Objective To systematically review the effect of percutaneous acupoint electrical stimulation (TEAS) on heart rate variability (HRV). Methods The PubMed, Embase, Ovid MEDLINE, Cochrane Library, CNKI, WanFang Data, VIP, and CBM databases were electronically searched to collect randomized controlled trials (RCTs) on the effects of percutaneous acupoint electrical stimulation on heart rate variability from inception to February 28, 2023. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed using RevMan 5.4 software. Results A total of 14 RCTs involving 719 patients were included. The results of meta-analysis showed that SDNN (MD=12.95, 95%CI 9.18 to 16.72, P<0.01), RMSSD (MD=1.81, 95%CI 0.10 to 3.53, P=0.04), pNN50 (MD=1.75, 95%CI 1.02 to 2.48, P<0.01), HF (SMD=0.27, 95%CI 0.01 to 0.52, P=0.04), LF/HF (MD=−0.07, 95%CI −0.12 to −0.03, P<0.01), ln-LF (MD=0.63, 95%CI 0.25 to 1.01, P<0.01), ln-HF (MD=1.05, 95%CI 0.60 to 1.49, P<0.01), mean RR (MD=−11.86, 95%CI −21.77 to −1.96, P=0.02), and HR (SMD=−0.43, 95%CI −0.66 to −0.20, P<0.01) all showed improvement compared with the control group. However, there were no significant differences between the two groups in LF (SMD=0.15, 95%CI −0.10 to 0.40, P=0.23), LF norm (SMD=0.24, 95%CI −0.10 to 0.58, P=0.16) or HF norm (SMD=0.25, 95%CI −0.47 to 0.97, P=0.5). TEAS on PC6: SDNN, pNN50, HF, LF/HF, LF norm, HF norm, ln-LF, ln-HF, and HR all showed improvement compared with the control group. However, there were no significant differences between the two groups in RMSSD, LF, or RR interval. Conclusion This study supports the improvement of heart rate variability by transcutaneous acupoint electrical stimulation and PC6 acupoint selection. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.
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.