Zhang Tie, Hong Cong, Zou Yanbiao, Zhao Jun
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, China.
China Rehabilitation Research Center, Beijing, 100000, China.
Heliyon. 2024 Jul 6;10(14):e34145. doi: 10.1016/j.heliyon.2024.e34145. eCollection 2024 Jul 30.
Defecation care for disabled patients is a major challenge in health management. Traditional post-defecation treatment will bring physical injury and negative emotions to patients, while existing pre-defecation forecasting care methods are physically intrusive. On the basis of exploring the mechanism of defecation intention generation, and based on the characteristic analysis and clinical application of bowel sounds, it is found that the generation of desire to defecate and bowel sounds are correlated to a certain extent. Therefore, a deep learning-based bowel sound recognition method is proposed for human defecation prediction. The wavelet domain based Wiener filter is used to filter the bowel sound data to reduce other noise. Statistical analysis, fast Fourier transform and wavelet packet transform are used to extract the integrated features of bowel sound in time, frequency and time-frequency domain. In particular, an audio signal expansion data algorithm based on the Informer model is proposed to solve the problem of poor generalization of the training model caused by the difficulty of collecting bowel sound in reality. An improved one-dimensional residual network model (1D-IResNet) for defecation classification prediction is designed based on multi-domain features. The experimental results show that the proposed bowel sound augmentation strategy can effectively improve the data sample size and increase the sample diversity. Under the augmented dataset, the training speed of the 1D-IResNet model is accelerated, and the classification accuracy reaches 90.54 %, the F1 score reaches 83.88 %, which achieves a relatively good classification stability while maintaining a high classification index.
为残疾患者提供排便护理是健康管理中的一项重大挑战。传统的排便后治疗会给患者带来身体伤害和负面情绪，而现有的排便前预测护理方法具有身体侵入性。在探究排便意图产生机制的基础上，基于肠鸣音的特征分析及临床应用，发现排便欲望的产生与肠鸣音在一定程度上存在关联。因此，提出一种基于深度学习的肠鸣音识别方法用于人体排便预测。采用基于小波域的维纳滤波器对肠鸣音数据进行滤波，以减少其他噪声。运用统计分析、快速傅里叶变换和小波包变换在时域、频域和时频域提取肠鸣音的综合特征。特别地，提出一种基于Informer模型的音频信号扩展数据算法，以解决实际中肠鸣音采集困难导致训练模型泛化性差的问题。基于多域特征设计一种用于排便分类预测的改进一维残差网络模型（1D-IResNet）。实验结果表明，所提出的肠鸣音增强策略能够有效提高数据样本量并增加样本多样性。在增强数据集下，1D-IResNet模型的训练速度加快，分类准确率达到90.54%，F1分数达到83.88%，在保持较高分类指标的同时实现了较好的分类稳定性。
