This study introduces a Hybrid Deep Learning (HDL) model called DenseNet-ABiLSTM, which uses densely connected convolutional networks and Attention-based Bidirectional Long Short-Term Memory (ABiLSTM) to categorize various types of arrhythmias. The model uses 1D convolutional kernels to acquire multi...
In our DenseNet-BiLSTM, the DenseNet is primarily applied to obtain local features, while the BiLSTM is used to grab time series features. In general, the DenseNet is used in computer vision tasks, and it may corrupt contextual information for speech audios. In order to make DenseNet ...
Firstly, the original signals are processed using wide-kernel convolution and DenseNet to suppress noise while extracting detailed features, thereby enhancing the flow of information between layers of the model. Then, BiLSTM is employed to learn long-term dependencies and explore temporal sequence ...
Therefore, a Densenet-121 is employed in this work to extract high-level and deep characteristics from the images. Afterwards, the training images are retrieved from the dataset and compared to the query image using a Bidirectional LSTM (BiLSTM) classifier to obtain the relevant ...