ArticleBiosensors2025
A Novel Adaptive Independent Component Analysis Method for Multi-Channel Optically Pumped Magnetometers' Magnetocardiography Signals.
Article in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Physiological Monitoring Applications of Wearable Multimodal Fusion Systems Based on ECG and PPG: A Comprehensive Review.Sensors (Basel, Switzerland) · 2026Review
- Progress in Clinical Magnetocardiography: The Contactless Breakthrough for Noninvasive Clinical Detection of Cardiac Ischemia Now Needs Worldwide Standardization.Sensors (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
With the gradual maturation of optically pumped magnetometer (OPM) technology, the use of OPMs to acquire weak magnetocardiography (MCG) signals has started to gain widespread application. Due to the complexity of magnetic environments, MCG signals are often subject to interference from various unknown sources. Independent component analysis (ICA) is one of the most widely used methods for blind source separation. However, in practical applications, the numbers of retained components and filtering components are often selected manually, relying on subjective experience. This study proposes an adaptive ICA method that estimates the signal-to-noise ratio (SNR) before processing to determine the number of components and selects heartbeat-related components based on their characteristic indicators. The method was validated using phantom experiments and MCG data in a 128-channel OPM-MCG system. In the human subject experiment, the array output SNR reached 31.8 dB, and the processing time was significantly reduced to 1/38 of the original. The proposed method outperformed traditional techniques in terms of its ability to identify artifacts and efficiency in this regard, providing strong support for the broader clinical application of OPM-MCG.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.