基于1D CNN-BiLSTM网络联合集成学习的心律失常智能诊断系统

为提高心律失常智能诊断的准确率,本研究提出了一种多网络融合模型和Stacking集成学习算法,用于八种心律失常疾病的智能诊断。使用1D CNN-BiLSTM融合网络提取单导联信号的高维特征和时域相关性特征,将十二个导联的心电信号特征融合,得到高维的特征张量,采用Stacking集成学习算法训练得到泛化性更好的诊断模型。通过比较准确性、精确性、召回率、F1-Score四个诊断性能指标,验证了利用十二导联融合特征作为最终诊断特征,准确率有显著提升,且Stacking集成学习算法较单一机器学习算法有更好的性能。本研究通过将机器学习、神经网络、集成学习算法有效结合,训练得到的心律失常智能诊断模型有较高的准确率,为基于心电信号的心律失常智能诊断提供了一种新方法。

Joint spectrogram segmentation and ridge-extraction method for separating multimodal guided waves in long bones

Ultrasonic guided waves (GWs) can be used to evaluate long bones effectively because of the ability to provide the information of the whole bone. In this study, a joint spectrogram segmentation and ridge-extraction (JSSRE) method was proposed to separate multiple modes in long bones. First, the Gabor time-frequency transform was applied to obtain the spectrogram of multimodal signals. Then, a multi-class image segmentation algorithm was used to find the corresponding region of each mode in the spectrogram, including an improved watershed transform and a region growing procedure. Finally, the ridges were extracted and the time domain signals representing individual modes were reconstructed from these ridges in each region. The validations of this method were discussed by simulated multimodal signals with different signal-to-noise ratios (SNR). The correlation coefficients between the original signals without noise and the reconstructed signals were calculated to analyze the results quantitatively. The results showed that the extracted ridges were in good agreement with generated theoretical dispersion curves, and the reconstructed signals were highly related to the original signals, even under the SNR=3 dB situation.