Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition Technique and lightweight CNN Module

Chatterjee, Tamaghno; Ghosh, Aayushman; Sarkar, Supriya

doi:10.1109/embc48229.2022.9871494

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…With these insights, we propose a quality metric that takes into account the spectral shape of the modulation lobes. While one may choose to use the modulation spectrogram directly as input to a machine learning algorithm (i.e., to be treated as an image, similar to many applications relying on spectrograms [24,25]) for PPG quality assessment, this increases the model complexity and makes the system less interpretable. To improve the interpretability and simplicity of the proposed method, here, we decided to extract metrics from the modulation spectrogram and then apply them to machine learning algorithms.…”

Section: Signal Processing Stepsmentioning

confidence: 99%

“…More recently, approaches based on machine learning have emerged. These can rely on conventional methods where features are first extracted and then applied to a classifier (e.g., [ 23 ]) or on end-to-end deep learning paradigms where decisions are made based on raw or transformed PPG signal inputs (e.g., [ 24 , 25 ]). The work in [ 24 ], for example, proposed the use of the short-time Fourier transformed version of the PPG signal as input to a convolutional neural network (CNN).…”

Section: Introductionmentioning

confidence: 99%

“…The work in [ 24 ], for example, proposed the use of the short-time Fourier transformed version of the PPG signal as input to a convolutional neural network (CNN). The authors in [ 25 ], in turn, decomposed the PPG signal into various frequency components and generated a 2D image to serve as input to a lightweight CNN for signal quality assessment.…”

Section: Introductionmentioning

confidence: 99%

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Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features

Tiwari

Gray²,

Bondi³

et al. 2023

Sensors

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Photoplethysmography (PPG) is used to measure blood volume changes in the microvascular bed of tissue. Information about these changes along time can be used for estimation of various physiological parameters, such as heart rate variability, arterial stiffness, and blood pressure, to name a few. As a result, PPG has become a popular biological modality and is widely used in wearable health devices. However, accurate measurement of various physiological parameters requires good-quality PPG signals. Therefore, various signal quality indexes (SQIs) for PPG signals have been proposed. These metrics have usually been based on statistical, frequency, and/or template analyses. The modulation spectrogram representation, however, captures the second-order periodicities of a signal and has been shown to provide useful quality cues for electrocardiograms and speech signals. In this work, we propose a new PPG quality metric based on properties of the modulation spectrum. The proposed metric is tested using data collected from subjects while they performed various activity tasks contaminating the PPG signals. Experiments on this multi-wavelength PPG dataset show the combination of proposed and benchmark measures significantly outperforming several benchmark SQIs with improvements of 21.3% BACC (balanced accuracy) for green, 21.6% BACC for red, and 19.0% BACC for infrared wavelengths, respectively, for PPG quality detection tasks. The proposed metrics also generalize for cross-wavelength PPG quality detection tasks.

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Section: Signal Processing Stepsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features

Tiwari

Gray²,

Bondi³

et al. 2023

Sensors

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show abstract

“…This measurement provides valuable information about the cardiovascular system. One challenge affecting the on-device performance of PPG is its vulnerability to noise, including motion artifacts [Chatterjee et al 2022], which can distort the signal's morphological properties and lead to erroneous estimation of the physiological variables. Due to the potential life-threatening consequences associated with erroneous assessments derived from these signals, such unreliable performance is unacceptable in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

Photoplethysmography Signal Quality Assessment using Attentive-CNN Models

Silva,

Lima,

Lucafo

et al. 2024

Anais Do XXIV Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2024)

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Due to the rapid popularization of wearable computers such as smartwatches, Health Monitoring Applications (HMA) are becoming increasingly popular because of their capability to track different health indicators, including sleep patterns, heart rate, and activity tracking movements. These applications usually employ Photoplethysmography (PPG) sensors to monitor various aspects of an individual’s health and well-being. PPG is a non-invasive and cost-effective optical technique based on the detection of blood volume changes in the microvascular bed of tissue, capturing the dynamic physiological changes in the body with continuous measurements taken over time. Analyzing PPG as a time series enables the extraction of meaningful information about cardiovascular health and other physiological parameters, such as Heart Rate Variability (HRV), Peripheral Oxygen Saturation (SpO2), and sleep status. To enable reliable health indicators, it is important to have robustly sampled PPG signals. However, in practice, the PPG signal is often corrupted with different types of noise and artifacts due to motion, especially in scenarios where wearables are used. Therefore, Signal Quality Assessment (SQA) plays a fundamental role in determining the reliability of a given PPG for use in HMA. Considering this, in this work, we propose a novel PPG SQA method focused on the balance between storage size and classifier quality, aiming to achieve a lightweight and robust model. This model is developed using recent advances in attention-based strategies to significantly improve the performance of purely Convolutional Neural Network (CNN)-based SQA classifiers.

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Schrödinger spectrum and slim CNN architecture-based signal quality estimation for Photoplethysmogram signals

Sarkar,

Ghosh

2024

Biomedical Signal Processing and Control

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Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition Technique and lightweight CNN Module

Cited by 10 publications

References 24 publications

Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features

Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features

Photoplethysmography Signal Quality Assessment using Attentive-CNN Models

Schrödinger spectrum and slim CNN architecture-based signal quality estimation for Photoplethysmogram signals

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