A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG

Abstract

Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1

Identification of erroneously detected and abnormal QRS complexes. (a) QRS complexes centered around the R-wave. (b) QRS variance and the upper (Qu) and lower (Ql) acceptance limits indicated by the dashed lines. Complexes with variance outside these limits were removed from the analysis (c). a.u. stands for arbitrary units.

Figure 2

Examples of respiratory segments with different spectral characteristics during deep sleep (a), apnea (b), and driving (c). The PSD of each segment is displayed at the bottom, and the shaded area indicate the bandwidth b(k). The number of modes m(k) is also indicated. a.u. stands for arbitrary units. The distribution of b(k) (top) and m(k) (bottom) for all datasets are indicated in (d).

Figure 3

Examples of the reference respiratory and EDR signals computed from two high-quality (q(k) = 100) segments of the Sleep dataset. Only the EDR signals with the best and worst wave morphology approximations, for these examples, are shown. (a) Segment during deep sleep with b(k) = 0.08 Hz and m(k) = 1. Correlation and coherence were highest for r^k(n), i.e., ∣ρ∣ = 0.88 and γr¯=0.98, and worst for r^a(n), i.e., ∣ρ∣ = 0.60 and γr¯=0.82. (b) Segment with an OSA event with b(k) = 0.57 Hz and m(k) = 5. Correlation and coherence were highest for r^k(n), i.e., ∣ρ∣ = 0.66 and γr¯=0.79, and worst for r^a(n), i.e., ∣ρ∣ = 0.47 and γr¯=0.45. The signal r^k(n) was inverted to facilitate visualization.

Figure 4

Errors in the estimation of the respiratory rate for high- and low-quality ECG segments.

Figure 5

Respiratory rate f(k) and cardiorespiratory parameters γxy¯(k) and Tx→y(k), estimated using rth(n) and r^sr(n) for normal activity and different respiratory events. Significant differences with respect to normal activity are indicated by *.

Figure 6

Difference between the reference and estimated respiratory rates, namely fth(k)−f^edr(k), where f^edr(k) was computed from r^rs(n), r^dw(n), r^sr(n), and r^cm(n). Differences are given in Hz. Each row corresponds, from top to bottom, to the Drivers, Fantasia, and Sleep datasets. For each case, the least-squares regression line is indicated by a solid line.

Figure 7

Similarity measures for all datasets and all EDR signals with respect to rth(n). The indices n were removed to facilitate visualization, i.e., rth(n) = rth. The similarity between rth(n) and both rab(n) and rna(n) are indicated in the shaded boxes. *Indicate that ∣ρ∣ and γr¯ are significantly different for all EDR signals.