Study Design and Patients
This prospective study included normal subjects and patients with a clinical diagnosis COPD, based on the Global Initiative for Obstructive Lung Disease Criteria (GOLD). The study was conducted at Chang Gung Memorial Hospital during January and December in 2019. Patients with heart failure (ejection fraction <40%), known malignancy, or atrial fibrillation as well as those using anti-arrhythmic agents for arrhythmia or oxygen were excluded. All COPD patients underwent cardiac echo analysis, biochemical analysis (eosinophils, high sensitivity C-reactive protein (HS-CRP), and IgE), pulmonary function tests, a six-minute walking test, and a coupling test during the first visit. Clinical profiles, a list of inhalation medicines, and acute exacerbation history [21] were also recorded (Figure 1). A total of 55 patients with COPD (69 (51-84) years old, 54 male) and ten normal subjects (64.5 (50-71) years old, 5 male) were enrolled. All participants signed informed consent prior to enrollment. The study was approved by the Ethics Committee of Chang Gung Memorial Hospital, Linkou, Taiwan (201702150B0).
Phase synchronization analysis
Instrumentation
Experiments were performed in a quiet room with the temperature maintained at 22-24 °C. Participants were instructed to avoid bronchodilators, such as beta-2 agonists, xanthene derivatives, alcohol, and coffee prior to the test. The chest skin was abraded using gel and then cleaned using alcohol to reduce electrode impedance prior to the attachment of electrocardiogram (ECG) electrodes. Prior to the examination, recordings were obtained of blood pressure, heart rate, and oxygen saturation. The subjects wore a pulse oximeter on the index finger and ECG electrodes on the chest wall. To avoid signal quality issues, the patient practiced breathing through the flow tube for 1 min before recording. ECG signals and flow signals were then recorded continuously for 5 min using three Actiwave devices (CamNtech Ltd, Cambridge, UK). The recorded signals were transferred in European Data Format to LabChart 8 software (ADInstruments, Dunedin, New Zealand), and then exported to text files for analysis.
Signal Processing and Synchrogram index
R peaks were detected using a standard R peak detection algorithm, and the time differences between consecutive R peaks were converted into an instantaneous heart rate (IHR) time series using a standard interpolation algorithm. [22] Instantaneous phases of the respiratory signals and IHR time series were extracted using the synchrosqueezing transform (SST). [23] The time-varying broad pass issue associated with the Hilbert transform was avoided using the generalized SST form. After obtaining the instantaneous phases of the IHR (denoted as ΦH) and the respiratory signal (denoted as ΦR), the synchrogram was used to quantify phase synchronization. [17, 24] Note that a synchrogram is a graphical tool used to depict phase coupling between two oscillatory signals. In the current study, we first obtained the timestamps tk at the point where the IHR phase attained 0 (i.e., ΦH(tk) mod 2π = 0). We then measured the respiratory phase at tk as follows: Ψ(tk) = 1/2π[ΦR(tk) mod 2π]. Plotting Ψ(tk) against tk made it possible to obtain a horizontal stripe in cases where the IHR and respiratory signals were synchronized; otherwise, we obtained scattered points. Cardiorespiratory synchronization was quantified by measuring the synchrogram index λ [24] as follows: where M is the number of detected cycles in the IHR. The synchronization index was closer to 1 in synchronized systems and closer to 0 in unsynchronized systems.
Statistical Analysis
All results are presented as median (range) or mean ± standard deviation. The nonparametric exact two-tailed Mann-Whitney U test was used to determine the statistical significance between two groups of continuous variables, and Fisher’s exact tests were used for categorical variables. Variance differences were evaluated using the F-test. Pearson’s correlation was used to examine the association between distance, distance saturation product, and synchrogram index. Forward stepwise multiple regression analysis of the distance and distance saturation product was used to evaluate the contribution of age, body mass index, and synchrogram index. All reported P values were two-sided, with P < 0.05 considered statically significant. All data were analyzed using R version 3.5.2 (R foundation for statistical computing).