A deformable CNN architecture for predicting clinical acceptability of ECG signal

The degraded quality of the electrocardiogram (ECG) signals is the main source of false alarms in critical care units. Therefore, a preliminary analysis of the ECG signal is required to decide its clinical acceptability. In conventional techniques, different handcrafted features are extracted from the ECG signal based on signal quality indices (SQIs) to predict clinical acceptability. A one-dimensional deformable convolutional neural network (1D-DCNN) is proposed in this work to extract features automatically, without manual interference, to detect the clinical acceptability of ECG signals efficiently. In order to create DCNN, the deformable convolution and pooling layers are merged into the regular convolutional neural network (CNN) architecture. In DCNN, the equidistant sampling locations of a regular CNN are replaced with adaptive sampling locations, which improves the network’s ability to learn based on the input. Deformable convolution layers concentrate more on significant segments of the ECG signals rather than giving equal attention to all segments. The proposed method is able to detect acceptable and unacceptable ECG signals with an accuracy of 99.50%, recall of 99.78%, specificity of 99.60%, precision of 99.47%, and F-score of 0.999. Experimental results show that the proposed method performs better than earlier state-of-the-art techniques.