Automatic epileptic seizure detection based on persistent homology.

Epilepsy is a prevalent brain disease, which is quite difficult-to-treat or cure. This study developed a novel automatic seizure detection method based on the persistent homology method. In this study, a Vietoris-Rips (VR) complex filtration model was constructed based on the EEG data. And the persistent homology method was applied to calculate the VR complex filtration barcodes to describe the topological changes of EEG recordings. Afterward, the barcodes as the topological characteristics of EEG signals were fed into the GoogLeNet for classification. The persistent homology is applicable for multi-channel EEG data analysis, where the global topological information is calculated and the features are extracted by considering the multi-channel EEG data as a whole, without the multiple calculations or the post-stitching. Three databases were used to evaluate the proposed approach and the results showed that the approach had high performances in the epilepsy detection. The results obtained from the CHB-MIT Database recordings revealed that the proposed approach can achieve a segment-based averaged accuracy, sensitivity and specificity values of 97.05%, 96.71% and 97.38%, and achieve an event-based averaged sensitivity value of 100% with 1.22 s average detection latency. In addition, on the Siena Scalp Database, the proposed method yields averaged accuracy, sensitivity and specificity values of 96.42%, 95.23% and 97.6%. Multiple tasks of the Bonn Database also showed achieved accuracy of 99.55%, 98.63%, 98.28% and 97.68%, respectively. The experimental results on these three EEG databases illustrate the efficiency and robustness of our approach for automatic detection of epileptic seizure.

Wearable Electrocardiogram Quality Assessment Using Wavelet Scattering and LSTM.

As the fast development of wearable devices and Internet of things technologies, real-time monitoring of ECG signals is quite critical for cardiovascular diseases. However, dynamic ECG signals recorded in free-living conditions suffered from extremely serious noise pollution. Presently, most algorithms for ECG signal evaluation were designed to divide signals into acceptable and unacceptable. Such classifications were not enough for real-time cardiovascular disease monitoring. In the study, a wearable ECG quality database with 50,085 recordings was built, including A/B/C (or high quality/medium quality/low quality) three quality grades (A: high quality signals can be used for CVD detection; B: slight contaminated signals can be used for heart rate extracting; C: heavily polluted signals need to be abandoned). A new SQA classification method based on a three-layer wavelet scattering network and transfer learning LSTM was proposed in this study, which can extract more systematic and comprehensive characteristics by analyzing the signals thoroughly and deeply. Experimental results ( mACC = 98.56%, mF 1 = 98.55%, Se A = 97.90%, Se B = 98.16%, Se C = 99.60%, + P A = 98.52%, + P B = 97.60%, + P C = 99.54%, F 1A = 98.20%, F 1B = 97.90%, F 1C = 99.60%) and real data validations proved that this proposed method showed the high accuracy, robustness, and computationally efficiency. It has the ability to evaluate the long-term dynamic ECG signal quality. It is advantageous to promoting cardiovascular disease monitoring by removing contaminating signals and selecting high-quality signal segments for further analysis.

Maize (Zea mays L. Sp.) varieties significantly influence bacterial and fungal community in bulk soil, rhizosphere soil and phyllosphere.

The plant-microbe interaction can affect ecosystem function, and many studies have demonstrated that plant species influence relevant microorganisms. In this study, microbial communities in bulk soil, rhizosphere soil and phyllosphere from different maize varieties were investigated using high-throughput sequencing method. Results demonstrated that cultivar Gaoneng 1 (G1) showed higher bacterial diversity in soil (both bulk and rhizosphere soils) and lower bacterial diversity in the phyllosphere, while cultivar Gaoneng 2 (G2) had lower fungal diversity in both the soil and phyllosphere compare to the other cultivars. The bacterial community structure of soils among the three varieties was significantly different; however, no significant differences were found in the soil fungal community and phyllosphere bacterial and fungal community. The soil networks from cultivar G1 and phyllosphere networks from cultivar Zhengdan (ZD) have the highest complexity in contrast to the other two cultivars. In conclusion, the bacterial community structure in bulk soil of different cultivars was significantly different, so do the co-occurrence ecological networks of phyllosphere bacterial community. This study comprehensively analyzed the microbial community among different maize cultivars and could be useful for guiding practices, such as evaluation of new plant cultivars and quality predictions of these varieties at the microbial level.

P-side up AlGaInP-based light emitting diodes with dot-patterned GaAs contact layers.

High-brightness p-side up AlGaInP-based red light emitting diodes (LEDs) with dot-patterned GaAs contact layer and surface rough structure are presented in this article. Initial LED structure of p-GaP/AlGaInP/GaAs is epitaxially grown using metal organic chemical vapor deposition technique. Using novel twice transferring process, the p-GaP layer is remained at the top side as both the current spreading and-window layer. Dot patterned GaAs contact dots are formed between main structure and rear mirror to improve light reflection and current spreading. Moreover, the surface of p-GaP window is further textured by nano-sphere lithography technique for improving the light extraction. Significant improvement in output power is found for AlGaInP LEDs with GaAs contact dots and roughened p-GaP window as compared with those of LEDs with traditional n-side up and p-side up structures without roughened surfaces.