Innovative deep learning and signal processing methods for ECG abnormality detection and diagnosis

With the advancement of medical technology, electrocardiogram (ECG) monitoring plays a crucial role in the diagnosis of heart diseases. However, traditional ECG analysis methods rely on the subjective judgment of professional doctors, which has certain limitations. This paper proposes a technology for the detection and diagnosis of ECG abnormalities based on deep learning and signal processing, aiming to enhance the automation and accuracy of ECG signal analysis. Initially, this paper employs wavelet transforms for preprocessing of ECG signals, effectively removing noise while preserving key ECG features. Subsequently, Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory networks (LSTM), are utilized for time-series analysis of ECG signals to differentiate between normal and abnormal ECG signals. Furthermore, a Convolutional Neural Network (CNN) is constructed to extract features from abnormal ECG signals for the identification of specific types of abnormalities. To augment the model's feature expression capability, this paper also introduces an attention mechanism, which enhances the weight of key signal segments, thereby improving the accuracy of abnormality detection. Experimental results demonstrate that this method achieves a high accuracy rate in the task of ECG abnormality detection, effectively assisting doctors in the diagnosis of electrocardiograms. This research not only has potential application value in intelligent monitoring systems and data mining technology but also provides a new technological approach for the automated diagnosis of electrocardiograms, with prospects for playing a significant role in the fields of industrial Internet of Things and intelligent manufacturing.