Have a personal or library account? Click to login
Reconceiving the Edge Intelligence Based IoT Devices for an effective Classification of ECG Systems Cover

Reconceiving the Edge Intelligence Based IoT Devices for an effective Classification of ECG Systems

Open Access
|Feb 2025

Figures & Tables

Figure 1:

Proposed Architecture
Proposed Architecture

Figure 2:

LSTM Structure
LSTM Structure

Figure 3:

Comparative Assessment with Varying Learning Data Percentages based on (a) testing accuracy, (b) sensitivity, (c) specificity (d) Precision (e) F1-Score
Comparative Assessment with Varying Learning Data Percentages based on (a) testing accuracy, (b) sensitivity, (c) specificity (d) Precision (e) F1-Score

Comparative Analysis between the Different Models in ECG signal classification

AlgorithmEvaluation Metrics (%)
AccuracyPrecisionrecallSpecificityF1-score
GRU91.3%90.4%90.2%89.2%89.3%
RF92.5%92.34%92.48%92.35%92.6%
DT93.4%93.7%93.1%93.2%93.0%
SVM PROPOSED89.0%89.9%89.78%89.68%90%
MODEL99%99%98.96%99.1%99%

Evaluation Metrices

SL.NOEvaluation MetricsMathematical Expression
01AccuracyTP+TNTP+TN+FP+FN
02RecallTPTP+FN×100
03SpecificityTNTN+FP
04PrecisionTNTP+FP
05F1-Score2.Precison*RecallPrecision+Recall
Language: English
Page range: 79 - 92
Submitted on: Aug 29, 2024
Accepted on: Oct 2, 2024
Published on: Feb 24, 2025
Published by: Future Sciences For Digital Publishing
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2025 Sangamesh H, Ramesh Cheripelli, Nijaguna G S, published by Future Sciences For Digital Publishing
This work is licensed under the Creative Commons Attribution 4.0 License.