Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography

Sadrawi, Muammar; Lin, Yin-Tsong; Lin, Chien-Hung; Mathunjwa, Bhekumuzi; Fan, Shou-Zen; Abbod, Maysam F.; Shieh, Jiann-Shing

doi:10.3390/s20143829

Cited by 29 publications

(34 citation statements)

References 32 publications

(47 reference statements)

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“…Additionally, while DCAE model only accepted fixed input length, the methodology proposed in the present work does not have that limitation. Although in Sideris et al [ 27 ] and Sadrawi et al [ 28 ] MAE and RMSE values were lower than those in the present work, the number of subjects evaluated was lower and there was no subject’s data restriction between train and test sets.…”

Section: Discussioncontrasting

confidence: 74%

“…On the contrary, in our approach just a single model needs to be trained. In Sadrawi et al [ 28 ] the proposed DCAE model was trained with 18 subjects from closed data. Additionally, while DCAE model only accepted fixed input length, the methodology proposed in the present work does not have that limitation.…”

Section: Discussionmentioning

confidence: 99%

“…Particularly, to the best of our knowledge, few studies aim to the hard task of directly estimate the continuous ABP. In Sideris et al [ 27 ] and Sadrawi et al [ 28 ] a RNN model, with long short-term memory (LSTM) units, and deep convolutional auto-encoder (DCAE) model, respectively, were proposed to transfer signals from PPG to ABP. General surveys on the existing and emerging approaches on this field can be found in Hosanee et al [ 29 ] and Kyriacou [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism

Aguirre

Grall‐Maës

Cymberknop

et al. 2021

Sensors

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Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject’s health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.

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Section: Discussioncontrasting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism

Aguirre

Grall‐Maës

Cymberknop

et al. 2021

Sensors

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“…In [6,7], the authors trained a very deep RNN by introducing skip connections between layers to overcome the vanishing gradient problem [44]. Yang [46].…”

Section: Bp Prediction Using Ppg Featuresmentioning

confidence: 99%

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Schrumpf

Frenzel

Aust

et al. 2021

Sensors

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Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.

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“…The CNN model is a feed forward neural network, and it is a multilayer perceptron model constructed to recognize two-dimensional and above images [ 13 , 14 ]. The current CNN model includes LeNet and AlexNet models, among which the LeNet-5 model is relatively mature, and the network structure is simpler [ 15 , 16 ].…”

Section: Discussionmentioning

confidence: 99%

Computerized Tomography Image Feature under Convolutional Neural Network Algorithm Evaluated for Therapeutic Effect of Clarithromycin Combined with Salmeterol/Fluticasone on Chronic Obstructive Pulmonary Disease

Luo

Lin

Yang

et al. 2021

Journal of Healthcare Engineering

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This study was to explore the use of convolutional neural network (CNN) for the classification and recognition of computerized tomography (CT) images of chronic obstructive pulmonary disease (COPD) and the therapeutic effect of clarithromycin combined with salmeterol/fluticasone. First, the clinical data of COPD patients treated in hospital from September 2018 to December 2020 were collected, and CT and X-ray images were also collected. CT-CNN and X ray-CNN single modal models were constructed based on the LeNet-5 model. The randomized fusion algorithm was introduced to construct a fused CNN model for the diagnosis of COPD patients, and the recognition effect of the model was verified. Subsequently, the three-dimensional reconstruction of the patient’s bronchus was performed using the classified CT images, and the changes of CT quantitative parameters in COPD patients were compared and analyzed. Finally, COPD patients were treated with salmeterol/fluticasone (COPD-C) and combined with clarithromycin (COPD-T). In addition, the differences between patients’ lung function indexes, blood gas indexes, St. George respiratory questionnaire (SGRQ) scores, and the number of acute exacerbations (AECOPD) before and after treatment were evaluated. The results showed that the randomized fusion model under different iteration times and batch sizes always had the highest recognition rate, sensitivity, and specificity compared to the two single modal CNN models, but it also had longer training time. After CT images were used to quantitatively evaluate the changes of the patient’s bronchus, it was found that the area of the upper and lower lung lobes of the affected side of COPD patients and the ratio of the area of the tube wall to the bronchus were significantly changed. The lung function, blood gas index, and SGRQ score of COPD-T patients were significantly improved compared with the COPD-C group ( P < 0.05 ), but there was no considerable difference in AECOPD ( P > 0.05 ). In summary, the randomized fusion-based CNN model can improve the recognition rate of COPD, and salmeterol/fluticasone combined with clarithromycin therapy can significantly improve the clinical treatment effect of COPD patients.

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Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography

Cited by 29 publications

References 32 publications

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Computerized Tomography Image Feature under Convolutional Neural Network Algorithm Evaluated for Therapeutic Effect of Clarithromycin Combined with Salmeterol/Fluticasone on Chronic Obstructive Pulmonary Disease

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