Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques

Abstract

In cardiac rhythm disorders, atrial fibrillation (AF) is among the most deadly. So, ECG signals play a crucial role in preventing CVD by promptly detecting atrial fibrillation in a patient. Unfortunately, locating trustworthy automatic AF in clinical settings remains difficult. Today, deep learning is a potent tool for complex data analysis since it requires little pre and postprocessing. As a result, several machine learning and deep learning approaches have recently been applied to ECG data to diagnose AF automatically. This study analyses electrocardiogram (ECG) data from the PhysioNet/Computing in Cardiology (CinC) Challenge 2017 to differentiate between atrial fibrillation (AF) and three other rhythms: normal, other, and too noisy for assessment. The ECG data, including AF rhythm, was classified using a novel model based on a combination of traditional machine learning techniques and deep neural networks. To categorize AF rhythms from ECG data, this hybrid model combined a convolutional neural network (Residual Network (ResNet)) with a Bidirectional Long Short Term Memory (BLSTM) network and a Radial Basis Function (RBF) neural network. Both the F1-score and the accuracy of the final hybrid model are relatively high, coming in at 0.80% and 0.85%, respectively.

Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification

“…First, we defined the performance metrics as given in previous studies [23][24][25][26][42][43][44][45]. Precision, the percentage of labels that were correctly predicted is represented by the model precision score.…”

“…These cross-subject problems with large and complex data can be handled in a much better way by employing deep learning techniques [10,21]. The studies reported in [22][23][24][25][26] quantified EEG features to recognize neurological deteriorations according to the task because of stroke and estimate the biomarkers to differentiate between healthy adults and ischemic stroke patients.…”