Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals
Open accessAbstract
Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients’ health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.
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In a study developing an AI model for heart failure, the algorithm's accuracy varied from 82.51% to 99.22% depending on the prevalence of heart failure in the study population.
“In a study that developed an AI model for heart failure, the algorithm was tested on several populations with a varying prevalence of heart failure, confirming a very large difference in the accuracy of the algorithm from 82.51% to 99.22%, depending on the prevalence of heart failure in the study population.108”
From Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: A systematic review and meta-analysis (2022) · cites this paper as [108]
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- Authors
- Wang, L., & Zhou, X.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Corresponding author
- Wang, Ludi
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-03-28
- Volume
- 19
- Issue
- 7
- Pages
- 1502
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6480269
- OpenAlex
- W2924256559
- Cited by
- 85
- References
- 37
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 13 · 2023: 17 · 2022: 16 · 2021: 12 · 2020: 9 · 2019: 4
- FWCI
- 7.38
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- BUPT Excellent Ph.D. Students Foundation (00)
- National Natural Science Foundation of China (6170204)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- heart rate variability, RR interval, long short-term memory, Inception module, wearable health monitoring, smartphone sensing
- MeSH terms
- Deep Learning, Adult, Aged, Electrocardiography, Female, Heart Failure, Heart Rate, Humans, Male, Middle Aged, Databases, Factual, Neural Networks, Computer, Young Adult
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