scholarly journals Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 319 ◽  
Author(s):  
Evangelos Kafantaris ◽  
Ian Piper ◽  
Tsz-Yan Milly Lo ◽  
Javier Escudero
Keyword(s):  
Time Series ◽  
Wearable Devices ◽  
Physiological Monitoring ◽  
Disruptive Effect ◽  
Respiratory Impedance ◽  
Common Occurrence ◽  
Original Algorithm ◽  
Physiological Signal ◽  
Monitoring Applications ◽  
Improved Performance

Entropy quantification algorithms are becoming a prominent tool for the physiological monitoring of individuals through the effective measurement of irregularity in biological signals. However, to ensure their effective adaptation in monitoring applications, the performance of these algorithms needs to be robust when analysing time-series containing missing and outlier samples, which are common occurrence in physiological monitoring setups such as wearable devices and intensive care units. This paper focuses on augmenting Dispersion Entropy (DisEn) by introducing novel variations of the algorithm for improved performance in such applications. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate interval, electroencephalogram, and respiratory impedance time-series. Our results indicate that the algorithmic variations of DisEn achieve considerable improvements in performance while our analysis signifies that, in consensus with previous research, outlier samples can have a major impact in the performance of entropy quantification algorithms. Consequently, the presented variations can aid the implementation of DisEn to physiological monitoring applications through the mitigation of the disruptive effect of missing and outlier samples.

Based on FPGA Intelligent Physiological Parameters Acquisition

2013 ◽  
Vol 344 ◽  
pp. 107-110
Author(s):  
Shun Ren Hu ◽  
Ya Chen Gan ◽  
Ming Bao ◽  
Jing Wei Wang
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Physiological Parameters ◽  
Sensor Data ◽  
Low Power Consumption ◽  
Physiological Signal ◽  
Pulse Sensor ◽  
Monitoring Applications ◽  
Field Programmable ◽  
Design Ideas

For the physiological signal monitoring applications, as a micro-controller based on field programmable gate array (FPGA) physiological parameters intelligent acquisition system is given, which has the advantages of low cost, high speed, low power consumption. FPGA is responsible for the completion of pulse sensor, the temperature sensor, acceleration sensor data acquisition and serial output and so on. Focuses on the design ideas and architecture of the various subsystems of the whole system, gives the internal FPGA circuit diagram of the entire system. The whole system is easy to implement and has a very good promotional value.

A Frequency-Domain Filtering Technique for Triple Decomposition of Unsteady Turbulent Flow

1992 ◽  
Vol 114 (1) ◽  
pp. 45-51 ◽  
Author(s):  
G. J. Brereton ◽  
A. Kodal
Keyword(s):  
Time Series ◽  
Turbulent Flow ◽  
Frequency Domain ◽  
Measurement Data ◽  
Averaging Technique ◽  
Phase Averaging ◽  
Spectral Estimates ◽  
Improved Performance ◽  
A New Technique ◽  
Flow Variables

A new technique is presented for decomposing unsteady turbulent flow variables into their organized unsteady and turbulent components, which appears to offer some significant advantages over existing ones. The technique uses power-spectral estimates of data to deduce the optimal frequency-domain filter for determining the organized and turbulent components of a time series of data. When contrasted with the phase-averaging technique, this method can be thought of as replacing the assumption that the organized motion is identically reproduced in successive cycles of known periodicity by a more general condition: the cross-correlation of the organized and turbulent components is minimized for a time series of measurement data, given the expected shape of the turbulence power spectrum. The method is significantly more general than the phase average in its applicability and makes more efficient use of available data. Performance evaluations for time series of unsteady turbulent velocity measurements attest to the accuracy of the technique and illustrate the improved performance of this method over the phase-averaging technique when cycle-to-cycle variations in organized motion are present.

Predicting in multivariate incomplete time series. Application of the expectation-maximisation algorithm supplemented by the Newton-Raphson method

2021 ◽  
Vol 68 (1) ◽  
pp. 17-46
Author(s):  
Adam Korczyński
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Em Algorithm ◽  
Measurement Errors ◽  
Parameters Estimation ◽  
Maximum Likelihood Estimates ◽  
Original Algorithm ◽  
Expectation Maximisation ◽  
Newton Raphson ◽  
Raphson Method

Statistical practice requires various imperfections resulting from the nature of data to be addressed. Data containing different types of measurement errors and irregularities, such as missing observations, have to be modelled. The study presented in the paper concerns the application of the expectation-maximisation (EM) algorithm to calculate maximum likelihood estimates, using an autoregressive model as an example. The model allows describing a process observed only through measurements with certain level of precision and through more than one data series. The studied series are affected by a measurement error and interrupted in some time periods, which causes the information for parameters estimation and later for prediction to be less precise. The presented technique aims to compensate for missing data in time series. The missing data appear in the form of breaks in the source of the signal. The adjustment has been performed by the EM algorithm to a hybrid version, supplemented by the Newton-Raphson method. This technique allows the estimation of more complex models. The formulation of the substantive model of an autoregressive process affected by noise is outlined, as well as the adjustment introduced to overcome the issue of missing data. The extended version of the algorithm has been verified using sampled data from a model serving as an example for the examined process. The verification demonstrated that the joint EM and Newton-Raphson algorithms converged with a relatively small number of iterations and resulted in the restoration of the information lost due to missing data, providing more accurate predictions than the original algorithm. The study also features an example of the application of the supplemented algorithm to some empirical data (in the calculation of a forecasted demand for newspapers).

