Enhancing the Development and Growth of Infant Cerebral Palsy Rats Using Selective Spinal Manipulations.

Cerebral palsy (CP) is a refractory pediatric disease with a high prevalence, high disability rate, and difficult treatment. A variety of treatments are currently used for CP. The treatment involves drug and non-drug therapy. Traditional Chinese medicine external therapy is a very distinctive treatment method in non-drug therapy. As one of the external therapies of traditional Chinese medicine, massage is used in treating cerebral palsy and has good efficacy, small side effects, and strong operability. As a part of TCM external therapy, selective spinal manipulation can effectively promote the growth and development of infant rats with cerebral palsy.The operation was mainly divided into four steps: first, the rubbing method was applied to the spine and both sides of the spine for 1 min. The pressing and kneading method was applied to the spine for 5 min, and the muscles on both sides of the spine for 5 min. Second, pressing and kneading the sensitive local acupoints in the spine for 2 min were performed. Thirdly, the affected limb was treated by twisting method for 1 min. Fourth, the rubbing method was applied to a midline from the forehead to the back of the brain for 1 min. This study aimed to use selective spinal manipulation to treat infant rats with cerebral palsy. The weight, Rotarod test, Foot-fault score, and growth hormone of infant rats with cerebral palsy were detected to understand the effect of selective spinal manipulation on the growth and development of infant rats with cerebral palsy. The results showed that it can promote weight gain, improve balance ability and motor function, promote growth and development of infant cerebral palsy rats, promote growth hormone secretion, and increase the temperature of sensitive parts of the back.

FGSQA-Net: A Weakly Supervised Approach to Fine-Grained Electrocardiogram Signal Quality Assessment.

OBJECTIVE: Due to the lack of fine-grained labels, current research can only evaluate the signal quality at a coarse scale. This article proposes a weakly supervised fine-grained electrocardiogram (ECG) signal quality assessment method, which can produce continuous segment-level quality scores with only coarse labels. METHODS: A novel network architecture, i.e. FGSQA-Net, is developed for signal quality assessment, which consists of a feature shrinking module and a feature aggregation module. Multiple feature shrinking blocks, which combine residual CNN block and max pooling layer, are stacked to produce a feature map corresponding to continuous segments along the spatial dimension. Segment-level quality scores are obtained by feature aggregation along the channel dimension. RESULTS: The proposed method was evaluated on two real-world ECG databases and one synthetic dataset. Our method produced an average AUC value of 0.975, which outperforms the state-of-the-art beat-by-beat quality assessment method. The results are visualized for 12-lead and single-lead signals over a granularity from 0.64 to 1.7 seconds, demonstrating that high-quality and low-quality segments can be effectively distinguished at a fine scale. CONCLUSION: FGSQA-Net is flexible and effective for fine-grained quality assessment for various ECG recordings and is suitable for ECG monitoring using wearable devices. SIGNIFICANCE: This is the first study on fine-grained ECG quality assessment using weak labels and can be generalized to similar tasks for other physiological signals.

An ECG Classification Method Based on Multi-Task Learning and CoT Attention Mechanism.

Electrocardiogram (ECG) is an efficient and simple method for the diagnosis of cardiovascular diseases and has been widely used in clinical practice. Because of the shortage of professional cardiologists and the popularity of electrocardiograms, accurate and efficient arrhythmia detection has become a hot research topic. In this paper, we propose a new multi-task deep neural network, which includes a shared low-level feature extraction module (i.e., SE-ResNet) and a task-specific classification module. Contextual Transformer (CoT) block is introduced in the classification module to dynamically model the local and global information of ECG feature sequence. The proposed method was evaluated on public CPSC2018 and PTB-XL datasets and achieved an average F1 score of 0.827 on the CPSC2018 dataset and an average F1 score of 0.833 on the PTB-XL dataset.