Compression of EEG signals with the LSTM-autoencoder via domain adaptation approach

The successful implementation of neural network-based EEG signal compression has led to significant cost reductions in data transmission. However, a major obstacle in this process arises from the decline in performance when compressing EEG signals from multiple subjects. This challenge arises due to the notable feature shift of EEG signals between subjects, which poses an impediment to the neural network's efficient concurrent acquisition of information from multiple subjects. To address this limitation and enable more effective utilization of data for improving the performance on target domain, we propose a Domain Adaptation (DA) framework based on LSTM-autoencoder. Our experiments encompassed the following: (1) A comparison between LSTM-autoencoder, GRU-autoencoder, and the commonly used convolutional autoencoder (CAE) in EEG compression. (2) A comparison between our proposed DA method and the MMD-based DA method, as well as Fine-tuning transfer learning. The results demonstrate the following: (1) LSTM-autoencoder outperforms other models in both subject-specific and cross-subject scenarios. (2) Using transfer learning improves the performance of LSTM-autoencoder on the target subject. (3) Our proposed method outperforms maximum mean discrepancy (MMD)-based domain adaptation and fine-tuning approaches, resulting in a more significant enhancement.

Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification

The electrical activity produced during the heartbeat is measured and recorded by an ECG. Cardiologists can interpret the ECG machine's signals and determine the heart's health condition and related causes of ECG signal abnormalities. However, cardiologist shortage is a challenge in both developing and developed countries. Moreover, the experience of a cardiologist matters in the accurate interpretation of the ECG signal, as the interpretation of ECG is quite tricky even for experienced doctors. Therefore, developing computer-aided ECG interpretation is required for its wide-reaching effect. 12-lead ECG generates a 1D signal with 12 channels among the well-known time-series data. Classical machine learning can develop automatic detection, but deep learning is more effective in the classification task. 1D-CNN is being widely used for CVDS detection from ECG datasets. However, adopting a deep learning model designed for computer vision can be problematic because of its massive parameters and the need for many samples to train. In many detection tasks ranging from semantic segmentation of medical images to time-series data classification, multireceptive field CNN has improved performance. Notably, the nature of the ECG dataset made performance improvement possible by using a multireceptive field CNN (MRF-CNN). Using MRF-CNN, it is possible to design a model that considers semantic context information within ECG signals with different sizes. As a result, this study has designed a multireceptive field CNN architecture for ECG classification. The proposed multireceptive field CNN architecture can improve the performance of ECG signal classification. We have achieved a 0.72 F 1 score and 0.93 AUC for 5 superclasses, a 0.46 F 1 score and 0.92 AUC for 20 subclasses, and a 0.31 F 1 score and 0.92 AUC for all the diagnostic classes of the PTB-XL dataset.