scholarly journals Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?

2020 ◽  
pp. 659-673
Author(s):  
Vishnu Unnikrishnan ◽  
Yash Shah ◽  
Miro Schleicher ◽  
Mirela Strandzheva ◽  
Plamen Dimitrov ◽  
...  
Keyword(s):  
Time Series ◽  
Pilot Study ◽  
Predictive Model ◽  
Health Condition ◽  
Diabetic Patients ◽  
User Engagement ◽  
Series Length ◽  
Short Time ◽  
Mhealth Apps ◽  
Over Time

Abstract Some mHealth apps record user activity continuously and unobtrusively, while other apps rely by nature on user engagement and self-discipline: users are asked to enter data that cannot be assessed otherwise, e.g., on how they feel and what non-measurable symptoms they have. Over time, this leads to substantial differences in the length of the time series of recordings for the different users. In this study, we propose two algorithms for wellbeing-prediction from such time series, and we compare their performance on the users of a pilot study on diabetic patients - with time series length varying between 8 and 87 recordings. Our first approach learns a model from the few users, on which many recordings are available, and applies this model to predict the 2nd, 3rd, and so forth recording of users newly joining the mHealth platform. Our second approach rather exploits the similarity among the first few recordings of newly arriving users. Our results for the first approach indicate that the target variable for users who use the app for long are not predictive for users who use the app only for a short time. Our results for the second approach indicate that few initial recordings suffice to inform the predictive model and improve performance considerably.

scholarly journals Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 385 ◽  
Author(s):  
David Cuesta-Frau ◽  
Juan Pablo Murillo-Escobar ◽  
Diana Alexandra Orrego ◽  
Edilson Delgado-Trejos
Keyword(s):  
Time Series ◽  
Classification Performance ◽  
Permutation Entropy ◽  
Series Length ◽  
Stability Point ◽  
Long Time ◽  
Model Time Series ◽  
The Stability ◽  
Forbidden Patterns ◽  
Short Time

Permutation Entropy (PE) is a time series complexity measure commonly used in a variety of contexts, with medicine being the prime example. In its general form, it requires three input parameters for its calculation: time series length N, embedded dimension m, and embedded delay τ . Inappropriate choices of these parameters may potentially lead to incorrect interpretations. However, there are no specific guidelines for an optimal selection of N, m, or τ , only general recommendations such as N > > m ! , τ = 1 , or m = 3 , … , 7 . This paper deals specifically with the study of the practical implications of N > > m ! , since long time series are often not available, or non-stationary, and other preliminary results suggest that low N values do not necessarily invalidate PE usefulness. Our study analyses the PE variation as a function of the series length N and embedded dimension m in the context of a diverse experimental set, both synthetic (random, spikes, or logistic model time series) and real–world (climatology, seismic, financial, or biomedical time series), and the classification performance achieved with varying N and m. The results seem to indicate that shorter lengths than those suggested by N > > m ! are sufficient for a stable PE calculation, and even very short time series can be robustly classified based on PE measurements before the stability point is reached. This may be due to the fact that there are forbidden patterns in chaotic time series, not all the patterns are equally informative, and differences among classes are already apparent at very short lengths.

Development and Pilot Study of an Integrated Sensor System to Measure Fuel Consumption and Cookstove Use

2018 ◽  
Author(s):  
Jennifer Ventrella ◽  
Nordica MacCarty
Keyword(s):  
Pilot Study ◽  
Fuel Consumption ◽  
Monitoring And Evaluation ◽  
User Engagement ◽  
State University ◽  
Integrated Sensor ◽  
Technical Performance ◽  
Sensor Platform ◽  
Monitoring Technologies ◽  
Over Time

