Quality Assessment for the Photoplethysmogram (PPG)

Orphanidou, Christina

doi:10.1007/978-3-319-68415-4_3

Cited by 23 publications

(15 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each question had specific numerical/range selections to choose from, there were no open responses to these questions. The questions related to the minimum length of recording time, the expected proportion of good quality beats per recording, the extent of noise needed to reject a recording, the minimum number of total and also sequential non-distorted i.e., diagnostic quality pulses required (criteria for an analysable beat outlined by Orphanidou ( 9 ): pulse requiring a clear peak, trough, anacrotic, and catacrotic phases), as well as the minimum number of respiratory cycles per recording needed, noting the low-frequency alterations in the PPG signal associated with respiration ( 15 ). The results from a subset of participants specializing in PPG and peripheral arterial occlusive disease (PAOD) diagnostics were also noted.…”

Section: Methodsmentioning

confidence: 99%

Advancing PPG Signal Quality and Know-How Through Knowledge Translation—From Experts to Student and Researcher

Huthart

Elgendi

Zheng

et al. 2020

Front. Digit. Health

View full text Add to dashboard Cite

Objective: Despite the vast number of photoplethysmography (PPG) research publications and growing demands for such sensing in Digital and Wearable Health platforms, there appears little published on signal quality expectations for morphological pulse analysis. Aim: to determine a consensus regarding the minimum number of undistorted i.e., diagnostic quality pulses required, as well as a threshold proportion of noisy beats for recording rejection.Approach: Questionnaire distributed to international fellow researchers in skin contact PPG measurements on signal quality expectations and associated factors concerning recording length, expected artifact-free pulses (“diagnostic quality”) in a trace, proportion of trace having artifact to justify excluding/repeating measurements, minimum beats required, and number of respiratory cycles.Main Results: 18 (of 26) PPG researchers responded. Modal range estimates considered a 2-min recording time as target for morphological analysis. Respondents expected a recording to have 86–95% of diagnostic quality pulses, at least 11–20 sequential pulses of diagnostic quality and advocated a 26–50% noise threshold for recording rejection. There were broader responses found for the required number of undistorted beats (although a modal range of 51–60 beats for both finger and toe sites was indicated).Significance: For morphological PPG pulse wave analysis recording acceptability was indicated if <50% of beats have artifact and preferably that a minimum of 50 non-distorted PPG pulses are present (with at least 11–20 sequential) to be of diagnostic quality. Estimates from this knowledge transfer exercise should help inform students and researchers as a guide in standards development for PPG study design.

show abstract

Section: Methodsmentioning

confidence: 99%

Advancing PPG Signal Quality and Know-How Through Knowledge Translation—From Experts to Student and Researcher

Huthart

Elgendi

Zheng

et al. 2020

Front. Digit. Health

View full text Add to dashboard Cite

show abstract

“…Then, we evaluate the quality of the signal to apply SPARE only if it needs to be cleaned. The signal quality assessment employs a trend-based approach that searches for regularity, the fourth standardized statistical moment (kurtosis) [ 42 ]. If the kurtosis of the signal in the current window is less than a threshold (fixed at 3, the kurtosis of any univariate normal distribution [ 43 ]), no further processing is executed on the signal itself, and it is outputted directly.…”

Section: Spare: Spectral Peak Recovery Algorithmmentioning

confidence: 99%

SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices

Masinelli

Dell’Agnola

Valdés

et al. 2021

Sensors

View full text Add to dashboard Cite

The photoplethysmographic (PPG) signal is an unobtrusive blood pulsewave measure that has recently gained popularity in the context of the Internet of Things. Even though it is commonly used for heart rate detection, it has been lately employed on multimodal health and wellness monitoring applications. Unfortunately, this signal is prone to motion artifacts, making it almost useless in all situations where a person is not entirely at rest. To overcome this issue, we propose SPARE, a spectral peak recovery algorithm for PPG signals pulsewave reconstruction. Our solution exploits the local semiperiodicity of the pulsewave signal, together with the information about the cardiac rhythm provided by an available simultaneous ECG, to reconstruct its full waveform, even when affected by strong artifacts. The developed algorithm builds on state-of-the-art signal decomposition methods, and integrates novel techniques for signal reconstruction. Experimental results are reported both in the case of PPG signals acquired during physical activity and at rest, but corrupted in a systematic way by synthetic noise. The full PPG waveform reconstruction enables the identification of several health-related features from the signal, showing an improvement of up to 65% in the detection of different biomarkers from PPG signals affected by noise.

show abstract

“…The waveform signal quality evaluation method adopted the skewness signal quality index (SSQI) [34]. Orphanidou [35] found that skewness is associated with corrupted PPG signals and has a certain connection with the quality of PPG signals. Liang et al [36] found skewness to be the optimal method for assessing the SQI in PPG signals.…”

Section: A Data Acquisitionmentioning

confidence: 99%

Noninvasive Classification of Blood Pressure Based on Photoplethysmography Signals Using Bidirectional Long Short-Term Memory and Time-Frequency Analysis

2020

View full text Add to dashboard Cite

The photoplethysmography (PPG) method for continuous noninvasive measurements of blood pressure (BP) offers a more comfortable solution than conventional methods. The main challenge in using the PPG method is that its accuracy is greatly influenced by motion artifacts. In addition, the characteristics of PPG vary depending on physiological conditions; hence, the system must be calibrated to adjust for such changes. We attempt to address these limitations and propose a novel method for the classification of BP using a bidirectional long short-term memory (BLSTM) network with time-frequency (TF) analysis based on PPG signals. The TF analysis extracts information from PPG signals using a short-time Fourier transform (STFT) in the time domain to produce two features, namely, the instantaneous frequency and spectral entropy. Training the BLSTM network using TF features significantly improves the classification performance and decreases the training time. We classify 900 PPG waveform segment samples from 219 adult subjects into three classification levels: normotension (NT), prehypertension (PHT) and hypertension (HT). The results show that the proposed method is successful in the classification of BP with accuracy, sensitivity, and speciticity values of 97.33%, 100%, and 94.87%, respectively. The F1 scores of three BP classifications were 97.29%, 97.39%, and 93.93%, respectively. A comparison of current and previous approaches to the classification of BP is accomplished. Our proposed method achieves a higher accuracy than convolutional neural networks (CNNs), k-nearest neighbors (KNN), bagged tree, logistic regression, and AdaBoost tree methods.

show abstract

Quality Assessment for the Photoplethysmogram (PPG)

Cited by 23 publications

References 31 publications

Advancing PPG Signal Quality and Know-How Through Knowledge Translation—From Experts to Student and Researcher

Advancing PPG Signal Quality and Know-How Through Knowledge Translation—From Experts to Student and Researcher

SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices

Noninvasive Classification of Blood Pressure Based on Photoplethysmography Signals Using Bidirectional Long Short-Term Memory and Time-Frequency Analysis

Contact Info

Product

Resources

About