scholarly journals Concatenated Convolutional Neural Network Model for Cuffless Blood Pressure Estimation Using Fuzzy Recurrence Properties of PPG Signals

2022 ◽  
Author(s):  
Ali Bahari Malayeri ◽  
Mohammad Bagher Khodabakhshi
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Neural Networks ◽  
Network Architecture ◽  
Spatial Information ◽  
Recurrence Plot ◽  
Estimation Model ◽  
British Hypertension Society ◽  
Blood Pressure Estimation ◽  
Cuffless Blood Pressure

Abstract Due to the importance of continuous monitoring of blood pressure (BP) in controlling hypertension, the topic of cuffless blood pressure (BP) estimation has been widely studied in recent years. A most important approach is to explore the nonlinear mapping between the recorded peripheral signals and the BP values which is usually conducted by deep neural networks. Because of the sequence-based pseudo periodic nature of peripheral signals such as photoplethysmogram (PPG), a proper estimation model needed to be equipped with the 1-dimensional (1-D) and recurrent layers. This, in turn, limits the usage of 2-dimensional (2-D) layers adopted in convolutional neural networks (CNN) for embedding spatial information in the model. In this study, considering the advantage of chaotic approaches, the recurrence characterization of peripheral signals was taken into account by a visual 2-D representation of PPG in phase space through fuzzy recurrence plot (FRP). FRP not only provides a beneficial framework for capturing the spatial properties of input signals but also creates a reliable approach for embedding the pseudo periodic properties to the neural models without using recurrent layers. Moreover, this study proposes a novel deep neural network architecture that combines the morphological features extracted simultaneously from two upgraded 1-D and 2-D CNNs capturing the temporal and spatial dependencies of PPGs in systolic and diastolic BP estimation. The model has been fed with the 1-D PPG sequences and the corresponding 2-D FRPs from two separate routes. The performance of the proposed framework was examined on the well-known public dataset, namely, Multi-Parameter Intelligent in Intensive Care II. Our scheme is analyzed and compared with the literature in terms of the requirements of the standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The proposed model met the AAMI requirements, and it achieved a grade of A as stated by the BHS standard. In addition, its mean absolute errors (MAE) and standard deviation for both systolic and diastolic blood pressure estimations were considerably low, 3.05±5.26 mmHg and 1.58±2.6 mmHg, in turn.

scholarly journals Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5668
Author(s):  
Yan-Cheng Hsu ◽  
Yung-Hui Li ◽  
Ching-Chun Chang ◽  
Latifa Nabila Harfiya
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Public Awareness ◽  
Absolute Error ◽  
Estimation Model ◽  
British Hypertension Society ◽  
Proposed Model

Due to the growing public awareness of cardiovascular disease (CVD), blood pressure (BP) estimation models have been developed based on physiological parameters extracted from both electrocardiograms (ECGs) and photoplethysmograms (PPGs). Still, in order to enhance the usability as well as reduce the sensor cost, researchers endeavor to establish a generalized BP estimation model using only PPG signals. In this paper, we propose a deep neural network model capable of extracting 32 features exclusively from PPG signals for BP estimation. The effectiveness and accuracy of our proposed model was evaluated by the root mean square error (RMSE), mean absolute error (MAE), the Association for the Advancement of Medical Instrumentation (AAMI) standard and the British Hypertension Society (BHS) standard. Experimental results showed that the RMSEs in systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 4.643 mmHg and 3.307 mmHg, respectively, across 9000 subjects, with 80.63% of absolute errors among estimated SBP records lower than 5 mmHg and 90.19% of absolute errors among estimated DBP records lower than 5 mmHg. We demonstrated that our proposed model has remarkably high accuracy on the largest BP database found in the literature, which shows its effectiveness compared to some prior works.

scholarly journals Dual - Stage Learning Approach Towards Continuous Cuffless Blood Pressure Monitoring

2020 ◽  
pp. 369-372
Author(s):  
Sree Niranjanaa Bose S
Keyword(s):  
Blood Pressure ◽  
Medical Records ◽  
Blood Pressure Monitoring ◽  
Pressure Monitoring ◽  
British Hypertension Society ◽  
Dual Stage ◽  
Blood Pressure Estimation ◽  
Cuffless Blood Pressure ◽  
The Given ◽  
Mimic Iii

