Aiming at the deficiencies of insufficient cross-domain generalization and poor rhythm sensitivity in atrial fibrillation (AF) detection from multi-lead electrocardiogram (ECG) signals, this paper proposes a novel AF detection algorithm based on multi-scale patch attention fusion. The method segmented ECG signals into overlapping temporal fragments of different scales to capture local waveform details and long-range rhythm patterns respectively; it fused cross-scale feature information through a multi-scale attention mechanism to strengthen the model’s ability to perceive local and global rhythm features, and introduced the self-attention mechanism of Transformer to capture long-range rhythm correlations among fragments, thus realizing in-depth mining of ECG features. The algorithm was validated on the public CinC2021 dataset and the self-constructed clinical Clin-ECG dataset. Experimental results showed that the algorithm achieved an accuracy of 94.6% and 92.7% on the two datasets, with F1 scores reaching 0.945 and 0.923, respectively. Compared with baseline models such as ECG-ResNet and CNN-BiLSTM, the proposed algorithm exhibited higher accuracy and better cross-dataset generalization ability, providing an effective method for the automatic detection of AF from multi-lead ECG signals.
The objective is to deal with brain effective connectivity among epilepsy electroencephalogram (EEG) signals recorded by use of depth electrodes in the cerebral cortex of patients suffering from refractory epilepsy during their epileptic seizures. The Wiener-Granger Causality Index (WGCI) is a well-known effective measure that can be useful to detect causal relations of interdependence in these kinds of EEG signals. It is based on the linear autoregressive model, and the issue of the estimation of the model parameters plays an important role in the calculation accuracy and robustness of WGCI to do research on brain effective connectivity. Focusing on this issue, a modified Akaike’s information criterion algorithm is introduced in the computation of the WGCI to estimate the orders involved in the underlying models and in order to advance the performance of WGCI to detect brain effective connectivity. Experimental results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and a physiology-based model.
Accurate neurological outcome assessment after cardiac arrest is critical for clinical diagnosis and treatment. Existing electroencephalogram (EEG) prediction models suffer from high computational complexity, redundant full-channel data, and insufficient fusion of time-frequency domain features. This study proposes a lightweight deep learning model, WaveConv-SR-RepVGG, based on adaptive time-frequency feature fusion and reparameterized convolution. Taking the reparameterized visual convolutional network (RepVGG) as the baseline framework, the model introduces a shrinkage (SR) mechanism into the time-domain branch to suppress interference from low signal-to-noise ratio EEG signals. A multi-scale wavelet convolution (MS-Conv) module is constructed to extract multi-scale spectral features, and time-frequency features are fused to form joint representations for prognostic classification. Experiments were conducted on the PhysioNet 2023 dataset. Under the 18-channel setting, the model achieved an accuracy of 81.7%, an F1 score of 86.1%, and an AUC of 0.847. Under the 4-channel setting, the accuracy reached 81.2%, the F1 score reached 86.7%, and the AUC reached 0.877, with overall performance outperforming various mainstream deep learning algorithms. The proposed model enables neurological outcome prediction for patients after cardiac arrest and maintains stable and excellent classification performance with fewer EEG acquisition channels.