Phase Transitions and Resilience in Physical and Psychological Health

2020 ◽  
pp. 59-72
Author(s):  
Marcel G. M. Olde Rikkert ◽  
Noemi Schuurman ◽  
René J. F. Melis
Keyword(s):  
Time Series ◽  
Psychological Health ◽  
Wearable Devices ◽  
Complexity Science ◽  
Detailed Knowledge ◽  
Tipping Points ◽  
Mental Processes ◽  
Slowing Down ◽  
Physical And Psychological Health ◽  
Aging Societies

Complexity science methods offer new opportunities for prognosis and treatment in healthcare and clinical psychology because of the increasing need for integration of the detailed knowledge of physiological and psychological subsystems and the increasing prevalence of multiple disease conditions in our aging societies. This chapter explains how the frequently occurring acute transitions and related tipping points in physical and mental processes in these populations can be monitored with time series and dynamical indicators of resilience. The authors introduce slowing down of recovery, increase in variance and autocorrelation, and increasing cross-correlation between subsystem time series as valid predictors of the proximity of tipping points in diseases such as depression, heart failure and syncope. Using wearable devices, together with these complex systems analyses, yields new methods of forecasting and may improve resilience of individual persons and their mental or physical (organ) subsystems

scholarly journals A Robust Beat-to-Beat Artifact Detection Algorithm for Pulse Wave

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Qihan Hu ◽  
Xintao Deng ◽  
Xin Liu ◽  
Aiguo Wang ◽  
Cuiwei Yang
Keyword(s):  
Pulse Wave ◽  
Smart Cities ◽  
Detection Algorithm ◽  
Wearable Devices ◽  
Segmentation Algorithm ◽  
Artifact Detection ◽  
Physiological Conditions ◽  
Physiological Signal ◽  
Pulse Waves ◽  
Complex Algorithm

With the rise of the concept of smart cities and healthcare, artificial intelligence helps people pay increasing attention to the health of themselves. People can wear a variety of wearable devices to monitor their physiological conditions. The pulse wave is a kind of physiological signal which is widely applied in the physiological monitoring system. However, the pulse wave is susceptible to artifacts, which prevents its popularization. In this work, we propose a novel beat-to-beat artifact detection algorithm, which performs pulse wave segmentation based on wavelet transform and then detects artifacts beat by beat based on the decision list. We verified our method on data acquired from different databases and compared with experts’ annotations. The segmentation algorithm achieved an accuracy of 96.13%. When it is applied to detect main peaks, the performance achieved an accuracy of 99.11%. After the previous segmentation algorithm, the artifact detection algorithm can detect beat-to-beat pulse waves and artifacts with an accuracy of 98.11%. The result indicated that the proposed method is robust for pulse waves of different patterns and could effectively detect the artifact without the complex algorithm. In summary, our proposed algorithm is capable of annotating pulse waves of various patterns and determining pulse wave quality. Since our method is developed and evaluated on the transmission-mode PPG data, it is more suitable for the devices and applications inside the hospitals instead of reflectance-mode PPG.

Fluctuations and determinism of respiratory impedance in asthma and chronic obstructive pulmonary disease

2010 ◽  
Vol 109 (6) ◽  
pp. 1582-1591 ◽  
Author(s):  
Michael Muskulus ◽  
Annelies M. Slats ◽  
Peter J. Sterk ◽  
Sjoerd Verduyn-Lunel
Keyword(s):  
Time Series ◽  
Power Law ◽  
Respiratory System ◽  
Single Frequency ◽  
Fluctuation Analysis ◽  
High Temporal Resolution ◽  
Deterministic System ◽  
Forced Oscillation Technique ◽  
Respiratory Impedance ◽  
Wasserstein Distances

Asthma and COPD are chronic respiratory diseases that fluctuate widely with regard to clinical symptoms and airway obstruction, complicating treatment and prediction of exacerbations. Time series of respiratory impedance obtained by the forced oscillation technique are a convenient tool to study the respiratory system with high temporal resolution. In previous studies it was suggested that power-law-like fluctuations exist also in the healthy lung and that respiratory system impedance variability differs in asthma. In this study we elucidate such differences in a population of well-characterized subjects with asthma ( n = 13, GINA 1+2), COPD ( n = 12, GOLD I+II), and controls ( n = 10) from time series at single frequency (12 min, f = 8 Hz). Maximum likelihood estimation did not rule out power-law behavior, accepting the null hypothesis in 17/35 cases ( P > 0.05) and with significant differences in exponents for COPD ( P < 0.03). Detrended fluctuation analysis exhibited scaling exponents close to 0.5, indicating few correlations, with no differences between groups ( P > 0.14). In a second approach, we considered asthma and COPD as dynamic diseases, corresponding to changes of unknown parameters in a deterministic system. The similarity in shape between the combined probability distributions of normalized resistance and reactance was quantified by Wasserstein distances and reliably distinguished the two diseases (cross-validated predictive accuracy 0.80; sensitivity 0.83, specificity 0.77 for COPD). Wasserstein distances between 3+3 dimensional phase space reconstructions resulted in marginally better classification (accuracy 0.84, sensitivity 0.83, specificity 0.85). These latter findings suggest that the dynamics of respiratory impedance contain valuable information for the diagnosis and monitoring of patients with asthma and COPD, whereas the value of the stochastic approach is not clear presently.

