A novel hybrid GRU-CNN and residual bias (RB) based RB-GRU-CNN models for prediction of PTB Diagnostic ECG time series data

Abstract

Time series analysis is crucial in the field of medical sciences. Recently, it has become crucial to comprehend the intricacies of data to forecast and assess the likelihood of survival in patients with different ailments. The study employs diverse datasets from multiple PTB electrocardiogram (ECG) datasets, which exhibit a high degree of uncorrelation. These datasets include patients with various heart diseases and age groups, ensuring dataset diversity. The paper presents two models that utilize deep learning and residual bias. The primary approach proposed is a hybrid GRU-CNN model. In contrast, the second model incorporates residual bias (RB) to improve upon the first model, referred to as the RB-GRU-CNN model. The hybrid GRU-CNN model has been compared to conventional deep learning models across multiple metrics, achieving the highest average percentage of improvement in RMSE (46.73%), MSE (67.46%), MAPE (43.92%), and MAE (48.52%) compared to LSTM. The RB-GRU-CNN proposed model demonstrated greater efficacy than the GRU-CNN proposed model, as confirmed by non-parametric statistical Friedman ranking and their corresponding Holm’s p-values. The comprehensive study of the results proves that the proposed models outperformed the performance of the traditional models.