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Multilabel electrocardiogram (ECG) classif- ication is of great significance for the diagnosis of cardiovascular diseases, and it is also a challenging task because of the various of pathological patterns across different cardiac abnormalities. This article analyzes the complex spatial-temporal variations information contained in the 12-lead ECG from a new perspective and proposes an advanced deep neural network (DNN) framework to accurately identify various multilabel ECG abnormalities. First, the 12-lead ECG was modeled as 3-D spatial-temporal variations involving the intraperiod and interperiod temporal dimensions and cross-lead spatial dimension. Then, a 3-D spatial-temporal learning network (3D-STLNet) was carefully designed to extract the complex pathological features from the 3-D spatial-temporal variations. Second, the multihead attention (MHA) mechanism was introduced into the proposed network to extract global temporal dependencies in ECG segments, which further improved the performance of the proposed network. By integrating a residual backbone network, a 3D-STLNet, and the MHA module, our proposed network allows end-to-end learning and prediction in a unified framework. The influence of several classic threshold selection strategies on the performance of multilabel ECG classification algorithm was also analyzed, which provides a reference for subsequent research on optimal threshold selection for multilabel ECG classification. Finally, our proposed network was evaluated on multiple large multilabel ECG public datasets, and the performance of our network outperforms that of current state-of-the-art networks, which demonstrates that our network has great competitiveness in identifying a wider range of multilabel ECG abnormalities.

Indoor formaldehyde (HCHO) is an important air pollutant, while it is very difficult to reduce HCHO to low-level (e.g. <0.08 mg/m3). Catalytic oxidation at ambient temperature has been increasingly recognized as one of the important methods to mitigate HCHO pollution due to its good effectiveness, stability, and recyclability. To further improve the activity of catalytic oxidation, this study develops the integrated MnCeOxcatalysts supported on palygorskite (Pal) and aluminium hydroxide (Al(OH)3). Our results indicate that the synergistic interaction in MnCeOxthrough the oxygen transfer mechanism from the oxygen reservoir CeO2to MnOxsignificantly improves the activity. Pal, Al(OH)3, etc. were applied as the supports with a focus on their dispersion, microstructure, strength, and relative role. MnCeOxcan be anchored on the surface of Al(OH)3with high dispersion. With the integrated catalyst, HCHO decreases from 1.012 to 0.086 mg/m3within 48 h. Higher oxidation activity of MnCeOxpowder may be ascribed to the amount of active components on the surface. The incorporation of ZSM–5 and activated carbon can improve the adsorption of HCHO, and all integrated catalysts exhibit stronger activities, with HCHO being degraded to the level lower than 0.08 mg/m3. Meantime, the samples exhibited good stability and strength (20.2 MPa) without obvious decreasing over five consecutive stability experiments.