Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks

Mehrabadi, Milad Asgari; Aqajari, Seyed Amir Hossein; Zargari, Amir Hosein Afandizadeh; Dutt, Nikil; Rahmani, Amir M.

doi:10.48550/arxiv.2201.09976

Cited by 2 publications

(6 citation statements)

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“…Ref. [ 75 ] displayed no result for waveform; even the estimated waveforms are not displayed. The results for SBP and DBP are presented, and the results satisfy the standards when the given value is used.…”

Section: Results Comparisonmentioning

confidence: 99%

“…There are no definitive metrics for the waveform estimation result. The majority of papers did not employ any metrics to evaluate the estimated waveforms [ 32 , 33 , 34 , 39 , 73 , 74 , 75 ]. The accuracy of waveforms is demonstrated by plotting several waveforms against their reference waveforms.…”

Section: Discussion and Future Prospectsmentioning

confidence: 99%

“…Another recent work [ 75 ] published on the preprint server, proposed the use of a cycle generative adversarial network (CycleGAN) to predict BP waveforms using PPG data as input. Despite the fact that the majority of the papers used an encoder–decoder method, they used a generator and a discriminator network to estimate the waveforms, which is a novel technique.…”

Section: Noninvasive Bp Waveform Estimation Methodsmentioning

confidence: 99%

“…In [ 75 ], the Fourier Transform (FFT) was used to remove unwanted information from both PPG and ABP signals. Then, for the PPG signal, a bandpass filter with cutoff frequencies of 0.1 Hz and 8 Hz was used, and for the ABP signal, a low-pass filter with a cutoff frequency of 5 Hz was used.…”

Section: Noninvasive Bp Waveform Estimation Methodsmentioning

confidence: 99%

“… Development of different methods over the years. Invasive blood pressure waveform study [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]; Arterial tonometry study [ 33 , 65 , 67 , 68 , 86 , 89 , 90 , 91 ]; Volume clamp method study [ 32 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 92 , 93 , 94 , 95 , 96 , 97 ]; Ultrasound-based method study [ 34 , 35 , 36 ]; Deep learning with biosignals related study [ 25 , 37 , 38 , 39 , 40 , 70 , 72 , 73 , 74 , 75 ]. …”

Section: Figurementioning

confidence: 99%

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A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform

Athaya

Choi

2022

Sensors

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Accurate estimation of blood pressure (BP) waveforms is critical for ensuring the safety and proper care of patients in intensive care units (ICUs) and for intraoperative hemodynamic monitoring. Normal cuff-based BP measurements can only provide systolic blood pressure (SBP) and diastolic blood pressure (DBP). Alternatively, the BP waveform can be used to estimate a variety of other physiological parameters and provides additional information about the patient’s health. As a result, various techniques are being proposed for accurately estimating the BP waveforms. The purpose of this review is to summarize the current state of knowledge regarding the BP waveform, three methodologies (pressure-based, ultrasound-based, and deep-learning-based) used in noninvasive BP waveform estimation research and the feasibility of employing these strategies at home as well as in ICUs. Additionally, this article will discuss the physical concepts underlying both invasive and noninvasive BP waveform measurements. We will review historical BP waveform measurements, standard clinical procedures, and more recent innovations in noninvasive BP waveform monitoring. Although the technique has not been validated, it is expected that precise, noninvasive BP waveform estimation will be available in the near future due to its enormous potential.

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Section: Results Comparisonmentioning

confidence: 99%

Section: Discussion and Future Prospectsmentioning

confidence: 99%

Section: Noninvasive Bp Waveform Estimation Methodsmentioning

confidence: 99%

Section: Noninvasive Bp Waveform Estimation Methodsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform

Athaya

Choi

2022

Sensors

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Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

Omer,

Salah,

Hassan

et al. 2024

BioMedInformatics

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One of the most significant indicators of heart and cardiovascular health is blood pressure (BP). Blood pressure (BP) has gained great attention in the last decade. Uncontrolled high blood pressure increases the risk of serious health problems, including heart attack and stroke. Recently, machine/deep learning has been leveraged for learning a BP from photoplethysmography (PPG) signals. Hence, continuous BP monitoring can be introduced, based on simple wearable contact sensors or even remotely sensed from a proper camera away from the clinical setup. However, the available training dataset imposes many limitations besides the other difficulties related to the PPG time series as high-dimensional data. This work presents beat-by-beat continuous PPG-based BP monitoring while accounting for the aforementioned limitations. For a better exploration of beats’ features, we propose to use wavelet scattering transform as a better descriptive domain to cope with the limitation of the training dataset and to help the deep learning network accurately learn the relationship between the morphological shapes of PPG beats and the BP. A long short-term memory (LSTM) network is utilized to demonstrate the superiority of the wavelet scattering transform over other domains. The learning scenarios are carried out on a beat basis where the input corresponding PPG beat is used for predicting BP in two scenarios; (1) Beat-by-beat arterial blood pressure (ABP) estimation, and (2) Beat-by-beat estimation of the systolic and diastolic blood pressure values. Different transformations are used to extract the features of the PPG beats in different domains including time, discrete cosine transform (DCT), discrete wavelet transform (DWT), and wavelet scattering transform (WST) domains. The simulation results show that using the WST domain outperforms the other domains in the sense of root mean square error (RMSE) and mean absolute error (MAE) for both of the suggested two scenarios.

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Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks

Cited by 2 publications

References 0 publications

A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform

A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform

Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

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