scholarly journals Indication of Mental Health from Fingertip Pulse Waves and Its Application

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Mayumi Oyama-Higa ◽  
Fumitake Ou
Keyword(s):  
Mental Health ◽  
Real Time ◽  
Time Series Data ◽  
Biological Information ◽  
Autonomic Nerve ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
Vascular Age ◽  
Pulse Waves ◽  
High Level

This article is a comprehensive review of recent studies of the authors on the indication of mental health from biological information contained in pulse waves. A series of studies discovered that the largest Lyapunov exponent (LLE) of the attractor, which is constructed for the time series data from pulse waves, can provide as an effective indicator of mental health. A low level of LLE indicates insufficiency of external adaptability, which is characteristic of dementia and depression sufferers. On the contrary, a continuous high level of LLE indicates excessive external adaptability, and people in this condition tend to resort to violence. With this discovery, real-time display of the LLE, combined with other physiological indexes such as the autonomic nerve balance (ANB), sample entropy, and vascular age, as a reference, can enable people to conduct self-check of mental status. To this end, software development was performed in order to enable users to conduct pulse wave measurement anywhere at any time and display the analytical results in real time during the measurement.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

scholarly journals Mental Health and Political Representation: A Roadmap

2021 ◽  
Vol 2 ◽  
Author(s):  
Luca Bernardi
Keyword(s):  
Mental Health ◽  
Mental Health Services ◽  
Health Services ◽  
Time Series Data ◽  
Political Representation ◽  
Negative Relationship ◽  
Series Data ◽  
Political Equality ◽  
Public Preferences ◽  
Policy Problems

Research on health and political behavior has identified a significant mental health-participation gap that is likely to have important consequences for political equality. Yet such consequences remain by and large unexplored. Inspired by 60 years of empirical research on public opinion, media and policy, this article proposes a roadmap for research on the political representation of mental health. It advances a number of research questions around 1) opinion formation and issue emergence and evolution, 2) multiple and complementary societal signals that can influence policy makers’ issue attention and policy change, and 3) different conceptions of representation and their consequences for public attitudes and political participation. The article also provides a preliminary attempt at addressing whether mental health spending incorporates signals from public preferences for spending on mental health services or policy problems. Making use of time-series data on spending on mental health services by local authorities in England between 1994 and 2013, the analysis finds no statistical association between spending and policy problems and reveals a negative relationship between spending and public preferences, suggesting that if spending is reacting at all to preferences, it misrepresents them. This article invites scholars to collect more data and produce more research that will guide interventions to help overcome stigma and participation challenges that undermine political equality as one of the key principles of democracy.

A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

2020 ◽  
Author(s):  
Meenakshi Narayan ◽  
Ann Majewicz Fey
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Real Time ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Force Sensor ◽  
Sensor Data ◽  
Series Data ◽  
Model Free ◽  
Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

scholarly journals Study on Fractal Multistep Forecast for the Prediction of Driving Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Longhai Yang ◽  
Hong Xu ◽  
Xiqiao Zhang ◽  
Shuai Li ◽  
Wenchao Ji
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Time Series Data ◽  
Back Propagation ◽  
Series Data ◽  
Vehicle Speed ◽  
Trajectory Data ◽  
Time Data ◽  
Characteristic Analysis ◽  
Real Time Data

The application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed and acceleration have fractal features by R/S analysis of the time series data of speed and acceleration. Based on the characteristic analysis of microscopic parameters, the characteristic indexes of parameters are quantified, the fractal multistep prediction model of microparameters is established, and the BP (back propagation neural networks) model is established to estimate predictable step of fractal prediction model. The fractal multistep prediction model is used to predict speed acceleration in the predictable step. NGSIM trajectory data are used to test the multistep prediction model. The results show that the proposed fractal multistep prediction model can effectively realize the multistep prediction of vehicle speed.

A hybrid fuzzy quantum time series and linear programming model: Special application on TAIEX index dataset

2019 ◽  
Vol 34 (25) ◽  
pp. 1950201 ◽  
Author(s):  
Pritpal Singh ◽  
Gaurav Dhiman ◽  
Sen Guo ◽  
Ritika Maini ◽  
Harsimran Kaur ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Programming ◽  
Real Time ◽  
Time Series Data ◽  
Programming Model ◽  
Series Data ◽  
Quantum Model ◽  
Quantum Time ◽  
Degree Of Membership ◽  
Quantum Approach

The supremacy of quantum approach is able to provide the solutions which are not practically feasible on classical machines. This paper introduces a novel quantum model for time series data which depends on the appropriate length of intervals. In this study, the effects of these drawbacks are elaborately illustrated, and some significant measures to remove them are suggested, such as use of degree of membership along with mid-value of the interval. All these improvements signify the effective results in case of quantum time series, which are verified and validated with real-time datasets.

A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data

2014 ◽  
Vol 140 ◽  
pp. 704-716 ◽  
Author(s):  
J.-F. Pekel ◽  
C. Vancutsem ◽  
L. Bastin ◽  
M. Clerici ◽  
E. Vanbogaert ◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Water Surface ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data ◽  
Surface Detection
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