Hypertension, being one of the associated factors of cardiovascular diseases needs to be monitored on the continuous manner to track the rapid BP changes. The paper proposes a dual-stage blood pressure estimation approach using the suitable features from Photoplethysmogram and machine learning models. The method initially classifies the given data among 4 classes given by British Hypertension Society (BHS). Further, the classified data is predicted from one of the four models. The 105 medical records consisting of clinical and digitalized signal data of 125 Hz are taken from the MIMIC-III database for the process. The dual-stage approach for the classification and estimation of BP outperforms the existing method by relative improvement in the MAE and RMSE by 64.4% and 36.37 % for systolic BP and 40.1% and 22.9% for diastolic BP respectively.

scholarly journals Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 11948-11956
Author(s):  
Siddharth Roheda ◽  
Hamid Krim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Action Recognition ◽  
Network Architecture ◽  
Video Sequence ◽  
Spatial Information ◽  
State Of The Art ◽  
Parallel Implementation ◽  
Filtering Approach

The importance of inference in Machine Learning (ML) has led to an explosive number of different proposals in ML, and particularly in Deep Learning. In an attempt to reduce the complexity of Convolutional Neural Networks, we propose a Volterra filter-inspired Network architecture. This architecture introduces controlled non-linearities in the form of interactions between the delayed input samples of data. We propose a cascaded implementation of Volterra Filtering so as to significantly reduce the number of parameters required to carry out the same classification task as that of a conventional Neural Network. We demonstrate an efficient parallel implementation of this Volterra Neural Network (VNN), along with its remarkable performance while retaining a relatively simpler and potentially more tractable structure. Furthermore, we show a rather sophisticated adaptation of this network to nonlinearly fuse the RGB (spatial) information and the Optical Flow (temporal) information of a video sequence for action recognition. The proposed approach is evaluated on UCF-101 and HMDB-51 datasets for action recognition, and is shown to outperform state of the art CNN approaches.

scholarly journals Cuff-Less Blood Pressure Estimation from Electrocardiogram and Photoplethysmography Based on VGG19-LSTM Network

2021 ◽  
Author(s):  
Yanan Pu ◽  
Xiaoxue Xie ◽  
Ling Xiong ◽  
Heng Zhang
Keyword(s):  
Blood Pressure ◽  
Standard Deviation ◽  
Average Error ◽  
Estimation Model ◽  
British Hypertension Society ◽  
Multi Stage ◽  
Pressure Estimation ◽  
Lstm Network ◽  
Signal Segment ◽  
Blood Pressure Estimation

In recent years, studies have found that the hierarchical neural network with LSTM network has higher accuracy than another feature engineering. Therefore, this paper first tries to build a multi-stage blood pressure estimation model through VGG19 and LSTM network. Based on the time node of the R wave peak in the QRS waveform in ECG, VGG19 is used to extract various higher-dimensional and rich life characteristics in the PPG signal segment by heartbeat as the unit and focus on processing the dynamics of SBP and DBP Correlation, finally use the LSTM model to extract the time dependence of the vital signs. Results: Experiments show that compared with similar multi-stage models, this model has higher accuracy. The performance of this method meets the Advancement of Medical Instrumentation (AAMI) standard and reaches the A level of the British Hypertension Society (BHS) standard. The average error and standard deviation of the estimated value of SBP were 1.7350 4.9606 mmHg, and the average error and standard deviation of the estimated value of DBP were 0.7839 2.7700 mmHg, respectively.

Cuffless blood pressure estimation based on composite neural network and graphics information

2021 ◽  
Vol 70 ◽  
pp. 103001
Author(s):  
Ye Qiu ◽  
Dongdong Liu ◽  
Guoyu Yang ◽  
Dezhen Qi ◽  
Yuer Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Pressure Estimation ◽  
Blood Pressure Estimation ◽  
Cuffless Blood Pressure

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

scholarly journals Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

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