Sleep condition detection and assessment with optical fiber interferometer based on machine learning

Wang, Qing; Lyu, Weimin; Zhou, Jing; Chen, Yu

doi:10.1016/j.isci.2023.107244

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…The electrical signal produced by the PD is partitioned into two channels, CH1 and CH2. CH1 encompasses the unprocessed data, whereas CH2 incorporates the data that have undergone processing via a low-pass filter (LPF) [15]. The LPF serves to eliminate high-frequency noise from the signal, thereby improving the accuracy of the data.…”

Section: Design Of Optical Fiber Monitoringmentioning

confidence: 99%

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DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing

Zhang,

Wang,

Lyu

et al. 2024

Sensors

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Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach–Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

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Section: Design Of Optical Fiber Monitoringmentioning

confidence: 99%

“…Initially, an adaptive heart rate calculation approach based on Bayesian probability is used to collect ECG data from the heartbeat interval sequence [15]. Each sequence of fiveminute heartbeat intervals is termed a sample.…”

Section: Data Resources and Processingmentioning

confidence: 99%