Detection of anomalies in physiological signals using artificial neural network

Early detection of anomalous physiological signals is crucial for making a diagnosis quickly and for taking appropriate medical action. Traditional approaches to signal processing often struggle with noise, variability, and limited labeled data. The use of artificial neural networks (ANNs) for anomaly detection in a variety of physiological signals, such as blood glucose, photoplethysmograms, electrocardiograms, electroencephalograms, electromyograms, and electrodermal activity, is investigated in the present work. It examines ANN-based methods, including feedforward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders for the detection of abnormal patterns in physiological data. An autoencoder model was trained on typical physiological signals in a related investigation, and anomalies were detected by analyzing reconstruction errors. This approach mimics real-world scenarios where labeled anomalies are sparse, enhancing the ability to identify abnormalities without relying on predefined categories. The performance of the model is evaluated using key metrics, demonstrating its effectiveness in distinguishing between normal and abnormal physiological patterns. The findings highlight the potential of neural network-based techniques in developing automated, intelligent diagnostic systems for improved healthcare monitoring and early disease detection.