Exploratory study on quantitative analysis of nocturnal breathing patterns in patients with acute heart failure based on wearable devices.
10.7507/1001-5515.202310015
- Author:
Mengwei LI
1
;
Yu KANG
2
;
Yuqing KOU
1
;
Shuanglin ZHAO
3
;
Xiu ZHANG
4
;
Lirui QIU
5
;
Wei YAN
6
;
Pengming YU
4
;
Qing ZHANG
2
;
Zhengbo ZHANG
7
Author Information
1. Medical School of Chinese PLA, Beijing 100853, P. R. China.
2. Department of Cardiology, West China Hospital of Sichuan University, Chengdu 610041, P. R. China.
3. Department of Medical Engineering, the 72nd Group Army Hospital of CPLA, Huzhou, Zhejiang 313000, P. R. China.
4. Rehabilitation Medical Center, West China Hospital of Sichuan University, Chengdu 610041, P. R. China.
5. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China.
6. Department of Hyperbaric Oxygen, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, P. R. China.
7. Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing 100853, P. R. China.
- Publication Type:Journal Article
- Keywords:
Acute heart failure;
Breathing pattern quantification;
Wearable devices
- MeSH:
Humans;
Heart Failure/diagnosis*;
Prognosis;
Respiration;
Wearable Electronic Devices;
Acute Disease
- From:
Journal of Biomedical Engineering
2023;40(6):1108-1116
- CountryChina
- Language:Chinese
-
Abstract:
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.