scholarly journals Research on Non-Contact Monitoring System for Human Physiological Signal and Body Movement

Biosensors ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 58 ◽  
Author(s):  
Qiancheng Liang ◽  
Lisheng Xu ◽  
Nan Bao ◽  
Lin Qi ◽  
Jingjing Shi ◽  
...  
Keyword(s):  
Monitoring System ◽  
Doppler Radar ◽  
Body Movement ◽  
Radar System ◽  
The Body ◽  
Body Motion ◽  
Physiological Monitoring ◽  
Physiological Signal ◽  
Signal Monitoring ◽  
Monitoring Devices

With the rapid increase in the development of miniaturized sensors and embedded devices for vital signs monitoring, personal physiological signal monitoring devices are becoming popular. However, physiological monitoring devices which are worn on the body normally affect the daily activities of people. This problem can be avoided by using a non-contact measuring device like the Doppler radar system, which is more convenient, is private compared to video monitoring, infrared monitoring and other non-contact methods. Additionally real-time physiological monitoring with the Doppler radar system can also obtain signal changes caused by motion changes. As a result, the Doppler radar system not only obtains the information of respiratory and cardiac signals, but also obtains information about body movement. The relevant RF technology could eliminate some interference from body motion with a small amplitude. However, the motion recognition method can also be used to classify related body motion signals. In this paper, a vital sign and body movement monitoring system worked at 2.4 GHz was proposed. It can measure various physiological signs of the human body in a non-contact manner. The accuracy of the non-contact physiological signal monitoring system was analyzed. First, the working distance of the system was tested. Then, the algorithm of mining collective motion signal was classified, and the accuracy was 88%, which could be further improved in the system. In addition, the mean absolute error values of heart rate and respiratory rate were 0.8 beats/min and 3.5 beats/min, respectively, and the reliability of the system was verified by comparing the respiratory waveforms with the contact equipment at different distances.

scholarly journals Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4271 ◽  
Author(s):  
Frédéric Li ◽  
Kimiaki Shirahama ◽  
Muhammad Adeel Nisar ◽  
Xinyu Huang ◽  
Marcin Grzegorzek
Keyword(s):  
Time Series ◽  
Ubiquitous Computing ◽  
Transfer Learning ◽  
Time Series Data ◽  
Wearable Devices ◽  
Series Data ◽  
Target Field ◽  
Proper Training ◽  
Application Fields ◽  
General Architecture

The scarcity of labelled time-series data can hinder a proper training of deep learning models. This is especially relevant for the growing field of ubiquitous computing, where data coming from wearable devices have to be analysed using pattern recognition techniques to provide meaningful applications. To address this problem, we propose a transfer learning method based on attributing sensor modality labels to a large amount of time-series data collected from various application fields. Using these data, our method firstly trains a Deep Neural Network (DNN) that can learn general characteristics of time-series data, then transfers it to another DNN designed to solve a specific target problem. In addition, we propose a general architecture that can adapt the transferred DNN regardless of the sensors used in the target field making our approach in particular suitable for multichannel data. We test our method for two ubiquitous computing problems—Human Activity Recognition (HAR) and Emotion Recognition (ER)—and compare it a baseline training the DNN without using transfer learning. For HAR, we also introduce a new dataset, Cognitive Village-MSBand (CogAge), which contains data for 61 atomic activities acquired from three wearable devices (smartphone, smartwatch, and smartglasses). Our results show that our transfer learning approach outperforms the baseline for both HAR and ER.

Improving Disadvantaged Families' Educational Skills: Changes in Children and Caregivers

1994 ◽  
Vol 75 (1) ◽  
pp. 701-702
Author(s):  
Cody L. Traver ◽  
Vikki F. Howard ◽  
T. F. McLaughlin
Keyword(s):  
At Risk ◽  
Time Series ◽  
Social Behaviors ◽  
University Student ◽  
Fifth Grade ◽  
Grade Child ◽  
Series Design ◽  
Time Series Design ◽  
Improved Performance ◽  
Disadvantaged Families

The purpose of this study was to evaluate the effects for three families, each with a fifth-grade child at risk, of training in skills such as communicating with teachers and administrators, providing a quiet place for their child to work, and praising their child's appropriate changes in academic and social behaviors. The caregivers were trained by a university student in these skills and evaluated in an AB time series design. All three families showed increased involvement in the school. Two of the three children improved performance both at school and at home.

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