Movement Noise Cancellation in Second Derivative of Photoplethysmography Signals with Wavelet Transform and Diversity Combining

Ban, Dahee; Shahid, Syed Maaz; Kwon, Sungoh

doi:10.3390/app8091531

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Wavelet analysis methods are powerful method for analyzing variability modes within a time series [27,28]. It has been widely applied in analyzing hydro-meteorological factors variations at multi-time scales [29].…”

Section: Wavelet Analysis Methodsmentioning

confidence: 99%

Multiscale Hydro-Meteorological Factors Variations and Its Links to Large-Scale Atmospheric Circulation Systems in the Source Region of Yangtze River

Tang¹

2018

Preprint

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Studying hydro-meteorological factors variations and its links to large-scale atmospheric circulation systems can facilitate the understanding of the hydrological processes and sustainable water resources management in the source region of the Yangtze River (SRYR). Currently, researches mostly focused on the temporal and spatial variation characteristics in hydro-meteorological factors; however, researches on the hydro-meteorological variations and its links to large-scale atmospheric circulation systems in the SRYR are scarce. Based on long-term hydro-meteorological and reanalysis data, this research investigated multiscale variations of hydro-meteorological factors and its links to large-scale atmospheric circulation characteristic indices during 1957~2012 in the SRYR. The results showed that the amounts of streamflows and precipitation in the SRYR declined during the 1990s. Since the 2000s, the amounts of streamflows and precipitation had increased significantly climate in the SRYR. The change trends of precipitation and streamflows in the SRYR are synergetic at annual and seasonal scales, and have three significant periods, namely 3~5 years, 15–20 years and 30–40 years. The South Asia monsoon (SAM) plays a relatively more important role in the hydro-meteorological factors changes in the SRYR. The relative contributions of SAM to streamflows and precipitation changes were 83.6% and 78%, respectively. During the driest (wettest) year, the SAM is relatively weak (strong), and brings less (more) southwest airflow into the SRYR, less (more) precipitation and streamflows will be generated in the SRYR.

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Section: Wavelet Analysis Methodsmentioning

confidence: 99%

Multiscale Hydro-Meteorological Factors Variations and Its Links to Large-Scale Atmospheric Circulation Systems in the Source Region of Yangtze River

Tang¹

2018

Preprint

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“…In the paper, 'Movement Noise Cancellation in Second Derivative of Photoplethysmography Signals with Wavelet Transform and Diversity Combining' [5] authors proposed an algorithm to remove movement noise from second derivative of photoplethysmography (SDPPG) signals. Experiment results show that the proposed algorithm outperforms the previous filter-based algorithm, and that movement noise with 30% time duration can be reduced by up to 70.89%.…”

Section: Contributionsmentioning

confidence: 99%

Wearable Wireless Devices

2019

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“…The aforementioned issues typically arise with pressure sensors affixed to the wristband and are often disregarded. Noise reduction techniques such as wavelet transform [28], independent components analysis [29], and empirical modal decomposition [30] have been widely used in pulse signal processing, but they have no apparent advantage or significance for pulse signals with considerable noise pollution. Therefore, this paper focuses on recognizing the effective single-cycle pulse wave from the signals acquired by the sensors.…”

Section: Introductionmentioning

confidence: 99%

Single-Cycle Pulse Signal Recognition Based on One-Dimensional Deep Convolutional Neural Network

Chen,

Geng,

Yao

et al. 2024

Electronics

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Pulse signals carry comprehensive information regarding human cardiovascular physiology and pathology, providing a noninvasive and continuous method to assess cardiovascular health status in blood pressure monitoring. The blood pressure measurement method based on the pulse signal needs to extract the features of the single-cycle pulse signal, while the pulse signal pertains to the weak physiological signal of body surface. The acquisition process is susceptible to various factors leading to abnormal cycles, especially adjacent channel interference, affecting the subsequent feature extraction. To address this problem, this paper conducts an analysis of the formation mechanism of adjacent channel interference and proposes a single-cycle pulse signal recognition algorithm based on a one-dimensional deep convolutional neural network (1D-CNN) model. Radial pulse signals were collected from 150 subjects by pulse bracelet, and a dataset comprising 3446 single-cycle signals was extracted in total after denoising, single-cycle segmentation, and standardized preprocessing. The 1D-CNN model is trained to classify input signals into three categories: effective pulse signals, distortion, and interference signals. This classification is achieved by evaluating the waveform morphology of the signals within a single cycle. The results show that the overall classification accuracy of the algorithm on the test set is 98.26%, in which the classification accuracy of pulse waves is 99.8%, indicating that it can effectively recognize single-cycle pulse waves, which lays the foundation for subsequent continuous blood pressure measurement.

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Movement Noise Cancellation in Second Derivative of Photoplethysmography Signals with Wavelet Transform and Diversity Combining

Cited by 4 publications

References 18 publications

Multiscale Hydro-Meteorological Factors Variations and Its Links to Large-Scale Atmospheric Circulation Systems in the Source Region of Yangtze River

Multiscale Hydro-Meteorological Factors Variations and Its Links to Large-Scale Atmospheric Circulation Systems in the Source Region of Yangtze River

Wearable Wireless Devices

Single-Cycle Pulse Signal Recognition Based on One-Dimensional Deep Convolutional Neural Network

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