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. 2023 Dec 17;23(24):9878.
doi: 10.3390/s23249878.

Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data

Ana Minic  1 , Luka Jovanovic  2 , Nebojsa Bacanin  2 , Catalin Stoean  3 , Miodrag Zivkovic  2 , Petar Spalevic  4 , Aleksandar Petrovic  2 , Milos Dobrojevic  2 , Ruxandra Stoean  3
Affiliations

Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data

Ana Minic et al. Sensors (Basel). .

Abstract

Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance.

Keywords: diagnosis; electrocardiogram; optimization; particle swarm optimization; recurrent neural networks.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Dataset visualization.
Figure 2
Figure 2
Experimental framework flowchart.
Figure 3
Figure 3
Objective and indicator function distributions.
Figure 4
Figure 4
Objective and indicator function convergence graphs.
Figure 5
Figure 5
Best-performing model’s ROC and PR curves.
Figure 6
Figure 6
Best-performing model’s confusion matrix.
Figure 7
Figure 7
Objective function outcome KDE plots for each metaheuristic.
Figure 8
Figure 8
SHAP analysis summary and feature impact outcomes.
See this image and copyright information in PMC

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