Physiological signals processing brings challenges including dimensionality (due to the number of channels), heterogeneity (due to the different range of values) and multimodality (due to the different sources). In this regard, the current study intended, first, to use time-frequency ridge mapping in exploring the use of fused information from joint EEG-ECG recordings in tracking the transition between different states of anesthesia. Second, it investigated the effectiveness of pre-trained state-of-the-art deep learning architectures for learning discriminative features in the fused data in order to classify the states during anesthesia. Experimental data from healthy-brain patients undergoing operation (N = 20) were used for this study. Data was recorded from the BrainStatus device with single ECG and 10 EEG channels. The obtained results support the hypothesis that not only can ridge fusion capture temporal-spectral progression patterns across all modalities and channels, but also this simplified interpretation of time-frequency representation accelerates the training process and yet improves significantly the efficiency of deep models. Classification outcomes demonstrates that this fusion could yields a better performance, in terms of 94.14% precision and 0.28 s prediction time, compared to commonly used data-level fusing methods. To conclude, the proposed fusion technique provides the possibility of embedding time-frequency information as well as spatial dependencies over modalities and channels in just a 2D array. This integration technique shows significant benefit in obtaining a more unified and global view of different aspects of physiological data at hand, and yet maintaining the desired performance level in decision making.

生理信号处理带来的挑战包括维度（由于通道数量），异质性（由于值范围不同）和多模式性（由于来源不同）。在这方面，当前的研究首先是要使用时频脊线图来探索使用来自联合EEG-ECG记录的融合信息来跟踪不同麻醉状态之间的转换。其次，它研究了预训练的最新深度学习体系结构对于学习融合数据中的判别特征以对麻醉期间的状态进行分类的有效性。这项研究使用了来自健康脑部手术患者的实验数据（N = 20）。使用单个ECG和10个EEG通道从BrainStatus设备记录数据。获得的结果支持这样的假说，即脊融合不仅可以捕获所有模态和通道的时谱进展模式，而且这种时频表示的简化解释可以加速训练过程，但可以显着提高深度模型的效率。分类结果表明，与常用的数据级融合方法相比，这种融合可以产生94.14％的精度和0.28 s的预测时间更好的性能。总而言之，所提出的融合技术提供了将时频信息以及空间相关性嵌入到仅二维数组中的模态和通道的可能性。