Neural network-based electrocardiogram signal abnormality detection

An electrocardiogram (ECG) is a non-invasive diagnostic test that monitors the heart's electrical activity. It is a valuable instrument for detecting a variety of cardiac conditions. However, interpreting ECG data can be difficult, and diagnostic errors can have dire consequences. In recent years, machine learning techniques have been applied to ECG analysis to improve diagnosis and reduce error risk. In this endeavour, we propose an autoencoder neural network-based anomaly detection method for ECG analysis. Multiple layers of densely interconnected neurons comprise the encoder and decoder of the autoencoder. The encoder discovers a lowdimensional representation of the input data, whereas the decoder attempts to reconstruct the original data from the discovered representation. The autoencoder is taught a pattern of normal cardiac activity using normal ECG data. During testing, an ECG is deemed abnormal if the reconstruction error of the autoencoder exceeds a specified threshold. We evaluate the performance of the proposed method on a dataset of labelled ECG anomaly recordings. Our results indicate that the autoencoder method can detect anomalies in ECG data with an accuracy of 94%. In addition, we demonstrate that the method is robust to noise and generalizable to unobserved data. The proposed method demonstrates promising results for detecting anomalies in ECG data, which can aid in the diagnosis of cardiac conditions and reduce the risk of errors. Future research could involve further optimisation of the autoencoder architecture and investigation of the use of other machine learning techniques for ECG analysis.