Adaptive notch-filtration to effectively recover photoplethysmographic signals during physical activity

Zheng, Xiaoyu; Dwyer, Vincent M.; Barrett, Laura A.; Derakhshani, Mahsa; Hu, Sijung

doi:10.1016/j.bspc.2021.103303

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…5 is a reference heart rate recorded using a Polar Bluetooth Smart chest strap and the Reference RR is collected by a VyntusTM CPX Metabolic cart. In both figures the ANF reference uses our previously published algorithm [12]. The performance in the extraction of HR and RR is robust and highly correlated with the two references provided.…”

Section: Resultsmentioning

confidence: 95%

“…With this in mind, the aim of the current study is to test the principal that a GAN can be used to clean noisy signals in an effective manner. Here, we use a simplified version of the cleansing process, the ANF algorithm, which is able to accurately extract both HR and RR, including during medium-high intensity physical activity [12]. We thus solve a proxy problem which replaces the waveforms p(t) which would nominally be created using the state-of-the-art cleaning methods with the ANF algorithm to create ANF-clean, paired data p ref (t) = A(x(t)) from measured training/testing data x(t) ∈ X tr ∪ X tr .…”

Section: B Focus Of Current Workmentioning

confidence: 99%

“…The exercise datasets used here are provided by Loughborough University (the in-house dataset) and the IEEE Signal Processing Cup 2015 (the IEEE-SPC dataset). The in-house dataset was recorded using a four-channel multi-wavelength illumination optoelectronic patch sensor (mOEPS) at a sampling frequency of 256Hz, from twelve subjects with ages from 23 to 37, while exercising on a treadmill [12]. Subjects performed the exercise at four different intensities (3kmh −1 , 6kmh −1 , 9kmh −1 , 12kmh −1 ); each stage lasting four minutes with a recovery of 60s between.…”

Section: B Measurement Protocolmentioning

confidence: 99%

See 2 more Smart Citations

PPG-GAN: An Adversarial Network to De-noise PPG Signals during Physical Activity

Zheng

Derakhshani

Barrett

et al. 2022

2022 IEEE International Conference on E-Health Networking, Application &Amp; Services (HealthCom)

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Quality photoplethysmographic (PPG) signals are essential for accurate physiological assessment. However, the PPG acquisition process is often accompanied by spurious motion artefacts (MAs), especially during medium-high intensity physical activity. This study proposes a generative adversarial network (PPG-GAN) to create de-noised versions of measure PPG signals.The Adaptive Notch Filtration (ANF) algorithm, which enables the extraction of accurate heart rates (HR) and respiration rates (RR) from PPG signals, is used as the approximate reference signal to train the PPG-GAN. The generated PPG signals from test inputs provide a heart rate (HR) with a mean absolute error of 1.68 bpm for the IEEE-SPC dataset. A comparison with goldstandard HR and RR measurements, for our in-house dataset, show the errors in absolute value of less than 5%. The generated PPG signals, for the test clips, show a very strong correlation with their reference values, R ≈ 0.98. The results suggest that PPG-GAN could be a paradigm for MA-free PPG signal processing specifically for personal healthcare, even during high intensity activity.

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Section: Resultsmentioning

confidence: 95%

Section: B Focus Of Current Workmentioning

confidence: 99%

Section: B Measurement Protocolmentioning

confidence: 99%

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PPG-GAN: An Adversarial Network to De-noise PPG Signals during Physical Activity

Zheng

Derakhshani

Barrett

et al. 2022

2022 IEEE International Conference on E-Health Networking, Application &Amp; Services (HealthCom)

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“…Firstly, the verification mainly ensures that the HRs in two successive time windows are not unrealistically different. The verification rule [36] is set by: |HR cal (i) − HR cal (i − 1)| < δ. Here δ is a tolerance parameter to ensure the change HR value is unphysical, here we set (δ=10).…”

Section: N: According Tomentioning

confidence: 99%

Rapid Vital Sign Extraction for Real-Time Opto-Physiological Monitoring at Varying Physical Activity Intensity Levels

Zheng

Dwyer

Barrett

et al. 2023

IEEE J. Biomed. Health Inform.

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“…By applying the technique to the raw PPG data, a clear spectrum was produced. Similarly, extracting heart rate and respiration rate values from raw PPG data with a three-axis accelerometer for motion reference was studied [ 18 ]. This study proposed an adaptive notch-filtration architecture, which comprises the adaptive moving average filter, the adaptive notch filter, and the extraction for physiological parameters.…”

Section: Introductionmentioning

confidence: 99%

Photoplethysmography-Based Distance Estimation for True Wireless Stereo

et al. 2023

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Recently, supplying healthcare services with wearable devices has been investigated. To realize this for true wireless stereo (TWS), which has limited resources (e.g., space, power consumption, and area), implementing multiple functions with one sensor simultaneously is required. The Photoplethysmography (PPG) sensor is a representative healthcare sensor that measures repeated data according to the heart rate. However, since the PPG data are biological, they are influenced by motion artifact and subject characteristics. Hence, noise reduction is needed for PPG data. In this paper, we propose the distance estimation algorithm for PPG signals of TWS. For distance estimation, we designed a waveform adjustment (WA) filter that minimizes noise while maintaining the relationship between before and after data, a lightweight deep learning model called MobileNet, and a PPG monitoring testbed. The number of criteria for distance estimation was set to three. In order to verify the proposed algorithm, we compared several metrics with other filters and AI models. The highest accuracy, precision, recall, and f1 score of the proposed algorithm were 92.5%, 92.6%, 92.8%, and 0.927, respectively, when the signal length was 15. Experimental results of other algorithms showed higher metrics than the proposed algorithm in some cases, but the proposed model showed the fastest inference time.

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Adaptive notch-filtration to effectively recover photoplethysmographic signals during physical activity

Cited by 9 publications

References 30 publications

PPG-GAN: An Adversarial Network to De-noise PPG Signals during Physical Activity

PPG-GAN: An Adversarial Network to De-noise PPG Signals during Physical Activity

Rapid Vital Sign Extraction for Real-Time Opto-Physiological Monitoring at Varying Physical Activity Intensity Levels

Photoplethysmography-Based Distance Estimation for True Wireless Stereo

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