ArticleIEEE transactions on bio-medical engineering2023
KUBAI: Sensor Fusion for Non-Invasive Fetal Heart Rate Tracking.
Article in IEEE transactions on bio-medical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- DHF: A Generative AI Approach to Data-Efficient Signal Separation for Wearable Embedded Systems.ACM transactions on embedded computing systems : TECS · 2026Article
- FOSTER: A Comprehensive Pipeline for Transabdominal Fetal Pulse Oximetry Validated in a Large Animal Model of Pregnancy.IEEE transactions on bio-medical engineering · 2026Article
- HDFusion: Hierarchical Data Fusion for Robust Fetal Heart Rate Estimation using Transabdominal PPG Signals.ACM transactions on computing for healthcare · 2026Article
- Article
- JOIN: Optimized Light Source Activation for Deep Tissue Optical Sensing.... ACM/IEEE International Conference on Cyber-Physical Systems. IEEE/ACM International Conference on Cyber-Physical Systems · 2025Article
- Transcutaneous Discrimination of Fetal Heart Rate from Maternal Heart Rate: A Fetal Oximetry Proof-of-Concept.Reproductive sciences (Thousand Oaks, Calif.) · 2024Article
- Deep Harmonic Finesse: Signal Separation in Wearable Systems with Limited Data.Proceedings. Design Automation Conference · 2024Article
- BASS: Safe Deep Tissue Optical Sensing for Wearable Embedded Systems.ACM transactions on embedded computing systems : TECS · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveFetal heart rate (FHR) is critical for perinatal fetal monitoring. However, motions, contractions and other dynamics may substantially degrade the quality of acquired signals, hindering robust tracking of FHR. We aim to demonstrate how use of multiple sensors can help overcome these challenges.
methodsWe develop KUBAI
resultsThe accuracy of the proposed method is evaluated against invasive ground-truth measurements. We obtained below 6 beats-per-minute (BPM) root-mean-square error (RMSE) with KUBAI, on five different datasets. KUBAI's performance is also compared against a single-sensor version of the algorithm to demonstrate the robustness due to sensor fusion. KUBAI's multi-sensor estimates are found to give overall 23.5% to 84% lower RMSE than single-sensor FHR estimates. The mean ± SD of improvement in RMSE is 11.95 ±9.62 BPM across five experiments. Furthermore, KUBAI is shown to have 84% lower RMSE and ∼ 3 times higher R
conclusionThe results support the effectiveness of KUBAI, the proposed sensor fusion algorithm, to non-invasively and accurately estimate fetal heart rate with varying levels of noise in the measurements. SIGNIFICANCE: The presented method can benefit other multi-sensor measurement setups, which may be challenged by low measurement frequency, low signal-to-noise ratio, or intermittent loss of measured signal.
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