scholarly journals Featureless Blood Pressure Estimation Based on Photoplethysmography Signal Using CNN and BiLSTM for IoT Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yung-Hui Li ◽  
Latifa Nabila Harfiya ◽  
Ching-Chun Chang
Keyword(s):  
Blood Pressure ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Feature Engineering ◽  
Short Term ◽  
Squared Error ◽  
Long Short Term Memory ◽  
Iot Devices ◽  
Blood Pressure Estimation

Continuous blood pressure (BP) acquisition is critical to health monitoring of an individual. Photoplethysmography (PPG) is one of the most popular technologies in the last decade used for measuring blood pressure noninvasively. Several approaches have been carried out in various ways to utilize features extracted from PPG. In this study, we develop a continuous systolic and diastolic blood pressure (SBP and DBP) estimation mechanism without the need for any feature engineering. The raw PPG signal only got preprocessed before being fed to our model which mainly consists of one-dimensional convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) network. We evaluate the resulting SBP and DBP value by the root-mean-squared error (RMSE) and mean absolute error (MAE). This study addresses the effectiveness of the model by outperforming the previous feature engineering-based methods. We achieve RMSE of 11.503 and 6.525 for SBP and DBP, respectively, and MAE of 7.849 and 4.418 for SBP and DBP, respectively. The proposed method is expected to substantially enhance the current efficiency of healthcare IoT (Internet of Things) devices in BP monitoring using PPG signals only.

Predicting Coordinated Actuated Traffic Signal Change Times using Long Short-Term Memory Neural Networks

2021 ◽  
pp. 036119812110007
Author(s):  
Seifeldeen Eteifa ◽  
Hesham A. Rakha ◽  
Hoda Eldardiry
Keyword(s):  
Loss Function ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Loss Functions ◽  
Short Term ◽  
Mean Relative Error ◽  
Squared Error ◽  
The Mean ◽  
Long Short Term Memory

Vehicle acceleration and deceleration maneuvers at traffic signals result in significant fuel and energy consumption levels. Green light optimal speed advisory systems require reliable estimates of signal switching times to improve vehicle energy/fuel efficiency. Obtaining these estimates is difficult for actuated signals where the length of each green indication changes to accommodate varying traffic conditions and pedestrian requests. This study details a four-step long short-term memory (LSTM) deep learning based methodology that can be used to provide reasonable switching time estimates from green to red and vice versa while being robust to missing data. The four steps are data gathering, data preparation, machine learning model tuning, and model testing and evaluation. The input to the models includes controller logic, signal timing parameters, time of day, traffic state from detectors, vehicle actuation data, and pedestrian actuation data. The methodology is applied and evaluated on data from an intersection in Northern Virginia. A comparative analysis is conducted between different loss functions including the mean squared error, mean absolute error, and mean relative error used in LSTM and a new loss function that is proposed in this paper. The results show that while the proposed loss function outperforms conventional loss functions in overall absolute error values, the choice of the loss function is dependent on the prediction horizon. Specifically, the proposed loss function is slightly outperformed by the mean relative error for very short prediction horizons (less than 20 s) and the mean squared error for very long prediction horizons (greater than 120 s).

scholarly journals Analysis of Lumber Prices Time Series Using Long Short-Term Memory Artificial Neural Networks

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 428
Author(s):  
Dercilio Junior Verly Lopes ◽  
Gabrielly dos Santos Bobadilha ◽  
Amanda Peres Vieira Bedette
Keyword(s):  
Stock Prices ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Short Term ◽  
Term Memory ◽  
Squared Error ◽  
Dense Output ◽  
The Usa ◽  
Long Short Term Memory

This manuscript confirms the feasibility of using a long short-term memory (LSTM) recurrent neural network (RNN) to forecast lumber stock prices during the great and Coronavirus disease 2019 (COVID-19) pandemic recessions in the USA. The database was composed of 5012 data entries divided into recession periods. We applied a timeseries cross-validation that divided the dataset into an 80:20 training/validation ratio. The network contained five LSTM layers with 50 units each followed by a dense output layer. We evaluated the performance of the network via mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) for 30, 60, and 120 timesteps and the recession periods. The metrics results indicated that the network was able to capture the trend for both recession periods with a remarkably low degree of error. Timeseries forecasting may help the forest and forest product industries to manage their inventory, transportation costs, and response readiness to critical economic events.

scholarly journals A Three-Stage Data-Driven Approach for Determining Reaction Wheels’ Remaining Useful Life Using Long Short-Term Memory

