ECG Measurement Uncertainty Based on Monte Carlo Approach: An Effective Analysis for a Successful Cardiac Health Monitoring System
Abstract
:1. Introduction
- Contribute to filling a small gap in the state of the art of evaluating the uncertainty of measurements performed with an ECG;
- Present a methodology capable of identifying opportunities for improvement in measurement system projects, using measurement uncertainty as a parameter.
2. Materials and Methods
2.1. Datasets
2.2. Methods
Algorithm 1 MCM implementation. |
//Initialize M (number of iterations) // The array A is declared //Assigns random number with proper PDF ⋮ //The array Y is declared //Function g defines the mathematical model //The array B is declared with n lines constants to n |
3. Results
3.1. Formulation of the Model
3.2. PDF Assignment
3.3. Validation and Comparisons with the Literature
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Requirement Description | Min/Max | Units | Value |
---|---|---|---|
Operating conditions: | |||
Line voltage | Range | V RMS | 104 to 1127 |
Frequency | Range | Hz | |
Temperature | Range | C | |
Relative humidity | Range | % | |
Atmospheric pressure | Range | kPa | 70 to 106 |
Input Dynamic Range: | |||
Range of linear operations of input signal | Min | mV | |
Allowed variation of amplitude with dc offset | Max | % | |
Gain control, accuracy, and stability: | |||
Gain error | Max | % | 5 |
Gain change rate/min | Max | %/min | |
Total gain change/h | Max | % | |
Time base selection and accuracy: | |||
Time base error | Max | % | |
Output display: | |||
Error of rulings | Max | % | |
Time marker error | Max | % | |
Accuracy of input signal reproduction: | |||
Overall error for signals | Max | % | |
Error in lead weighting factors | Max | % | 5 |
Hysteresis after 15 mm deflection from baseline | Max | mm | |
Standardizing voltage: | |||
Amplitude error | Max | % | |
System noise: | |||
Multichannel crosstalk | Max | % | 2 |
Baseline stability: | |||
Baseline drift rate RTI | Max | V/s | 10 |
Total baseline drift RTI (2 min period) | Max | V | 500 |
Quantity | Parameters | |||||
---|---|---|---|---|---|---|
a | b | Unit | ||||
Measurand: | ||||||
mV | ||||||
mV | ||||||
Baseline | mV | |||||
Measuring system: | ||||||
k | ||||||
k | ||||||
k | ||||||
k | ||||||
k | ||||||
M | ||||||
k | ||||||
F | ||||||
nF | ||||||
Environment: | ||||||
Noise | mV |
Source of Uncertainty | Vout (mV) | |||
---|---|---|---|---|
Measurand: | ||||
2596 | 44 | 87 | ||
Baseline | 2596 | 8 | 15 | |
Measuring system: | ||||
Preamplifier | 2596 | 27 | 54 | |
Final stage | 2596 | 20 | 39 | |
Environment: | ||||
Noise | 2596 | 8 | 15 |
Source of Uncertainty | Vout (mV) | |||
---|---|---|---|---|
Measurand: | ||||
2596 | 45 | 87 | ||
Baseline | 2596 | 8 | 15 | |
Measuring system: | ||||
Preamplifier | 2596 | 3 | 5 | |
Final stage | 2596 | 2 | 4 | |
Environment: | ||||
Noise | 2596 | 8 | 15 |
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Share and Cite
Silva, J.H.B.d.; Cortez, P.C.; Jagatheesaperumal, S.K.; de Albuquerque, V.H.C. ECG Measurement Uncertainty Based on Monte Carlo Approach: An Effective Analysis for a Successful Cardiac Health Monitoring System. Bioengineering 2023, 10, 115. https://doi.org/10.3390/bioengineering10010115
Silva JHBd, Cortez PC, Jagatheesaperumal SK, de Albuquerque VHC. ECG Measurement Uncertainty Based on Monte Carlo Approach: An Effective Analysis for a Successful Cardiac Health Monitoring System. Bioengineering. 2023; 10(1):115. https://doi.org/10.3390/bioengineering10010115
Chicago/Turabian StyleSilva, Jackson Henrique Braga da, Paulo Cesar Cortez, Senthil K. Jagatheesaperumal, and Victor Hugo C. de Albuquerque. 2023. "ECG Measurement Uncertainty Based on Monte Carlo Approach: An Effective Analysis for a Successful Cardiac Health Monitoring System" Bioengineering 10, no. 1: 115. https://doi.org/10.3390/bioengineering10010115
APA StyleSilva, J. H. B. d., Cortez, P. C., Jagatheesaperumal, S. K., & de Albuquerque, V. H. C. (2023). ECG Measurement Uncertainty Based on Monte Carlo Approach: An Effective Analysis for a Successful Cardiac Health Monitoring System. Bioengineering, 10(1), 115. https://doi.org/10.3390/bioengineering10010115