Диагностика сигналов ЭКГ на основе цифровой обработки и оптимизированной модели трансформера

Abstract

Heartbeat disorders are considered one of the main maladies that cause mortality. Therefore, their precocious diagnosis via ECG signal is critical for introducing prompt therapy. The advanced automatic classification of ECG signals has the potential to save cardiologists a tremendous amount of time while simultaneously decreasing the chance of misdiagnosis. The dilemma of massive parameters is troubling the current methods of ECG signal classification. Most recent methods exhibit inadequate performance for diagnosing ECG signals in the inter-patient mode. In an attempt to deal with the above limitations, this study offers an innovative, efficient, and end-to-end model. The suggested model uses the optimized transformer framework to classify the heartbeats according to the "Association for the Advancement of Medical Instrumentation, AAMI," and obeys the inter-patient setting. We constructed an efficient architecture called the optimized network to substitute the Self Attention Unit (SAU) in the encoder part of the transformer model. The suggested model, which includes an optimized network, outperforms the SAU-based transformer model and requires fewer computations. A robust embedding architecture based on a Convolutional Neural Network (CNN) with a Squeeze and Excitation (SE) network-based attention scheme that has been used for weighting the Local Heartbeat Shape Pattern (LHSP) features is presented. The introduced model exceeds the state-of-the-art. An extensive test has been done to compare the achievements of the suggested model with those of the cardiologists. The results proved the closeness of their performances.