Real time cardiac image registration during respiration: a time series prediction approach

2011 ◽  
Vol 8 (2) ◽  
pp. 179-191 ◽  
Author(s):  
Mehdi Esteghamatian ◽  
Zohreh Azimifar ◽  
Perry Radau ◽  
Graham Wright
Keyword(s):  
Time Series ◽  
Image Registration ◽  
Real Time ◽  
Time Series Prediction ◽  
Cardiac Image ◽  
Prediction Approach

scholarly journals A Time Series Prediction Approach Based on Hybrid Tuning for Database Performance Indicator in AIOps

2020 ◽  
Vol 38 (5) ◽  
pp. 1030-1037
Author(s):  
Xiaoling Wang ◽  
Xiaofang Zhang ◽  
Ning Li ◽  
Chengfeng Han ◽  
Zhuping Yuan ◽  
...  
Keyword(s):  
Time Series ◽  
Model Building ◽  
Performance Indicator ◽  
Time Series Prediction ◽  
Arima Model ◽  
Parameter Tuning ◽  
Trend Prediction ◽  
Cloud Database ◽  
Variation Data ◽  
Prediction Approach

One of the most important applications of the intelligent operation and maintenance of a cloud database is its trend prediction of key performance indicators (KPI), such as disk use, memory use, etc. We propose a method named AutoPA4DB (Auto Prophet and ARIMA for Database) to predict the trend of the KPIs of the cloud database based on the Prophet model and the ARIMA model. Our AutoPA4DB method includes data preprocessing, model building, parameter tuning and optimization. We employ the weighted MAPE coverage to measure its accuracy and use 6 industrial datasets including 10 KPIs to compare the AutoPA4DB method with other three time-series trend prediction algorithms. The experimental results show that our AutoPA4DB method performs best in predicting monotonic variation data, e.g.disk use trend prediction. But it is unstable in predicting oscillatory variation data; for example, it is acceptable in memory use trend prediction but has poor accuracy in predicting the number of database connection trends.

scholarly journals A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Anh-Duc Pham ◽  
Minh-Tu Cao
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Hybrid Approach ◽  
Series Data ◽  
Support Vector ◽  
Promising Alternative ◽  
Prediction Approach

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction(FLSVRTSP). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR’s tuning parameters, theFLSVRTSPincorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that theFLSVRTSPhas achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction.

scholarly journals Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6593
Author(s):  
Ahmed Youssef Ali Amer ◽  
Femke Wouters ◽  
Julie Vranken ◽  
Dianne de Korte-de Boer ◽  
Valérie Smit-Fun ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Real Time ◽  
Early Warning ◽  
Continuous Monitoring ◽  
Time Series Prediction ◽  
Vital Signs ◽  
Wearable Technology ◽  
High Rate ◽  
Hospitalised Patients

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.

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