagnostic modality for OSAS. ECG monitoring provides adjunctive cardiovascular information and may serve as a low-cost screening trigger in specific high-risk populations (e.g., patients with AF, unexplained syncope, or metabolic syndrome), but confirmed diagnosis and severity assessment of OSAS requires polysomnography or validated home sleep apnea testing. The clinical algorithms proposed in this article are designed to complement, not substitute, established diagnostic pathways.Beyond OSAS diagnosis, ECG findings provide valuable prognostic information regarding cardiovascular risk. Frequent premature atrial contractions identify patients at heightened risk for AF development. QT prolongation and increased QT dispersion associate with ventricular arrhythmias and sudden cardiac death. The pro-QTc risk score demonstrates independent predictive value for mortality in OSAS patients (Memon et al., 2023).[REVISED R1-#4] Nocturnal heart rate and ECG-derived parameters have emerged as powerful independent predictors of cardiovascular morbidity and mortality in OSAS. Two landmark cohort studies have provided robust evidence in this regard. Sankari et al. (2019), analyzing data from the Wisconsin Sleep Cohort Study (n=569, up to 15 years of follow-up), demonstrated that the nocturnal R-R interval dips index (RRDI) -reflecting beat-to-beat heart rate oscillations during sleep -was independently associated with composite cardiovascular events and mortality. Individuals in the highest RRDI category (≥28 dips/hour) had a 7.4-fold increased hazard for CVD incidence and mortality compared to those in the lowest category (HR 7.4, 95% CI 1.97-27.7, p=0.003), independent of AHI, demographics, and comorbidities (Sankari et al., 2019). In a complementary approach, Azarbarzin et al. (2021) quantified the sleep apnea-specific pulse-rate response (ΔHR -the magnitude of heart rate increase following apneas and hypopneas) in the MESA (n=1,395) and SHHS (n=4,575) cohorts. Individuals with a high ΔHR were at significantly increased risk of non-fatal cardiovascular disease (adjusted HR 1.60, 95% CI 1.28-2.00), fatal CVD (adjusted HR 1.68, 95% CI 1.22-2.30), and all-cause mortality (adjusted HR 1.29, 95% CI 1.07-1.55), with the greatest risk amplification in those with concurrent high hypoxic burden. These findings collectively suggest that ECG-derived nocturnal heart rate metrics provide clinically actionable prognostic information beyond conventional AHI-based severity classification.Risk stratification algorithms incorporating ECG parameters alongside OSAS severity and clinical variables outperform traditional cardiovascular risk scores in predicting adverse events. Ambulatory ST-segment monitoring during sleep reveals ischemic episodes in 15-30% of OSAS patients, many of which occur silently without recognized anginal symptoms.Detection of nocturnal ischemia may prompt earlier coronary artery evaluation and revascularization in high-risk patients.Continuous positive airway pressure (CPAP) represents the first-line treatment for moderateto-severe OSAS, and ECG monitoring provides objective markers of therapeutic response.Studies consistently demonstrate significant improvements in most ECG parameters within 3-6 months of effective CPAP therapy. Nocturnal arrhythmia burden decreases by 60-80% in adherent patients, with particular reductions in bradyarrhythmic episodes. AF burden shows substantial reduction with effective CPAP therapy, especially in patients with good adherence (Sánchez-de-la-Torre et al., 2023). QT parameters also improve with CPAP therapy, with studies demonstrating normalization of QTc intervals and reduction in QT dispersion.[REVISED R1-#5] Importantly, some ECG improvements may occur acutely with CPAP application. Optimal CPAP pressure significantly normalized R-R interval dip indices and reduced heart rate changes associated with non-apneic respiratory events, with RRI returning to baseline levels once resistive respiratory load was abolished (Sankari et al., 2017). This acute normalization reflects the immediate restoration of sympathovagal balance and elimination of hypoxemia-related autonomic excitation, even before structural cardiac remodeling can occur. This acute response has a practical clinical implication: the absence of short-term ECG improvement despite apparent CPAP adherence may therapy or parameters consistently improve with CPAP therapy, with and demonstrating significant increases within of treatment improvements occur in patients with higher CPAP suggesting a relationship between treatment and autonomic The magnitude of HRV improvement correlates with cardiovascular positioning HRV monitoring as a potential tool for treatment and adherence assessment (Qin et al., and upper airway also demonstrate ECG typically of magnitude compared to CPAP therapy. A of the for by et al. demonstrated that therapy in patients with moderate-to-severe OSAS significant improvements in and low-frequency HRV power across all sleep at with the of HRV improvement with AHI reduction achieved et al., 2019). Notably, a of the therapy not reverse the HRV suggesting autonomic These the that upper airway of the modality the of autonomic cardiac particular as a treatment with direct cardiovascular electrophysiological independently and promotes atrial OSAS these reduction of in OSAS patients significantly AHI and ECG improvements including normalization of reduction in premature atrial and restoration of HRV parameters The cardiovascular ECG of to through direct improvement in metabolic and inflammatory milieu myocardial and from reduced upper airway the and most has been associated with the most substantial ECG including reductions in AF burden and ventricular ectopy to those with ECG with the of further the of as a cardiovascular risk in OSAS increasingly the value of ECG monitoring in OSAS The 2021 American Heart Association Scientific Statement recommends cardiac rhythm assessment in patients with moderate-to-severe OSAS or significant cardiovascular comorbidities et al., 2021). requires for ECG analysis, and in the of We a clinically to ECG in OSAS that reflects real-world patient In ECG assessment in OSAS typically in clinical the sleep where patients are referred for OSAS, and the cardiology where patients with established AF or unexplained bradycardia referred for sleep assessment prior to cardiac In the the initial evaluation at the clinic on cardiovascular risk assessment prior arrhythmia of During the diagnostic sleep a single-lead ECG channel is during polysomnography cardiac rhythm data the In home sleep apnea rate as a its is to reveals significant arrhythmias bradyarrhythmia, ventricular or concerning QT changes formal ECG Holter is as a high-risk patients with syncope, or elevated sudden cardiac ECG monitoring with provides In the clinical -the patient referred from cardiology recommend that OSAS be identified and prior to AF or therapy as OSAS treatment may or significantly reduce arrhythmia burden (Mehra et al., et al., intelligence and deep learning applications represent the of ECG-based OSAS The 2025 meta-analysis confirmed that machine learning algorithms analyzing ECG signals achieve diagnostic accuracy of with the most deep learning models exceeding 90-97% (Hosseini et al., et al., 2023). devices a particularly promising for the between and and monitors worn for 7-14 days night-to-night variability in cardiac rhythm and a more of OSAS cardiovascular impact than a In patients CPAP therapy, wearable ECG can HRV and arrhythmia burden to objective The of ECG assessment into OSAS represents a in of breathing as a with cardiovascular ECG monitoring offers low of and to provide which a of a ECG-based approaches can night-to-night variability and treatment response important be The greatest clinical value of ECG in OSAS may not in diagnosis per but in cardiovascular risk stratification and treatment OSAS patients with high arrhythmia prolonged QT increased QT variability, or nocturnal ischemia that may adverse cardiovascular events. The pro-QTc risk score demonstrates ECG parameters can be integrated into clinical tools for mortality risk stratification (Memon et al., 2023). ECG improvements with CPAP therapy provides objective evidence of treatment beyond of artificial intelligence and wearable to access to cardiac monitoring and OSAS learning approaches achieve clinically diagnostic accuracy (Hosseini et al., with the most models or exceeding