Heart rate detrended fluctuation indexes as estimate of obstructive sleep apnea severity

Eduardo Luiz Pereira da Silva, Rafael Pereira, Luciano Neves Reis, Valter Luis Pereira Jr, Luciana Aparecida Campos, Niels Wessel, Ovidiu Constantin Baltatu, Eduardo Luiz Pereira da Silva, Rafael Pereira, Luciano Neves Reis, Valter Luis Pereira Jr, Luciana Aparecida Campos, Niels Wessel, Ovidiu Constantin Baltatu

Abstract

In the present study, we aimed at investigating a heart rate variability (HRV) biomarker that could be associated with the severity of the apnea-hypopnea index (AHI), which could be used for an early diagnosis of obstructive sleep apnea (OSA). This was a cross-sectional observational study on 47 patients (age 36 ± 9.2 standard deviation) diagnosed with mild (23.4%), moderate (34%), or severe (42.6%) OSA. HRV was studied by linear measures of fast Fourier transform, nonlinear Poincaré analysis, and detrended fluctuation analysis (DFA)—DFA α1 characterizes short-term fluctuations, DFA α2 characterizes long-term fluctuations. Associations between polysomnography indexes (AHI, arousal index [AI], and oxygen desaturation index [ODI]) and HRV indexes were studied. Patients with different grades of AHI had similar sympathovagal balance levels as indicated by the frequency-domain and Poincaré HRV indexes. The DFA α2 index was significantly positive correlated with AHI, AI, and ODI (Pearson r: 0.55, 0.59, and 0.59, respectively, with P < 0.0001). The ROC analysis revealed that DFA α2 index predicted moderate and severe OSA with a sensitivity/specificity/area under the curve of 0.86/0.64/0.8 (P = 0.005) and 0.6/0.89/0.76 (P = 0.003), respectively. Our data indicate that the DFA α2 index may be used as a reliable index for the detection of OSA severity.

Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures

FIGURE 1
FIGURE 1
(A) AHI, (B) AI, and (C) ODI in obstructive sleep apnea across different degrees of severity according to the AHI: mild (AHI  30); #, P < 0.001. (D) Correlations between AHI and the other polysomnography indexes, AI, and ODI. Values are Pearson r and 95% CI. AHI = apnea–hypopnea index, AI = arousal index, CI = confidence interval, ODI = oxygen desaturation index.
FIGURE 2
FIGURE 2
Basal sympathovagal balance levels indicated by (A) and (B) the frequency-domain HRV indexes, and the (C) and (D) time-domain HRV indexes in obstructive sleep apnea across different degrees of severity according to the AHI: mild (AHI  30). AHI = apnea–hypopnea index, HF = high frequency, HRV = heart rate variability, LF = low frequency, SD = standard deviation.
FIGURE 3
FIGURE 3
HRV DFA indexes in different degrees of obstructive sleep apnea and their correlations with polysomnography indexes. (A) and (B) DFA α1 index has no correlation with the AHI and the other polysomnography indexes. (C) and (D) DFA α2 index is significant positive correlated with the AHI and the other polysomnography indexes. AHI = apnea–hypopnea index, a.u. = arbitrary units, DFA = detrended fluctuation analysis, HRV = heart rate variability.
FIGURE 4
FIGURE 4
Diagnostic yield for DFA α2 index cutoff values compared with AHI cutoff values (reference values) obtained by polysomnography. ROC curve for identifying patients with (A) moderate (AHI threshold = 15) or (B) severe obstructive sleep apnea (AHI threshold = 30). AHI = apnea–hypopnea index, AUC = area under the curve, CI = confidence interval, DFA = detrended fluctuation analysis, ROC = receiver–operator characteristic.

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