Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

Salah, Mostafa; Omer, Osama A.; Hassan, Loay; Ragab, Mohamed; Hassan, Ammar M.; Abdelreheem, Ahmed

doi:10.1109/access.2022.3175436

Cited by 16 publications

(18 citation statements)

References 54 publications

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“…The cleaned dataset [27] is employed. The dataset was split into training, validation, and test sets on a beat basis to prevent contamination of the validation and test set by training data.…”

Section: Resultsmentioning

confidence: 99%

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Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

Omer

Salah

Hassan

et al. 2023

Preprint

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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 is leveraged for learning the 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. Long Short-Term Memory (LSTM) network is utilized to demonstrate the superiority of the wavelet scattering transform over others 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, DCT, DWT, and wavelet scattering Domains. The simulation results show that using the wavelet scattering 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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Section: Resultsmentioning

confidence: 99%

“…An inspection of that data set, however, indicates a high number of erroneous PPG and ABP signals. That dataset will be used to produce a simultaneously cleaned PPG-ABP dataset [27] that will be fed into two deep learning-based BP estimation models. This original data set contains 12,000 records of varying lengths.…”

Section: Data Setmentioning

confidence: 99%

Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

Omer

Salah

Hassan

et al. 2023

Preprint

Self Cite

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“…Hence, the constraints on HR imply corresponding constraints on the spectral shape. Moreover, thanks to the semi-periodicity of PPG/ECG signals, most of the signal power has to be concentrated around the fundamental frequency and its harmonics with a very narrow bandwidth of 0.2 Hz [16]. The ratio of the power of the inband signal to the power of the out-band signal is calculated by Eq.…”

Section: Learning Phasementioning

confidence: 99%

“…The skewed beat in green is invalid as well. The three following criteria [16] are used to select valid beats and reject the others:…”

Section: Beat Segmentationmentioning

confidence: 99%

Beat-by-Beat ECG Monitoring from Photoplythmography Based on Scattering Wavelet Transform

Omer¹,

Salah²,

Hassan³

et al. 2022

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The electrocardiogram (ECG) is a popular measurement scheme to assess and diagnose cardiovascular diseases. ECG devices use gel material and electrodes that may cause skin irritation and discomfort during long use, which restricts the long-term use of these devices. On the other hand, Photoplethysmography (PPG) is an optical approach used to estimate the skin blood flow using photons. Recently, the relationship between the PPG and ECG has been recognized and there are early stage attempts to reconstruct the ECG signals from PPG signals that can lead to giving up electrodes and skin irritating and uncomfortable materials. However, these recent researches suffer from the sensitivity to the PPG signal quality, shifting, and scaling. Therefore, it is restricted with constrained PPG signals. In this paper, we propose an ECG reconstruction system that is independent of PPG scaling and shifting. The proposed system is based on scattering wavelet transform (SWT) as a feature domain along with the deep learning network. Using SWT helps deep learning networks to learn the non-linear relationship between ECG and PPG even with small datasets. Also, the proposed system is based on beat-by-beat ECG estimation rather than signal-based which leads to the learning of local features rather than global features. Based on the presented simulation results, the proposed system with beat-by-beat SWT features extraction outperforms the other feature domains; Time, DCT, and DWT domains.

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“…These methods usually take the PPG signal, along with the Electrocardiogram (ECG) signal, in most cases, and predict the values of Diastolic Blood Pressure (DBP), Systolic Blood Pressure (SBP), and Mean Arterial Pressure (MAP). Some other studies used Deep Machine Learning-based approaches for BP prediction from PPG and/or ECG signals [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. However, they are suffering from some limitations.…”

Section: Introductionmentioning

confidence: 99%

PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms

et al. 2022

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Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.

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Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

Cited by 16 publications

References 54 publications

Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

Beat-by-Beat ECG Monitoring from Photoplythmography Based on Scattering Wavelet Transform

PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms

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