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2432
Author(s):  
Md Sirajul Islam ◽  
Afshin Rahimi
Keyword(s):  
System Parameter ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Remaining Useful Life ◽  
Health Index ◽  
Short Term ◽  
Term Memory ◽  
Squared Error ◽  
Useful Life ◽  
Long Short Term Memory

Reaction wheels are widely used in the attitude control system of small satellites. Unfortunately, reaction wheels failure restricts the efficacy of a satellite, and it is one of the many reasons leading to premature abandonment of the satellites. This study observes the measurable system parameter of a faulty reaction wheel induced with incipient fault to estimate the remaining useful life of the reaction wheels. We achieve this goal in three stages, as none of the observable system parameters are directly related to the health of a reaction wheel. In the first stage, we identify the necessary observable system parameter and predict the future of these parameters using sensor acquired data and a long short-term memory recurrent neural network. In the second stage, we estimate the health index parameter using a multivariate long short-term memory network. In the third stage, we predict the remaining useful life of reaction wheels based on historical data of the health index parameter. Normalized root mean squared error is used to evaluate the performance of the various models in each stage. Additionally, three different timespans (short, moderate, and extended in the scale of small satellite orbit times) are simulated and tested for the performance of the proposed methodology regarding the malfunction of reaction wheels. Furthermore, the robustness of the proposed method to missing values, input frequency, and noise is studied. The results show promising performance for the proposed scheme with accuracy in predicting health index parameter around 0.01–0.02 normalized root mean squared error, the accuracy in prediction of RUL of 1%–2.5%, and robustness to various uncertainty factors, as discussed above.

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

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.

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

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.

scholarly journals TIME SERIES FORECASTING FOR AVERAGE TEMPERATURE IN 96041 STATION USING LONG SHORT-TERM MEMORY MODEL

2021 ◽  
Vol 5 (1) ◽  
pp. 33-36
Author(s):  
Nancy Lusiana Damanik ◽  
◽  
Elida Pane ◽  
Kartika Dewi ◽  
Efrianses F. H. Sinaga ◽  
...  
Keyword(s):  
Long Memory ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Climate Information ◽  
Short Term ◽  
Average Temperature ◽  
Squared Error ◽  
Single Station ◽  
Long Short Term Memory

An understanding of patterns and gauge of normal temperature joined of parameter climate and climate information for way better water asset administration and arranging during a bowl is exceptionally vital. Investigate climate patterns utilizing typical and neighborhood annually normal temperatures, compare and make perceptions. during this consider, we'll analyze nearby and ordinary normal temperature information in 96041 Station supported perception station in place. the foremost objective of this considers to seem the execution of the traditional temperature in an exceedingly single station and to foresee the conventional temperature information utilizing the Long memory Demonstrate approach. supported the results of ordinary informatics of investigating temperature with nearby temperature relationship, we got the show of preparing bend, remaining plot, and therefore the diffuse plot is appeared utilizing these codes. the nice execution of 96041 Station had an Mean Squared Error esteem of 0.01 and R squared esteem 0.98, concerning zero will speak to superior quality of the indicator.

scholarly journals PREDIKSI DATA TRANSAKSI PENJUALAN TIME SERIES MENGGUNAKAN REGRESI LSTM

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Marie Luthfi Ashari ◽  
Mujiono Sadikin
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Long Short Term Memory

Sebagai upaya untuk memenangkan persaingan di pasar, perusahaan farmasi harus menghasilkan produk obat – obatan yang berkualitas. Untuk menghasilkan produk yang berkualitas, diperlukan perencanaan produksi yang baik dan efisien. Salah satu dasar perencanaan produksi adalah prediksi penjualan. PT. Metiska Farma telah menerapkan metode prediksi dalam proses produksi, akan tetapi prediksi yang dihasilkan tidak akurat sehingga menyebabkan tidak optimal dalam memenuhi permintaan pasar. Untuk meminimalisir masalah kurang akuratnya proses prediksi tersebut, dalam penelitian yang disajikan pada makalah ini dilakukan uji coba prediksi menggunakan teknik Machine Learning dengan metode Regresi Long Short Term Memory (LSTM). Teknik yang diusulkan diuji coba menggunakan dataset penjualan produk “X” dari PT. Metiska Farma dengan parameter kinerja Root Mean Squared Error (RMSE) dan MAPE (Mean Absolute Percentage Error). Hasil penelitian ini berupa nilai rata – rata evaluasi error dari pemodelan data training dan data testing. Di mana hasil menunjukan bahwa Regresi LSTM memiliki nilai prediksi penjualan dengan evaluasi model melalui RMSE sebesar 286.465.424 untuk data training dan 187.013.430 untuk data testing. Untuk nilai MAPE sebesar 787% dan 309% untuk data training dan data testing secara berurut.

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