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System and approach to detecting of gastric slow wave and environmental noise suppression based on optically pumped magnetometer

Liang Shuang, Gao Kexin, He Junhuai, Jia Yikang, Jiao Hongchen, Feng Lishuang

Biocybernetics and Biomedical Engineering

2024

Vol. 44, no. 1

1--8

Gastric slow waves (SWs) are commonly used for the quantitative assessment of gastric functional disorders. Compared with surface electrogastrography, using of magnetic signals to record SWs can achieve higher-quality signal recording. In this study, we discovered that optically pumped magnetometers (OPM) based on the spin exchange relaxation-free method have comparable weak magnetic detection capabilities to superconducting quantum interference devices but without liquid helium cooling. However, owing to the inevitable interference of low-frequency environmental drift, the characteristic features of SW are obscured, greatly increasing the difficulty in detecting gastric magnetic signals. Therefore, in this study, we constructed an OPM Magnetogastrography (OPM-MGG). We proposed an adaptive filtering architecture combined with environmental drift suppression and a non-stationary signal decomposition method for extracting SW signals. Through controlled human experiments, the results demonstrated that our testing system successfully extracted SW signals in the frequency range of 2-4 cycles per minute. The extracted SW signals exhibited consistent power and time-frequency characteristics with the reported results. This study validates the feasibility of (1) using the OPM-MGG system for capturing SW signals and (2) the proposed processing strategies for identifying ultralow-frequency SW signals. In conclusion, the OPM-MGG system and the signal extraction strategies developed in this study have the potential to provide a wearable technology for bioweak magnetic field measurements, offering new opportunities for both research and clinical applications.

Investigating the effects of i-complexity and e-complexity on the learnability of morphological systems

Johnson Tamar, Gao Kexin, Smith Kenny, Rabagliati Hugh, Culbertson Jennifer

Journal of Language Modelling

2021

Vol. 9, No. 1

97--150

Research on cross-linguistic differences in morphological paradigms reveals a wide range of variation on many dimensions, including the number of categories expressed, the number of unique forms, and the number of inflectional classes. However, in an influential paper, Ackerman and Malouf (2013) argue that there is one dimension on which languages do not differ widely: in predictive structure. Predictive structure in a paradigm describes the extent to which forms predict each other, called i-complexity. Ackerman and Malouf (2013) show that although languages differ according to measure of surface paradigm complexity, called e-complexity, they tend to have low i-complexity. They conclude that morphological paradigms have evolved under a pressure for low i-complexity. Here, we evaluate the hypothesis that language learners are more sensitive to i-complexity than e-complexity by testing how well paradigms which differ on only these dimensions are learned. This could result in the typological findings Ackerman and Malouf (2013) report if even paradigms with very high e-complexity are relatively easy to learn, so long as they have low i-complexity. First, we summarize a recent work by Johnson et al. (2020) suggesting that both neural networks and human learners may actually be more sensitive to e-complexity than i-complexity. Then we build on this work, reporting a series of experiments which confirm that, indeed, across a range of paradigms that vary in either e- or icomplexity, neural networks (LSTMs) are sensitive to both, but show a larger effect of e-complexity (and other measures associated with size and diversity of forms). In human learners, we fail to find any effect of i-complexity on learning at all. Finally, we analyse a large number of randomly generated paradigms and show that e- and i-complexity are negatively correlated: paradigms with high e-complexity necessarily show low i-complexity. We discuss what these findings might mean for Ackerman and Malouf’s hypothesis, as well as the role of ease of learning versus generalization to novel forms in the evolution of paradigms.