Abstract
Most existing Electrocardiogram (ECG) classification methods assume that all arrhythmia classes are known during the training phase. In this paper, the problem of learning several successive tasks is addressed, where, in each new task, there are new arrhythmia classes to learn. Unfortunately, in machine learning it is known that when a model is retrained onto a new task, the machine tends to forget the old task. This is known in machine learning, as 'the catastrophic forgetting phenomenon'. To this end, a learn-without-forgetting (LwF) approach to solve this problem is proposed. This novel deep LwF method for ECG heartbeat classification is the first work of its kind in the field. This proposed LwF approach consists of a deep learning architecture that includes the following important aspects: feature extraction module, classification layers for each learned task, memory module to store one prototype for each task, and a task selection module able to identify the most suitable task for each input sample. The feature extraction module constitutes another contribution of this work. It starts with a set of deep layers that convert an ECG heartbeat signal into an image, then the pre-trained DenseNet169 CNN takes the obtained image and extracts rich and powerful features that are effective inputs for the classifications layers of the model. Whenever a new task is to be learned, the network expands with a new classification layer having a Softmax activation function. The newly added layer is responsible for learning the classes of the new task. When the network is trained for the new task, the shared layers, as well as the output layers of the old tasks, are also fine-tuned using pseudo labels. This helps in retaining knowledge of old tasks. Finally, the task selector stores feature prototypes for each task, and using a distance matching network, is trained to select which task is more suitable to classify a new test sample. The whole network uses end-to-end learning to optimize one loss functions, which is a weighted combination of the loss functions of the different network modules. The proposed model was tested on three common ECG datasets, namely the MIT-BIH, INCART, and SVDB datasets. The results obtained demonstrate the success of the proposed method in learning, without forgetting, successive ECG heartbeat classification tasks.