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[Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi

August 2024

Key Laboratory of Biomedical Effect of Physical Field and Instrument, School of Electrical and Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, P. R. China.

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.

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Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF.

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Objective: Photoelectric volumetric tracing (PPG) exhibits high sensitivity and specificity in flap monitoring. Deep learning (DL) is capable of automatically and robustly extracting features from raw data. In this study, we propose combining PPG with 1D convolutional neural networks (1D-CNN) to preliminarily explore the method's ability to distinguish the degree of embolism and to localize the embolic site in skin flap arteries.

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Monitoring the force of fingertip manipulation without disturbing the natural sense of touch is crucial for digitizing the skills of experienced craftsmen. However, conventional force sensors need to be put between the skin and the objects, which affects the natural sense of the skin. Here, we proposed a fingertip force sensing method based on changes of blood volume and designed a wearable photoelectric fingertip force sensing system (PFFS) for digitalization of traditional Chinese medicine (TCM) pulse diagnosis.

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Photo-plethysmography (PPG) making use of a monochromic light source and a photoelectric sensor is a non-invasive detection method to record blood volume changes in vessels and can be used to extract cardiac-related heart pulse information. Here, we demonstrated the fabrication of a monolithic photonic chip by integrating light-emitting diodes (LEDs) with photodetectors (PDs) on the same GaN-on-sapphire wafer containing InGaN/GaN multi-quantum wells (MQWs). The MQWs simultaneously act as light emitters in the LEDs and detectors in the PDs, and their mechanism has been studied.

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