A Novel Blood Pressure Estimation Method with the Combination of Long Short Term Memory Neural Network and Principal Component Analysis Based on PPG Signals

The interaction between the gird and wind farms has significant impact on the power grid, therefore prediction of the interaction between gird and wind farms is of great significance. In this paper, a wind turbine-gird interaction prediction model based on long short term memory (LSTM) network under the TensorFlow framework is presented. First, the multivariate time series was screened by principal component analysis (PCA) to reduce the data dimensionality. Secondly, the LSTM network is used to model the nonlinear relationship between the selected sequence of wind turbine network interactions and the actual output sequence of the wind farms, it is proved that it has higher accuracy and applicability by comparison with single LSTM model, Autoregressive Integrated Moving Average (ARIMA) model and Back Propagation Neural Network (BPNN) model, the Mean Absolute Percentage Error (MAPE) is 0.617%, 0.703%, 1.397% and 3.127%, respectively. Finally, the Prony algorithm was used to analyze the predicted data of the wind turbine-grid interactions. Based on the actual data, it is found that the oscillation frequencies of the predicted data from PCA-LSTM model are basically the same as the oscillation frequencies of the actual data, thus the feasibility of the model proposed for analyzing interaction between grid and wind turbines is verified.

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Stock Price Prediction Based on Principal Component Analysis and Long Short-Term Memory Network

Advances in Applied Mathematics ◽

10.12677/aam.2020.911225 ◽

2020 ◽

Vol 09 (11) ◽

pp. 1954-1960

Author(s):

甲刘

Keyword(s):

Principal Component Analysis ◽

Stock Price ◽

Short Term Memory ◽

Principal Component ◽

Short Term ◽

Term Memory ◽

Stock Price Prediction ◽

Price Prediction ◽

Memory Network ◽

Long Short Term Memory

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Remaining Useful Life Prediction of Airplane Engine Based on PCA–BLSTM

Sensors ◽

10.3390/s20164537 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4537

Author(s):

Shixin Ji ◽

Xuehao Han ◽

Yichun Hou ◽

Yong Song ◽

Qingfu Du

Keyword(s):

Principal Component Analysis ◽

Hybrid Model ◽

Short Term Memory ◽

Principal Component ◽

Remaining Useful Life ◽

Short Term ◽

Term Memory ◽

Airplane Engine ◽

Useful Life ◽

Long Short Term Memory

The accurate prediction of airplane engine failure can provide a reasonable decision basis for airplane engine maintenance, effectively reducing maintenance costs and reducing the incidence of failure. According to the characteristics of the monitoring data of airplane engine sensors, this work proposed a remaining useful life (RUL) prediction model based on principal component analysis and bidirectional long short-term memory. Principal component analysis is used for feature extraction to remove useless information and noise. After this, bidirectional long short-term memory is used to learn the relationship between the state monitoring data and remaining useful life. This work includes data preprocessing, the construction of a hybrid model, the use of the NASA’s Commercial Aerodynamic System Simulation (C-MAPSS) data set for training and testing, and the comparison of results with those of support vector regression, long short-term memory and bidirectional long short-term memory models. The hybrid model shows better prediction accuracy and performance, which can provide a basis for formulating a reasonable airplane engine health management plan.

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Artificial Neural Networks in Motion Analysis—Applications of Unsupervised and Heuristic Feature Selection Techniques

Sensors ◽

10.3390/s20164581 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4581

Author(s):

Marion Mundt ◽

Arnd Koeppe ◽

Franz Bamer ◽

Sina David ◽

Bernd Markert

Keyword(s):

Neural Networks ◽

Principal Component Analysis ◽

Motion Analysis ◽

Lower Limb ◽

Short Term Memory ◽

Principal Component ◽

Joint Angles ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural networks have the opportunity to estimate joint angles from a sparse dataset, which enables the reduction of sensors necessary for the determination of all three-dimensional lower limb joint angles. Additionally, the dimensions of the problem can be simplified using principal component analysis. Training a long short-term memory neural network on the prediction of 3D lower limb joint angles based on inertial data showed that three sensors placed on the pelvis and both shanks are sufficient. The application of principal component analysis to the data of five sensors did not reveal improved results. The use of longer motion sequences compared to time-normalised gait cycles seems to be advantageous for the prediction accuracy, which bridges the gap to real-time applications of long short-term memory neural networks in the future.

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Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation

Applied Sciences ◽

10.3390/app112412019 ◽

2021 ◽

Vol 11 (24) ◽

pp. 12019

Author(s):

Chia-Chun Chuang ◽

Chien-Ching Lee ◽

Chia-Hong Yeng ◽

Edmund-Cheung So ◽

Yeou-Jiunn Chen

Keyword(s):

Blood Pressure ◽

Neural Networks ◽

Short Term Memory ◽

Attention Mechanism ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Blood Pressure Estimation ◽

Fully Connected ◽

Electrocardiogram Ecg

Monitoring people’s blood pressure can effectively prevent blood pressure-related diseases. Therefore, providing a convenient and comfortable approach can effectively help patients in monitoring blood pressure. In this study, an attention mechanism-based convolutional long short-term memory (LSTM) neural network is proposed to easily estimate blood pressure. To easily and comfortably estimate blood pressure, electrocardiogram (ECG) and photoplethysmography (PPG) signals are acquired. To precisely represent the characteristics of ECG and PPG signals, the signals in the time and frequency domain are selected as the inputs of the proposed NN structure. To automatically extract the features, the convolutional neural networks (CNNs) are adopted as the first part of neural networks. To identify the meaningful features, the attention mechanism is used in the second part of neural networks. To model the characteristic of time series, the long short-term memory (LSTM) is adopted in the third part of neural networks. To integrate the information of previous neural networks, the fully connected networks are used to estimate blood pressure. The experimental results show that the proposed approach outperforms CNN and CNN-LSTM and complies with the Association for the Advancement of Medical Instrumentation standard.

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Blood Pressure Estimation from Photoplethysmography with Motion Artifacts Using Long Short Term Memory Network

Journal of Biomimetics Biomaterials and Biomedical Engineering ◽

10.4028/www.scientific.net/jbbbe.54.31 ◽

2022 ◽

Vol 54 ◽

pp. 31-39

Author(s):

Anuradhi Welhenge ◽

Attaphongse Taparugssanagorn

Keyword(s):

Blood Pressure ◽

Elderly Population ◽

Short Term Memory ◽

Continuous Measurement ◽

Motion Artifacts ◽

Short Term ◽

Term Memory ◽

Memory Network ◽

Long Short Term Memory ◽

Blood Pressure Estimation

Continuous measurement of the Blood Pressure (BP) is important in hypertensive patientsand elderly population. Traditional cuff based methods are difficult to use since it is uncomfortable towear a cuff throughout the day. A more suitable method is to estimate the BP using the Photoplethysmography(PPG) signal. However, it is difficult to estimate a BP when the PPG is corrupted withMotion Artifacts (MAs). In this paper, Long Short Term Memory (LSTM) an extension of RecurrentNeural Networks (RNN) is used used to improve the accuracy of the estimation of the BP from thecorrupted PPG. It shows that an accuracy of 97.86 is achieved.

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