Real-World PPG dataset

Siam, Ali I.; El-Samie, Fathi Abd; Elazm, Atef Abu; El‐Bahnasawy, Nirmeen A.; El‐Banby, Ghada M.

doi:10.17632/yynb8t9x3d.1

Cited by 8 publications

(26 citation statements)

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“…The NPD used for the whole experiments illustrated above was trained with input signals resampled according to Mendeley data [ 14 ]. However, similar to BIDMC data [ 15 ] and wrist PPG data [ 20 ], there are data of various sampling rates, such as 125 Hz and 256 Hz, and the PPG signal obtained by a more precise machine has a 1 kHz sampling rate.…”

Section: Resultsmentioning

confidence: 99%

“…By casting the denoising problem into an image-restoration problem using a SR approach, the authors were able to build transformer-based deep generative models that could facilitate the universal noise removal of continuous PPG signals using a variant of an image-based TTSR framework [ 12 ]. Open datasets Mendeley data [ 14 ] and BIDMC [ 15 ] were used for NPD learning and performance evaluation. The PPG signals obtained from the dataset were resampled to 50 Hz and normalized to a range of −1 to 1.…”

Section: Discussionmentioning

confidence: 99%

“…Experiments were conducted to evaluate the performance of the NPD in various types of noise. The Mendeley data [ 14 ] and BIDMC [ 15 ] open datasets were used for this. Mendeley data were collected from healthy people with no control conditions, whereas the BIDMC was acquired from critically ill patients during hospital care at Beth Israel Deaconess Medical Center (Boston, MA, USA).…”

Section: Methodsmentioning

confidence: 99%

“…The data presented in this study are openly available in Mendeley Data at 10.17632/yynb8t9x3d.1 (accessed on 5 March 2022), reference number [ 14 ], The data presented in this study are openly available in PhysioNet at (accessed on 5 March 2022), reference number [ 15 ]. Publicly available datasets were analyzed in this study.…”

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Preeminently Robust Neural PPG Denoiser

Kwon

Kim

et al. 2022

Sensors

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Photoplethysmography (PPG) is a simple and cost-efficient technique that effectively measures cardiovascular response by detecting blood volume changes in a noninvasive manner. A practical challenge in the use of PPGs in real-world applications is noise reduction. PPG signals are likely to be compromised by various types of noise, such as scattering or motion artifacts, and removing such compounding noises using a monotonous method is not easy. To this end, this paper proposes a neural PPG denoiser that can robustly remove multiple types of noise from a PPG signal. By casting the noise reduction problem into a signal restoration approach, we aim to achieve a solid performance in the reduction of different noise types using a single neural denoiser built upon transformer-based deep generative models. Using this proposed method, we conducted the experiments on the noise reduction of a PPG signal synthetically contaminated with five types of noise. Following this, we performed a comparative study using six different noise reduction algorithms, each of which is known to be the best model for each noise. Evaluation results of the peak signal-to-noise ratio (PSNR) show that the neural PPG denoiser is superior in three out of five noise types to the performance of conventional noise reduction algorithms. The salt-and-pepper noise type showed the best performance, with the PSNR of the neural PPG denoiser being 36.6080, and the PSNRs of the other methods were 19.8160 and 32.8234. The Poisson noise type performed the worst, showing a PSNR of 33.0090; the PSNRs of other methods were 35.1822 and 33.4795, respectively. Thereafter, an experiment to recover a signal synthesized with two or more of the five noise types was conducted. When the number of mixed noises was two, three, four, and five, the PSNRs were 29.2759, 27.8759, 26.5608, and 25.9402, respectively. Finally, an experiment to recover motion artifacts was also conducted. The synthesized motion artifact signal was created by synthesizing only a certain ratio of the total signal length. As a result of the motion artifact signal restoration, the PSNRs were 25.2872, 22.8240, 21.2901, and 19.9577 at 30%, 50%, 70%, and 90% motion artifact ratios, respectively. In the three experiments conducted, the neural PPG denoiser showed that various types of noise were effectively removed. This proposal contributes to the universal denoising of continuous PPG signals and can be further expanded to denoise continuous signals in the general domain.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Preeminently Robust Neural PPG Denoiser

Kwon

Kim

et al. 2022

Sensors

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“…We use two datasets to evaluate the performance of Car-dioID. The first dataset, using pulse oximeter for signal collection, is public and recently published in [34]. All subjects are sitting during the signal collection.…”

Section: Datasetsmentioning

confidence: 99%

CardioID: Mitigating the Effects of Irregular Cardiac Signals for Biometric Identification

Wang¹,

Zúñiga²,

Wang³

2022

Preprint

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Cardiac patterns are being used to obtain hard-to-forge biometric signatures and have led to high accuracy in stateof-the-art (SoA) identification applications. However, this performance is obtained under controlled scenarios where cardiac signals maintain a relatively uniform pattern, facilitating the identification process. In this work, we analyze cardiac signals collected in more realistic (uncontrolled) scenarios and show that their high signal variability (i.e., irregularity) makes it harder to obtain stable and distinct user features. Furthermore, SoA usually fails to identify specific groups of users, rendering existing identification methods futile in uncontrolled scenarios. To solve these problems, we propose a framework with three novel properties. First, we design an adaptive method that achieves stable and distinct features by tailoring the filtering spectrum to each user. Second, we show that users can have multiple cardiac morphologies, offering us a much bigger pool of cardiac signals and users compared to SoA. Third, we overcome other distortion effects present in authentication applications with a multicluster approach and the Mahalanobis distance. Our evaluation shows that the average balanced accuracy (BAC) of SoA drops from above 90% in controlled scenarios to 75% in uncontrolled ones, while our method maintains an average BAC above 90% in uncontrolled scenarios.

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Biosignal classification for human identification based on convolutional neural networks

Siam

Sedik

El‐Shafai

et al. 2021

Int J Communication

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Human identification is considered as a serious challenge for several applications such as cybersecurity and access control. Recently, the trend of human identification has been directed to human biometrics, which can be used to recognize persons based on some physiological or behavioral characteristics that they own, such as fingerprint, iris, and biosignals. There are several types of human biosignals including electroencephalography (EEG), electrocardiography (ECG), and photoplethysmography (PPG) signals. This paper presents a human identification system based on PPG signals. The proposed system consists of three main phases: signal acquisition, signal pre‐processing, and feature extraction/classification. The pre‐processing phase involves denoising of the acquired signal, transformation of the 1D signal sequence into a 2D image, and computation of the spectrogram. Feature extraction is carried out on the images obtained from the pre‐processing phase. Features are extracted from the images based on convolutional neural networks (CNNs). The proposed CNN model consists of a sequence of convolutional (CNV) and pooling layers. Finally, the obtained feature maps are fed to the classifier to discriminate human identities. The proposed identification algorithm is applied on signals with and without an additive white Gaussian noise (AWGN). The simulation results reveal that the proposed algorithm achieves an accuracy of 99.5% with the spectrogram representation and 89.8% with the 2D image representation, in the absence of noise. In addition, the paper gives a discussion of the efficiency of denoising techniques such as wavelet denoising, Savitzky–Golay and Kalman filtering, when involved with the proposed algorithm. The simulation results prove that the wavelet dencoising technique has a best performance among the discussed noise reduction techniques.

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Real-World PPG dataset

Cited by 8 publications

References 0 publications

Preeminently Robust Neural PPG Denoiser

Preeminently Robust Neural PPG Denoiser

CardioID: Mitigating the Effects of Irregular Cardiac Signals for Biometric Identification

Biosignal classification for human identification based on convolutional neural networks

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