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Related Concept Videos

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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Pulse rhythm01:30

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Drug Concentration Versus Time Correlation

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Related Experiment Video

Updated: Jun 18, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Piecewise-linear trend detection in longitudinal physiological measurements.

Stephen J Redmond1, Jim Basilakis, Yang Xie

  • 1Graduate School of Biomedical Engineering, University of New South Wales, NSW 2052, Sydney, Australia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary

This study introduces a new method for analyzing patient health data trends, improving remote chronic disease monitoring. The approach accurately identifies subtle condition changes, aiding clinicians in early detection and intervention.

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Last Updated: Jun 18, 2026

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Published on: August 5, 2020

Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Medical Data Analysis

Background:

  • Telecare solutions enable remote chronic disease monitoring.
  • Managing numerous remote patients necessitates automated decision support systems (DSS).
  • Existing threshold-based alerts miss subtle, incipient changes in patient condition.

Purpose of the Study:

  • To develop an automated approach for analyzing longitudinal physiological data trends.
  • To create a piecewise-linear fitting technique comparable to human analysis.
  • To implement this technique via a graphical user interface for clinical use.

Main Methods:

  • Developed a piecewise-linear fitting algorithm for longitudinal physiological data.
  • Integrated the algorithm into a graphical user interface.
  • Validated the method using both simulated and real patient data.
  • Compared algorithm performance against human expert scoring.

Main Results:

  • The algorithm matched or exceeded human performance in fitting simulated data.
  • Performance improvement was most significant with noisier simulated data.
  • For real physiological data, the deviation from human marking was less than 0.35 fractional variability.

Conclusions:

  • The developed piecewise-linear fitting method effectively analyzes physiological trends for remote patient monitoring.
  • This automated approach can assist clinicians in detecting early signs of chronic disease deterioration.
  • The technique shows promise for enhancing decision support systems in telecare.