A Fuzzy-Operated Convolutional Autoencoder for Classification of Wearable Device-Collected Electrocardiogram

Utilizing electrocardiogram (ECG) to assess patients' cardiac health is currently the popular diagnostic method. This approach demonstrates high accuracy and helps alleviate the workload of medical professionals. However, the ECG signals consist of physiological signals with low-frequency and low-amplitude features, including various interference noises. Traditional feature extraction methods of classification models have proven ineffective in handling this data type. Moreover, previous classification models fail to capture the spatial and local structures within ECG data, resulting in learned features that do not include the key features of ECG signals. Additionally, traditional models often adopt neural network as prediction modules, which are likewise inefficient in handling noise within the data. To end these issues, we propose a fuzzy-operated convolutional autoencoder (FOCAE) for the classification of ECG collected from wearable devices. The FOCAE model integrates convolutional autoencoder and fuzzy neural network, enabling it to learn high-quality features from noisy data to ensure the model's inferential capability. Furthermore, the FOCAE model inputs the learned features into the fuzzy neural network and fully connected network to obtain the classification results of ECG. Using fuzzy rules in the fuzzy neural network to describe the mapping between features and classification results enhances the robustness of the predictive outcomes. Some experimental results on a real ECG dataset validate the superior performance of the FOCAE model. The performance of the FOCAE model is superior to other three baseline models. Specifically, the Precision, Accuracy, and F1-Score of the FOCAE model are 0.971, 0.968 and 0.965, respectively.