Heart rate variability time and frequency indices are widely used in functional assessment for autonomic nervous system (ANS). However, this method merely analyzes the effect of cardiac dynamics, overlooking the effect of cardio-pulmonary interplays. Given this, the present study proposes a novel cardiopulmonary coupling (CPC) algorithm based on cross-wavelet transform to quantify cardio-pulmonary interactions, and establish an assessment system for ANS aging effects using wearable electrocardiogram (ECG) and respiratory monitoring devices. To validate the superiority of the proposed method under nonstationary and low signal-to-noise ratio conditions, simulations were first conducted to demonstrate the performance strength of the proposed method to the traditional one. Next, the proposed CPC algorithm was applied to analyze cardiac and respiratory data from both elderly and young populations, revealing that young populations exhibited significantly stronger couplings in the high-frequency band compared with their elderly counterparts. Finally, a CPC assessment system was constructed by integrating wearable devices, and additional recordings from both elderly and young populations were collected by using the system, completing the validation and application of the aging effect assessment algorithm and the wearable system. In conclusion, this study may offers methodological and system support for assessing the aging effects on the ANS.
Humans ; Autonomic Nervous System/physiology* ; Algorithms ; Aging/physiology* ; Electrocardiography/methods* ; Heart Rate/physiology* ; Wavelet Analysis ; Aged ; Signal Processing, Computer-Assisted ; Wearable Electronic Devices

Objective:To investigate and analyze the predictive value of inhibin A (INHA), galectin-13 (Gal-13), leucine rich alpha-2-glycoprotein 1 (LRG1) in serum for adverse pregnancy outcomes in pregnant women with gestational diabetes mellitus (GDM) .Methods:From Jan. 2022 to Dec. 2023, 87 GDM pregnant women admitted to Obstetrics Department of Shanxi Children’s Hospital were included as the study group, and were assigned into a good outcome group ( n=54) and an adverse outcome group ( n=33) based on pregnancy outcomes. Meantime, another 87 healthy pregnant women who underwent normal prenatal examinations at our hospital and had no complications were selected as the control group. ELISA method was applied to detect serum levels of INHA, LRG1, and Gal-13. Multiple factor Logistic regression model was constructed to analyze the factors affecting adverse pregnancy outcomes in GDM pregnant women. Receiver operating characteristic (ROC) curves were applied to evaluate the efficacy of the three methods in predicting adverse pregnancy outcomes in GDM pregnant women. Results:Compared with the control group, the levels of INHA and LRG1 in the serum of pregnant women in the study group were obviously higher, and the level of Gal-13 in the serum was obviously lower ( P<0.05). Compared with the good outcome group, the adverse outcome group showed an increase in serum INHA and LRG1 levels and a decrease in serum Gal-13 level ( P<0.05). Elevated levels of serum INHA and LRG1 were risk factors for adverse pregnancy outcomes in GDM pregnant women, while elevated level of serum Gal-13 was a protective factor ( P<0.05). The AUC values for predicting adverse pregnancy outcomes in GDM pregnant women based solely on serum INHA, Gal-13, and LRG1 levels were 0.859, 0.850, and 0.841, respectively. The AUC predicted by the combination of the three factors was 0.978, which was better than the individual predictions of serum INHA, Gal-13, and LRG1 ( Zcombination-HA=2.378, Z combination-Gal-13=3.193, Zcombination-LRG1=3.050, P=0.017, 0.001, 0.002) . Conclusions:Serum levels of INHA and LRG1 are elevated in GDM pregnant women, while serum level of Gal-13 is decreased. All three are potential factors that affect the pregnancy outcomes of GDM pregnant women, and the combination of the three shows higher efficacy in predicting adverse pregnancy outcomes in GDM pregnant women.

Objective:To investigate and analyze the predictive value of inhibin A (INHA), galectin-13 (Gal-13), leucine rich alpha-2-glycoprotein 1 (LRG1) in serum for adverse pregnancy outcomes in pregnant women with gestational diabetes mellitus (GDM) .Methods:From Jan. 2022 to Dec. 2023, 87 GDM pregnant women admitted to Obstetrics Department of Shanxi Children’s Hospital were included as the study group, and were assigned into a good outcome group ( n=54) and an adverse outcome group ( n=33) based on pregnancy outcomes. Meantime, another 87 healthy pregnant women who underwent normal prenatal examinations at our hospital and had no complications were selected as the control group. ELISA method was applied to detect serum levels of INHA, LRG1, and Gal-13. Multiple factor Logistic regression model was constructed to analyze the factors affecting adverse pregnancy outcomes in GDM pregnant women. Receiver operating characteristic (ROC) curves were applied to evaluate the efficacy of the three methods in predicting adverse pregnancy outcomes in GDM pregnant women. Results:Compared with the control group, the levels of INHA and LRG1 in the serum of pregnant women in the study group were obviously higher, and the level of Gal-13 in the serum was obviously lower ( P<0.05). Compared with the good outcome group, the adverse outcome group showed an increase in serum INHA and LRG1 levels and a decrease in serum Gal-13 level ( P<0.05). Elevated levels of serum INHA and LRG1 were risk factors for adverse pregnancy outcomes in GDM pregnant women, while elevated level of serum Gal-13 was a protective factor ( P<0.05). The AUC values for predicting adverse pregnancy outcomes in GDM pregnant women based solely on serum INHA, Gal-13, and LRG1 levels were 0.859, 0.850, and 0.841, respectively. The AUC predicted by the combination of the three factors was 0.978, which was better than the individual predictions of serum INHA, Gal-13, and LRG1 ( Zcombination-HA=2.378, Z combination-Gal-13=3.193, Zcombination-LRG1=3.050, P=0.017, 0.001, 0.002) . Conclusions:Serum levels of INHA and LRG1 are elevated in GDM pregnant women, while serum level of Gal-13 is decreased. All three are potential factors that affect the pregnancy outcomes of GDM pregnant women, and the combination of the three shows higher efficacy in predicting adverse pregnancy outcomes in GDM pregnant women.