MLP-BP: A novel framework for cuffless blood pressure measurement with PPG and ECG signals based on MLP-Mixer neural networks

Huang, Bin; Chen, Weihai; Lin, Chia Feng; Juang, Chia‐Feng; Wang, Jianhua

doi:10.1016/j.bspc.2021.103404

Cited by 60 publications

(22 citation statements)

References 44 publications

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“…The mean absolute error (MAE), root mean square error (RMSE), and mean error (ME) between the reference BP (

, also called the ground truth value or actual value) and predicted BP (

, also called the estimated value) were adopted to evaluate the experimental results. Unless specified, mathematically speaking, if there are N sets of references and predicted BP values, then they are defined as [ 89 ]

however, some scholars think that the error should be evaluated by subtracting the actual value from the estimated value. Based on this logic, each error should be found by

for each integer k , which implies that the mean error should be defined as follows:

…”

Section: Contact-based Bp Measurement From Ppg Signalsmentioning

confidence: 99%

“…There are also some models other than that in the CNN-RNN architecture for PTT-based BP prediction. Inspired by the MLP-Mixer in computer vision, the authors of [ 89 ] proposed MLP-BP. The ECG and PPG signals are preprocessed by a novel multi-filter-to-multi-channel (MFMC) algorithm, which stacks 12 differently filtered signals as the input.…”

Section: Contact-based Bp Measurement From Ppg Signalsmentioning

confidence: 99%

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Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

et al. 2022

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Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

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“…The mean absolute error (MAE), root mean square error (RMSE), and mean error (ME) between the reference BP (

, also called the ground truth value or actual value) and predicted BP (

however, some scholars think that the error should be evaluated by subtracting the actual value from the estimated value. Based on this logic, each error should be found by

for each integer k , which implies that the mean error should be defined as follows:

…”

Section: Contact-based Bp Measurement From Ppg Signalsmentioning

confidence: 99%

Section: Contact-based Bp Measurement From Ppg Signalsmentioning

confidence: 99%

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

et al. 2022

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“…For instance, a fast discriminative complex-valued convolutional neural network (CNN) [2], namely FDCCNN, is designed to apprehend the hidden correlations in the electroencephalogram (EEG) signals to expedite the sleep stage classification task. Alternatively, MLP-Mixer [16] is also used in the multichannel temporal signal data, resulting in promising results on the regression task. It is proven effective to use recurrent neural network (RNN) based models for capturing non-linear interdependencies, including ConvLSTM [4], Bi-LSTM [5].…”

Section: Related Work a Deep Learning For Spatial-temporal Datamentioning

confidence: 99%

“…• MLP-Mixer [16] is a simple yet powerful model initially designed to capture spatial location features for image data, and also found useful when processing temporal signal data.…”

Section: Algorithm 1 Federated Relevance Trainingmentioning

confidence: 99%

FedRel: An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning

Zhang¹,

Liu²,

Shen³

et al. 2022

Preprint

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Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medical institutions often use electrodes attached to different parts of a patient to analyse the electorencephal data rich with spatial and temporal features for health assessment and disease diagnosis. Existing research has mainly used deep learning techniques such as convolutional neural network (CNN) or recurrent neural network (RNN) to extract hidden spatialtemporal features. Yet, it is challenging to incorporate both inter-dependencies spatial information and dynamic temporal changes simultaneously. In reality, for a model that leverages these spatial-temporal features to fulfil complex prediction tasks, it often requires a colossal amount of training data in order to obtain satisfactory model performance. Considering the abovementioned challenges, we propose an adaptive federated relevance framework, namely FedRel, for spatial-temporal graph learning in this paper. After transforming the raw spatialtemporal data into high quality features, the core Dynamic Inter-Intra Graph (DIIG) module in the framework is able to use these features to generate the spatial-temporal graphs capable of capturing the hidden topological and long-term temporal correlation information in these graphs. To improve the model generalization ability and performance while preserving the local data privacy, we also design a relevance-driven federated learning module in our framework to leverage diverse data distributions from different participants with attentive aggregations of their models. In addition, we conduct extensive experiments on two real-world spatial-temporal datasets. The results demonstrate the effectiveness of our proposed framework in spatial-temporal interpretation, collaborative model training, and divergent data distribution handling in most settings of comparison.

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“…The biggest feature of the GMDH network is that the input quantity taken is automatically determined during the training process, while neural networks such as BP require a lot of prior knowledge to determine the network structure, but the problem of time-consuming nonlinear modeling of the GMDH algorithm is unavoidable [32,33]. MLP is a generalization of single-layer perceptron, which can solve nonlinear problems that single-layer perceptron cannot solve [34]. Roshani, G. H. et al using an ANN model, the volume fraction of gas, oil and water in three-phase flow independent of flow regime was predicted with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

Zhou

Qin

et al. 2022

Energies

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With the gradual popularization of hydrogen fuel cell vehicles (HFCVs), the construction and planning of hydrogen refueling stations (HRSs) are increasingly important. Taking operational HRSs in China’s coastal and major cities as examples, we consider the main factors affecting the site selection of HRSs in China from the three aspects of economy, technology and society to establish a site selection evaluation system for hydrogen refueling stations and determine the weight of each index through the analytic hierarchy process (AHP). Then, combined with fuzzy comprehensive evaluation (FCE) method and artificial neural network model (ANN), FCE method is used to evaluate HRS in operation in China's coastal areas and major cities, and we used the resulting data obtained from the comprehensive evaluation as the training data to train the neural network. So, an intelligent site selection model for HRSs based on fuzzy comprehensive evaluation and artificial neural network model (FCE-ANN) is proposed. The planned HRSs in Shanghai are evaluated, and an optimal site selection of the HRS is obtained. The results show that the optimal HRSs site selected by the FCE-ANN model is consistent with the site selection obtained by the FCE method, and the accuracy of the FCE-ANN model is verified. The findings of this study may provide some guidelines for policy makers in planning the hydrogen refueling stations.

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MLP-BP: A novel framework for cuffless blood pressure measurement with PPG and ECG signals based on MLP-Mixer neural networks

Cited by 60 publications

References 44 publications

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

FedRel: An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning

An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

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