A Survey on Denoising Techniques of PPG Signal

Mishra, Bhanupriya; Nirala, Neelam Sobha

doi:10.1109/inocon50539.2020.9298358

Cited by 21 publications

(13 citation statements)

References 26 publications

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“…However, we did not implement this as part of this work in keeping with noisy real-world data. In the case of using denoising methods for PPG signals to obtain clean training data, we can seek to apply the techniques listed in [ 27 ]. Furthermore, with the increasing environmental costs of machine-learning practises worldwide, we are concerned with model complexity and training time.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Brophy

Vos

Boylan

et al. 2021

Sensors

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Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.

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Section: Discussionmentioning

confidence: 99%

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Brophy

Vos

Boylan

et al. 2021

Sensors

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“…In each entry in Table 2 , we listed the datasets, their descriptions, the preprocessing methods, the model names, and their results. Only the preprocessing methods that affected the input features or model structure were mentioned, and the reduction of noise for the PPG waveform signals (Multi-various kinds of algorithms were adopted [ 90 ], including an LMS filter [ 91 ], moving average filters [ 92 ], adaptive filters [ 93 , 94 ], the Kalman filter [ 95 , 96 ], IMAR [ 97 ], wavelet analysis [ 98 , 99 , 100 ], median analysis [ 101 , 102 , 103 ], and a notch filter [ 104 ].) were excluded.…”

Section: Contact-based Bp Measurement From Ppg Signalsmentioning

confidence: 99%

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

et al. 2022

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Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

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“…Under normal circumstances, the centerline of the pulse wave signal is relatively smooth, indicating the signal's DC component is stable. However, the acquired signal has a constantly changing amplitude value of the overall waveform due to BW caused by multiple factors, such as temperature variations, the bias of the instrumentation amplifier, and breathing motion [37]. High-frequency noise is normally caused by power line interference that refers to the ambient electromagnetic signal of the instrument amplifier and the power supply obstruction of the PPG recording probe [34].…”

Section: B Noise Reduction Layermentioning

confidence: 99%

SoK: An Overview of PPG's Application in Authentication

Li¹,

Chen²,

Pan³

et al. 2022

Preprint

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Biometric authentication prospered during the 2010s. Vulnerability to spoofing attacks remains an inherent problem with traditional biometrics. Recently, unobservable physiological signals (e.g., Electroencephalography, Photoplethysmography, Electrocardiography) as biometrics have been considered a potential solution to this problem. In particular, Photoplethysmography (PPG) measures the change of blood flow of the human body by an optical method. Clinically, researchers commonly use PPG signals to obtain patients' blood oxygen saturation, heart rate, and other information to assist in diagnosing heart-related diseases. Since PPG signals are easy to obtain and contain a wealth of individual cardiac information, researchers have begun to explore its potential applications in information security. The unique advantages (simple acquisition, difficult to steal, and live detection) of the PPG signal allow it to improve the security and usability of the authentication in various aspects. However, the research on PPG-based authentication is still in its infancy. The lack of systematization hinders new research in this field. We conduct a comprehensive study of PPGbased authentication and discuss these applications' limitations before pointing out future research directions.

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A Survey on Denoising Techniques of PPG Signal

Cited by 21 publications

References 26 publications

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

SoK: An Overview of PPG's Application in Authentication

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