Due to the complexity of myocardial infarction (MI) waveform, most traditional automatic diagnosis models rarely detect it, while those able to detect MI often require high computing and storage capacity, rendering them unsuitable for portable devices. Therefore, in order for convenient real-time MI detection, it is essential to design lightweight models suitable for resource-limited portable devices. This paper proposes a novel multi-channel lightweight model (ML-Net), that provides a new solution for portable detection devices with limited resources. In ML-Net, each electrocardiogram (ECG) lead is assigned an independent channel, ensuring data independence and preserve the ECG characteristics of different angles represented by different leads. Moreover, convolution kernels of heterogeneous sizes are utilized to achieve accurate classification with only a small amount of lead data. Extensive experiments over actual ECG data from the PTB diagnostic database are conducted to evaluate ML-Net. The results show that ML-Net outperforms comparable schemes in diagnosing MI, and it requires lower computational cost and less memory, so that portable devices can be more widely used in the field of Internet of Medical Things(IoMT).

Body movement is one of the most important indicators of sleep quality for elderly people living alone. Body movement is crucial for sleep staging and can be combined with other indicators such as breathing and heart rate to monitor sleep quality. Nevertheless, traditional sleep monitoring methods are inconvenient and may invade users’ privacy. To solve these problems, we propose contactless Body Movement Recognition (CBMR) method via WiFi signals. Firstly, CBMR uses commercial off-the-shelf WiFi devices to collect Channel State Information (CSI) data of body movement and segment the CSI data by sliding window. Then, the context information of the segmented CSI data is learned by a Bi-directional Recurrent Neural Network (Bi-RNN). Bi-RNN can fuse the forward and backward propogation information at some point, and input it into a deeper Independently Recurrent Neural Network (IndRNN) with residual mechanism to extract the deeper features and capture the time dependencies of CSI data. Finally, the type of body movement can be recognized and classified by the softmax function. CBMR can effectively reduce data preprocessing and the delay caused by manually extracting features. The results of an experiment conducted on a complex body movement dataset show that our method gives desirable performance and achieves average accuracy of greater than 93.5%, which implies a prospect application of CBMR.

Electrocardiogram (ECG) waveform delineation is fundamental to cardiac disease diagnosis. This task requires precise localization of key fiducial points, specifically the onset, peak, and offset positions of P-waves, QRS complexes, and T-waves. Current methods exhibit significant performance degradation in noisy clinical environments (baseline drift, electromyographic interference, powerline interference, etc.), compromising diagnostic reliability. To address this limitation, we introduce ECG-SCFNet: a novel dual-stream architecture employing selective context fusion. Our framework is further enhanced by a consistency training paradigm, enabling it to maintain robust waveform delineation accuracy under challenging noise conditions.The network employs a dual-stream architecture: (1) A temporal stream captures dynamic rhythmic features through sequential multi-branch convolution and temporal attention mechanisms; (2) A morphology stream combines parallel multi-scale convolution with feature pyramid integration to extract multi-scale waveform structural features through morphological attention; (3) The Selective Context Fusion (SCF) module adaptively integrates features from the temporal and morphology streams using a dual attention mechanism, which operates across both channel and spatial dimensions to selectively emphasize informative features from each stream, thereby enhancing the representation learning for accurate ECG segmentation. On the LUDB and QT datasets, ECG-SCFNet achieves high performance, with F1-scores of 97.83% and 97.80%, respectively. Crucially, it maintains robust performance under challenging noise conditions on these datasets, with 88.49% and 86.25% F1-scores, showing significantly improved noise robustness compared to other methods and demonstrating exceptional robustness and precise boundary localization for clinical ECG analysis.

Medical report generation is a rising application of big data, which mitigates burden on doctors in clinical trail. However, the serious imbalanced distribution of diseases leads to data bias, an important issue in qualified medical reports generation. To address this issue, a sequence-to-sequence model with incorporation of clinical experience is proposed to generate Chinese ElectroCardioGraph (ECG) reports. The proposed model consists of Electrocardiograph Feature Extractor (EFE), Posterior Knowledge Embedding (PKE) and Report Generator (RG). Firstly, we introduce a novel spatial-temporal information fusion module in EFE to extract robust features from ECG data. Then, embeddings of ECG tags extracted from clinical ECG reports combined with output of EFE are then feed into PKE, which builds a bridge between ECG tags and ECG data, alleviating the problem caused by data bias. Finally, a transformer-based decoder is used in RG to generate ECG reports step by step with output of PKE as key and value. Experiments conducted on private data show that the proposed model can obtain an accuracy 50.26% on BLEU-4, 7.69% higher than state-of-the-art. Our method can also achieve better fluent reports, as demonstrated by the performance on CIDEr, a commonly used content metric.

Super-Resolution (SR) is a fundamental computer vision task, which reconstructs high-resolution images from low-resolution ones. Existing SR methods mainly recover images from clear low-resolution images, leading to unsatisfactory results when processing compressed low-resolution images. In the paper, we propose a two-stage SR method for compressed images, which consists of the Compression Artifact Removal Module (CARM) and Super-Resolution Module (SRM). The compressed low-resolution image is used to reconstruct the clear low-resolution image by CARM, and the high-resolution image is obtained by SRM. In addition, we propose a residual information distillation block to learn the texture details which are lost during the compression process. The proposed method has been validated and compared with the state of the art, and experimental results show that the proposed method outperforms other super-resolution methods in terms of visual effects and objective evaluation metrics.