Electroencephalogram prediction based on machine learning

Runan Wang

doi:10.1117/12.2629166

22 April 2022 Electroencephalogram prediction based on machine learning

Runan Wang

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121740U (2022) https://doi.org/10.1117/12.2629166
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

The quality of sleep has become a matter of concern to modern people. The medical field usually uses Electroencephalogram to detect the quality of sleep. The method in machine learning can figure out people's sleep quality by researching Electroencephalogram. However, this method still has its drawbacks, such as being restricted by data. In order to study the relationship between data characteristics and model performance, this paper explored the impact of standardization and data volume on accuracy based on Support Vector Machine. For these investigations, we set up three types of control groups. The first group compares standardized and non-standardized data. The second group compares segmentation at different time intervals, and the third group compares different data volumes. The experiment results show that the accuracy of the standardized data shows a specific upward trend under different segmentation methods. In contrast, the non-standardized data has no apparent phenomenon. Also, in the case of the same amount of data for each interval, taking 25 seconds as an example, the overall accuracy of the sampling situation is lower than that of the entire situation. However, the accuracy of the third class of data is increased. When the same data set uses different amounts of data to experiment, there are no significant test results. In other words, this experiment found that the size of the data and the way the data is processed have affected the accuracy of the results to a certain extent.

Citation Download Citation

Runan Wang "Electroencephalogram prediction based on machine learning", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121740U (22 April 2022); https://doi.org/10.1117/12.2629166

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