An optimal filter for short photoplethysmogram signals

Liang, Yongbo; Elgendi, Mohamed; Chen, Zhencheng; Ward, Rabab

doi:10.1038/sdata.2018.76

Cited by 145 publications

(77 citation statements)

References 52 publications

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“…Biosignals are easily affected by noise, which can change the wave morphologies. To reduce noise in PPG signals, three main approaches were proposed in the literature: the use of filtering [90,91], signal quality index [92], and machine learning [73].…”

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confidence: 99%

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Welykholowa

Hosanee

Chan

et al. 2020

JCM

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Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010–2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology.

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confidence: 99%

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Welykholowa

Hosanee

Chan

et al. 2020

JCM

Self Cite

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“…Recently, photoplethysmography (PPG) has been proposed as a continuous, non-invasive approach to BP estimation that can be integrated into wearable devices. PPG records the volumetric pulsations of blood in tissue; these pulsations are associated with the arterial pressure pulse [5]. PPG signals may be integrated with other modalities, such as electrocardiograms, to obtain features such as pulse wave velocity, pulse transit time (PTT), and pulse arrival time (PAT) for BP measurement [6].…”

Section: Plethysmography As a Continuous Non-invasive Approach To Bpmentioning

confidence: 99%

Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring

Hosanee

Chan

Welykholowa

et al. 2020

JCM

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One in three adults worldwide has hypertension, which is associated with significant morbidity and mortality. Consequently, there is a global demand for continuous and non-invasive blood pressure (BP) measurements that are convenient, easy to use, and more accurate than the currently available methods for detecting hypertension. This could easily be achieved through the integration of single-site photoplethysmography (PPG) readings into wearable devices, although improved reliability and an understanding of BP estimation accuracy are essential. This review paper focuses on understanding the features of PPG associated with BP and examines the development of this technology over the 2010–2019 period in terms of validation, sample size, diversity of subjects, and datasets used. Challenges and opportunities to move single-site PPG forward are also discussed.

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“…The acquired raw PPG signal was then pre-processed by a Chebyshev II bandpass filter with the lower and upper cut off frequencies of 0.5 and 10 Hz respectively. This procedure was used to remove the noise from the raw PPG signal which in turn increases the signal quality index (SQI) (Liang et al 2018). The raw ECG signal is processed by a Butterworth bandpass filter with cut off frequencies of 0.5 and 40 Hz to remove the baseline wander, and to suppress spurious data fluctuations due to motion (Shin et al 2010).…”

Section: Signal Pre-processingmentioning

confidence: 99%

Blood pressure estimation with complexity features from electrocardiogram and photoplethysmogram signals

et al. 2020

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A novel method for the continual, cuff-less estimation of the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values based on signal complexity analysis of the photoplethysmogram (PPG) and the electrocardiogram (ECG) is reported. The proposed framework estimates the blood pressure (BP) values obtained from signals generated from 14 volunteers subjected to a series of exercise routines. Herein, the physiological signals were first pre-processed, followed by the extraction of complexity features from both the PPG and ECG. Subsequently the complexity features were used in regression models (artificial neural network (ANN), support vector machine (SVM) and LASSO) to predict the BP. The performance of the approach was evaluated by calculating the mean absolute error (MAE) and the standard deviation (STD) of the predicted results and compared with the recommendations made by the British Hypertension Society (BHS) and Association for the Advancement of Medical Instrumentation (AAMI). Complexity features from the ECG and PPG were investigated independently, along with the combined dataset. It was observed that the complexity features obtained from the combination of ECG and PPG signals resulted to an improved estimation accuracy for the BP. The most accurate DBP result of 5.15 ± 6.46 mmHg was obtained from ANN model, and SVM generated the most accurate prediction for the SBP which was estimated as 7.33 ± 9.53 mmHg. Results for DBP fall within recommended performance of the BHS but SBP is outside the range. Although initial results are promising, further improvements are required before the potential of this approach is fully realised.

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An optimal filter for short photoplethysmogram signals

Cited by 145 publications

References 52 publications

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring

Blood pressure estimation with complexity features from electrocardiogram and photoplethysmogram signals

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