GraphTS: Graph-represented time series for subsequence anomaly detection

Automatic detection of subsequence anomalies (i.e., an abnormal waveform denoted by a sequence of data points) in time series is critical in a wide variety of domains. However, most existing methods for subsequence anomaly detection often require knowing the length and the total number of anomalies in time series. Some methods fail to capture recurrent subsequence anomalies due to using only local or neighborhood information for anomaly detection. To address these limitations, in this paper, we propose a novel graph-represented time series (GraphTS) method for discovering subsequence anomalies. In GraphTS, we provide a new concept of time series graph representation model, which represents both recurrent and rare patterns in a time series. Particularly, in GraphTS, we develop a new 2D time series visualization (2Dviz) method, which compacts all 1D time series patterns into a 2D spatial temporal space. The 2Dviz method transfers time series patterns into a higher-resolution plot for easier sequence anomaly recognition (or detecting subsequence anomalies). Then, a Graph is constructed based on the 2D spatial temporal space of time series to capture recurrent and rare subsequence patterns effectively. The represented Graph also can be used to discover single and recurrent subsequence anomalies with arbitrary lengths. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and efficiency.

Strain-Insensitive Elastic Surface Electromyographic (sEMG) Electrode for Efficient Recognition of Exercise Intensities

Surface electromyography (sEMG) sensors are widely used in the fields of ergonomics, sports science, and medical research. However, current sEMG sensors cannot recognize the various exercise intensities efficiently because of the strain interference, low conductivity, and poor skin-conformability of their electrodes. Here, we present a highly conductive, strain-insensitive, and low electrode-skin impedance elastic sEMG electrode, which consists of a three-layered structure (polydimethylsiloxane/galinstan + polydimethylsiloxane/silver-coated nickel + polydimethylsiloxane). The bottom layer of the electrode consists of vertically conductive magnetic particle paths, which are insensitive to stretching strain, collect sEMG charge from human skin, and finally transfer it to processing circuits via an intermediate layer. Our skin-friendly electrode exhibits high conductivity (0.237 and 1.635 mΩ.cm resistivities in transverse and longitudinal directions, respectively), low electrode-skin impedance (47.23 kΩ at 150 Hz), excellent strain-insensitivity (10% change of electrode-skin impedance within the 0%-25% strain range), high fatigue resistance (>1500 cycles), and good conformability with skin. During various exercise intensities, the signal-to-noise ratio (SNR) of our electrode increased by 22.53 dB, which is 206% and 330% more than that of traditional Ag/AgCl and copper electrode, respectively. The ability of our electrode to efficiently recognize various exercise intensities confirms its great application potential for the field of sports health.

Retinal nerve fibre layer thickness is reduced in metabolic syndrome

AIMS

To investigate retinal nerve fibre layer (RNFL) thickness in people with metabolic syndrome (MetS) and healthy controls.

METHODS

A cross-sectional study was performed from March 2014 to January 2016. All participants underwent anthropometric and serological biochemical measurements, ophthalmological examination, and spectral-domain optical coherence tomography (SD-OCT). Individuals with elevated intraocular pressure, glaucoma, diabetic retinopathy and other ocular disorders were excluded. T-test, Chi square and general linear models were used to analyse the data.

RESULTS

In total, 278 eyes from 139 participants were investigated [median (interquartile range) age: 37 (32-43) years]. RNFL thickness was lower in the nasal superior (107.8 ± 19.5μm) and temporal superior (135.7 ± 18.9μm) sectors in MetS group compared with the control group (114.6 ± 22.4 μm, P = 0.013 and 140.7 ± 18.2 μm, P = 0.027, respectively). After multiple adjustments for age, gender and the side of the examined [right (OD)/left (OS)] eye, MetS was independently associated with a lower RFNL thickness in the nasal superior (β = 0.20, P = 0.009) and temporal superior (β = 0.14, P = 0.048) sectors. RNFL thickness was significantly reduced in participants with higher numbers of metabolic abnormalities, independent of age, gender and the side of the examined eye (P = 0.043).

CONCLUSION

Our findings demonstrate that MetS is independently associated with reduced RNFL thickness, suggesting that neurodegeneration is implicated in pathogenesis of MetS.

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals

BACKGROUND AND OBJECTIVES

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity.

METHODS

This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms.

RESULTS

The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time results show that the proposed method has less time complexity after feature selection.

CONCLUSIONS

The proposed feature extraction method is very effective for getting representatives information from mental states EEG signals in BCI applications and reducing the computational complexity of classifiers by reducing the number of extracted features.