Prediction of QT interval in ECGs through baseline wander removal and deep learning technique

Abstract Background In electrocardiograms (ECGs), the QT interval, calculated from the onset of the Q-wave to the end of the T-wave, reflects the repolarization status of the heart. Currently, the QT interval automatically measured in 12-lead electrocardiograms is inaccurately derived as the median value, rather than utilizing the more precise mean value of QT intervals from all leads. This approach fails to provide information on heterogeneous QT prolongation crucial for predicting arrhythmias, as it does not accurately represent the values of QT intervals in each lead. Purpose Traditional analysis of ECGs based on QT intervals necessitates direct interpretation by cardiologists, leading to potential errors in diagnosis. In response to this challenge, we present a model that integrates low-pass filtering and continuous wavelet transformation to eliminate baseline wander noise occurring during ECG measurements. Moreover, to account for temporal features, we incorporate Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) to estimate QT intervals. Methods The dataset used in this study was the QT Database, comprising ECG data for 105 patients recorded at a sampling rate of 250Hz over a duration of 15 minutes. Each ECG was associated with annotations for two leads and information regarding the P, QRS, and T waves. We structured the dataset into a ratio of approximately 8:1:1, resulting in 51 patients for training (15,490 samples), 6 patients for validation (1,564 samples), and 7 patients for testing (1,682 samples). Results For the evaluation of deep learning, we selected Accuracy and F1-Score as metrics. We experimented with two configurations: the proposed model with two Bi-LSTM layers and an alternative with a single Bi-LSTM layer of 100 units. We also tested a similar recurrent neural network, GRU, with the same unit configuration. The results indicated that the method with two Bi-LSTM layers outperformed others in both Accuracy and F1-Score. Conclusion By incorporating CNN and Bi-LSTM, we conducted QT interval estimation, leveraging temporal features. This methodology facilitated the elimination of diverse noise types during ECG measurements, resulting in enhanced accuracy for QT interval estimation through the consideration of temporal ECG characteristics. In our future research endeavors, we plan to deploy this model for predicting arrhythmias, specifically targeting conditions like long QT syndrome and drug-induced QT prolongation.Methods and result