Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data

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.

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

“…For example, recurrent neural networks (RNNs) are known for their excellent performance in forecasting time series. These networks were applied with considerable success to identify Parkinson's disease [28,29] and analyze electrocardiograms [30][31][32] and electroencephalography data [33][34][35]. AI methods were also examined for respiratory condition classifications, like identifying asthma [36], pneumonia [37], tuberculosis [38,39] and lung tumors [40].…”

Computer-Vision Unmanned Aerial Vehicle Detection System Using YOLOv8 Architectures

“…Moreover, hybrid methods between machine/deep learning and metaheuristics excel in other application domains as well, as evidenced by numerous successful recent applications including medicine [22,13,8,27,32,6,24], agriculture [25], environmental monitoring [5,20], economy [13,41,38] and power grids [29,14,3,39,45]. Other notable applications include weather forecasting [21], cloud computing [7,33,4,9], wireless sensor networks [46,11,44] and intrusion detection [35,36,23,15].…”