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.
Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%–104.28)% vs. 58.48% (45.34%–65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.