Accurate, accessible methods for monitoring and evaluation of improved cookstoves are necessary to optimize designs, quantify impacts, and ensure programmatic success. Despite recent advances in cookstove monitoring technologies, there are no existing devices that autonomously measure fuel use in a household over time and this important metric continues to rely on in-person visits to conduct measurements by hand. To address this need, researchers at Oregon State University and Waltech Systems have developed the Fuel, Usage, and Emissions Logger (FUEL), an integrated sensor platform that quantifies fuel consumption and cookstove use by monitoring the mass of the household’s fuel supply with a load cell and the cookstove body temperature with a thermocouple. Following a proof-of-concept study of five prototypes in Honduras, a pilot study of one hundred prototypes was conducted in the Apac District of northern Uganda for one month. The results were used to evaluate user engagement with the system, verify technical performance, and develop algorithms to quantify fuel consumption and stove usage over time. Due to external hardware malfunctions, 31% of the deployed FUEL sensors did not record data. However, results from the remaining 69% of sensors indicated that 82% of households used the sensor consistently for a cumulative 2188 days. Preliminary results report an average daily fuel consumption of 6.3 ± 1.9 kg across households. Detailed analysis algorithms are still under development. With higher quality external hardware, it is expected that FUEL will perform as anticipated, providing long-term, quantitative data on cookstove adoption, fuel consumption, and emissions.

scholarly journals Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants

2021 ◽  
Author(s):  
Airton Monte Serrat Borin ◽  
Anne Humeau-Heurtier ◽  
Luiz Otavio Murta ◽  
Luiz Eduardo Virgilio Silva
Keyword(s):  
Time Series ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Short Term ◽  
Series Length ◽  
Short Time Series ◽  
Fuzzy Algorithms ◽  
Computing Performance ◽  
Similar Accuracy ◽  
Short Time

Abstract Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series. This is why previous studies applied different kinds of algorithm derivations to short-term time series. However, no study has systematically analyzed and compared their reliabilities. This study compares the MSE algorithm variations adapted to short time series on both human and rat heart rate variability (HRV) time series. The most used variations of MSE are studied: composite MSE (CMSE), refined composite MSE (RCMSE), modified MSE (MMSE), and their fuzzy versions. We also analyze the errors in MSE estimations for a range of incorporated fuzzy exponents. The results show that fuzzy MSE versions-as a function of time series length-present minimal errors compared to the non-fuzzy algorithms. The traditional multiscale entropy algorithm with fuzzy counting (MFE) has similar accuracy to alternative algorithms with better computing performance. For the best accuracy, the findings suggest different fuzzy exponents according to the time series length.

scholarly journals Detection of Dynamical Regime Transitions with Lacunarity as a Multiscale Recurrence Quantification Measure

2020 ◽  
Author(s):  
Tobias Braun ◽  
Norbert Marwan ◽  
Vishnu R. Unni ◽  
Raman I. Sujith ◽  
Juergen Kurths
Keyword(s):  
Time Series ◽  
Recurrence Plot ◽  
Dynamical Regime ◽  
Series Length ◽  
Dynamical Complexity ◽  
Recurrence Quantification ◽  
The Rich ◽  
Short Time ◽  
Recurrent Patterns ◽  
Regime Transitions

<p>We propose Lacunarity as a novel recurrence quantification measure and apply it in the context of dynamical regime transitions. Many complex real-world systems exhibit abrupt regime shifts. We carry out a recurrence plot based analysis for different paradigmatic systems and thermoacoustic combustion time series in order to demonstrate the ability of our method to detect dynamical transitions on variable temporal scales. Lacunarity is usually interpreted as a measure of ‘gappiness’ of an arbitrary spatial pattern. In application to recurrence plots, it quantifies the degree of heterogenity in the temporal recurrent patterns. Our method succeeds to distinguish states of varying dynamical complexity in presence of noise and short time series length. In contrast to traditional recurrence quantifiers, no specification of minimal line lengths is required and features beyond the scope of line structures can be accounted for. Applied to acoustic pressure fluctuation time series, it captures both the rich variability in dynamical complexity and detects shifts of characteristic time scales.</p>

scholarly journals Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants Compared to Full-Length Long Signals

