The electrocardiogram (ECG) is a non-invasive, inexpensive, and effective tool for myocardial infarction (MI) diagnosis. Conventional detection algorithms require solid domain expertise and rely heavily on handcrafted features. Although previous works have studied deep learning methods for extracting features, these methods still neglect the relationships between different leads and the temporal characteristics of ECG signals. To handle the issues, a novel multi-lead attention (MLA) mechanism integrated with convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) framework (MLA-CNN-BiGRU) is therefore proposed to detect and locate MI via 12-lead ECG records. Specifically, the MLA mechanism automatically measures and assigns the weights to different leads according to their contribution. The two-dimensional CNN module exploits the interrelated characteristics between leads and extracts discriminative spatial features. Moreover, the BiGRU module extracts essential temporal features inside each lead. The spatial and temporal features from these two modules are fused together as global features for classification. In experiments, MI location and detection were performed under both intra-patient scheme and inter-patient scheme to test the robustness of the proposed framework. Experimental results indicate that our intelligent framework achieved satisfactory performance and demonstrated vital clinical significance.

The electronic nose (e-nose) system is a newly developing detection technology for its advantages of non-invasiveness, simple operation, and low cost. However, lung cancer screening through e-nose requires effective pattern recognition frameworks. Existing frameworks rely heavily on hand-crafted features and have relatively low diagnostic sensitivity. To handle these problems, gated recurrent unit based autoencoder (GRU-AE) is adopted to automatically extract features from temporal and high-dimensional e-nose data. Moreover, we propose a novel margin and sensitivity based ordering ensemble pruning (MSEP) model for effective classification. The proposed heuristic model aims to reduce missed diagnosis rate of lung cancer patients while maintaining a high rate of overall identification. In the experiments, five state-of-the-art classification models and two popular dimensionality reduction methods were involved for comparison to demonstrate the validity of the proposed GRU-AE-MSEP framework, through 214 collected breath samples measured by e-nose. Experimental results indicated that the proposed intelligent framework achieved high sensitivity of 94.22%, accuracy of 93.55%, and specificity of 92.80%, thereby providing a new practical means for wide disease screening by e-nose in medical scenarios.

Multipath problems in complex structures continue to place limitations on local damage detection among common vibration-based nondestructive detection methods. An interactive vibration sensor based on time reversal technique for mechanical fatigues detection is proposed. The vibration sensor consists of an exciter for vibration generating and a receiver for picking-up the multipath transferred vibration signal. The exciter initially generates vibration with a chirp signal on the object structure and the receiver captures the reverberated signal. The time-reversed signal is then reconstructed and reemitted by the exciter to complete one iterative sensing cycle. It is found that the energy focusing effect of time reversal technique can be used as an indicator for detecting local damages in mechanical structures. A stainless-steel flat plate and a three-story frame are taken as two typical objects for proof-of-concept experiments. Mechanical fatigues are simulated by attaching a mass block on the flat plate and changing the tighten torque of the connection bolts in the frame. The peak values and the symmetry of the focused waveforms decrease while transfer paths changes with the simulated fatigues, suggesting the ability of the proposed technique in detecting the existence of local damages of the structure and its approximate location. Besides, robustness test results show that such a method performs good anti-interference ability against background vibration and weakly coupled loads.