Validation of denoising system using non-harmonic analysis and denoising convolutional neural network for removal of Gaussian noise from gravitational waves observed by LIGO

Kato, Takashi; Hasegawa, M.

doi:10.1016/j.ascom.2022.100607

Cited by 1 publication

(2 citation statements)

References 52 publications

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“…Machine learning was implemented in MATLAB. In contrast to previous studies where machine learning was employed to discern noise from gravity wave signals [36], the proposed method focuses on extracting the heartbeat component through training specifically on heartbeat signals. However, transfer learning was performed on a general network instead of utilizing specially tuned networks.…”

Section: Networkmentioning

confidence: 99%

“…To overcome these problems, the proposed approach utilizes non-harmonic analysis (NHA) [27], wherein the frequency resolution is independent of the data length. This method has proven effective in various applications, including optical coherence tomography [28], [29], image processing [30]- [32], gravitational wave detection [33]- [36], and steady-state visually evoked potential analysis [37]. In the context of heartbeat component detection using NHA, Konishi et al recorded the heartbeat of a stationary subject by placing a Doppler sensor in the driver's seat and confirmed the potential for heartbeat detection.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis

Goto,

Horimoto,

Koyama

et al. 2024

IEEE Access

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The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an increased focus on the routine analysis of vital signs such as breathing and pulse rates. Radar technology has proven effective for non-contact, long-term monitoring of these vital signs, with frequency analysis being the default method for processing signals from Doppler radar owing to their inherent noise. However, conventional analysis approaches often struggle to detect weak signals buried within the sidelobes of other signals. Some data analysis techniques for Doppler radar rely on machine learning, but they struggle to generate clear time-frequency diagrams, complicating heartbeat detection. In this study, we employed non-harmonic analysis (NHA) as a frequency analysis method to mitigate sidelobe interference and implemented semantic segmentation for precise heartbeat detection. To validate the proposed approach, we conducted heartbeat detection tests both in stationary, low-noise conditions and in a noisy driving simulation environment. The results indicated that the NHA method successfully analyzed heartbeat harmonics, suggesting its potential for detecting heartbeat components through machine learning. To validate these findings, we determined the detection accuracy by comparing true and false positive rates, allowing us to quantify the detectability of heartbeats under both resting and driving simulation conditions.INDEX TERMS Continuous-wave Doppler radar (CW Doppler radar), driving simulation, harmonic, heartbeat, non-harmonic analysis (NHA).

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Section: Networkmentioning

confidence: 99%