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1620
Author(s):  
Airton Borin ◽  
Anne Humeau-Heurtier ◽  
Luiz Virgílio Silva ◽  
Luiz Murta
Keyword(s):  
Time Series ◽  
Multiscale Entropy ◽  
Short Term ◽  
Series Length ◽  
Short Time Series ◽  
Fuzzy Algorithms ◽  
Computing Performance ◽  
Similar Accuracy ◽  
Short Time

Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series. This is why previous studies applied different kinds of algorithm derivations to short-term time series. However, no study has systematically analyzed and compared their reliabilities. This study compares the MSE algorithm variations adapted to short time series on both human and rat heart rate variability (HRV) time series using long-term MSE as reference. The most used variations of MSE are studied: composite MSE (CMSE), refined composite MSE (RCMSE), modified MSE (MMSE), and their fuzzy versions. We also analyze the errors in MSE estimations for a range of incorporated fuzzy exponents. The results show that fuzzy MSE versions—as a function of time series length—present minimal errors compared to the non-fuzzy algorithms. The traditional multiscale entropy algorithm with fuzzy counting (MFE) has similar accuracy to alternative algorithms with better computing performance. For the best accuracy, the findings suggest different fuzzy exponents according to the time series length.

SCALE INVARIANCE IN SELECTIN-MEDIATED LEUKOCYTE ROLLING

Fractals ◽  
2004 ◽  
Vol 12 (02) ◽  
pp. 235-241 ◽  
Author(s):  
MICHAEL R. KING
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Hurst Exponent ◽  
White Blood Cells ◽  
Spatial Arrangement ◽  
Range Analysis ◽  
Rescaled Range Analysis ◽  
Long Time ◽  
Short Time ◽  
Over Time

White blood cells slowly roll along the walls of blood vessels, due to the coordinated formation and breakage of chemical selectin-carbohydrate bonds. Using detailed computer simulations of cells rolling on a selectin surface under flow, we show the time series of the cell translational velocity to be fractal in nature over time scales ranging from 22–211 ms. A rescaled range analysis was performed to determine the Hurst exponent of the velocity time series, for simulations of cells rolling on either a uniform or punctate distribution of P-selectin molecules. The rolling behavior was found to exhibit two very distinct regimes, with a negative Hurst exponent ranging from -(1.2-0.6) over time scales of 23-27 ms, and a positive Hurst exponent of +0.47±0.03 over time scales of 27-211 ms. The short-time Hurst exponent was found to be a strong function of the molecular distribution and also a function of average molecular density, while the long-time Hurst exponent was unchanged over all conditions studied. The implication is that the short-time adhesive behavior of cells interacting with a reactive surface is sensitive to the spatial arrangement of molecules, and the total number of molecules on the surface.

Kategorisierung der Schädelform des Cavalier King Charles Spaniels

2019 ◽  
Vol 47 (02) ◽  
pp. 133-133
Keyword(s):  
Pilot Study ◽  
Cavalier King Charles Spaniel ◽  
Conformation Changes ◽  
Veterinary Record ◽  
Changes Over Time ◽  
Over Time

Knowler SP, Gillstedt L, Mitchell TJ et al. Pilot study of head conformation changes over time in the Cavalier King Charles spaniel breed. Veterinary Record 2019. doi:10.1136/vr.105135.

627-P: Efficacy and Safety of Allogeneic Platelet Rich Plasma in Treatment of Diabetic Patients with Lower Limb Ulcers: A Pilot Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 627-P
Author(s):  
WUQUAN DENG ◽  
MIN HE ◽  
BING CHEN ◽  
YU MA ◽  
DAVID ARMSTRONG ◽  
...  
Keyword(s):  
Pilot Study ◽  
Lower Limb ◽  
Platelet Rich Plasma ◽  
Diabetic Patients ◽  
Efficacy And Safety

scholarly journals Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Tie Liang ◽  
Qingyu Zhang ◽  
Xiaoguang Liu ◽  
Bin Dong ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Gamma Band ◽  
Motor Dysfunction ◽  
Beta Band ◽  
Short Time Series ◽  
Information Interaction ◽  
Stroke Group ◽  
Short Time ◽  
Maximal Information Coefficient

Abstract Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

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