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Open AccessArticle

Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease

1
Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), 02792 Seoul, Korea
2
Korea Institute of Science and Technology Information (KISTI), 02456 Seoul, Korea
3
Science and Technology Information Science, University of Science & Technology (UST), 34113 Daejeon, Korea
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(8), 1750; https://doi.org/10.3390/ijerph15081750
Received: 4 July 2018 / Revised: 11 August 2018 / Accepted: 13 August 2018 / Published: 15 August 2018
Interest in research involving health-medical information analysis based on artificial intelligence, especially for deep learning techniques, has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. These features are extracted by analyzing various clinical pathology data, such as EHR (electronic health records), and academic literature using the techniques of data analysis, natural language processing, etc. However, still needed are more research and interest in applying the latest advanced artificial intelligence-based data analysis technique to bio-signal data, which are continuous physiological records, such as EEG (electroencephalography) and ECG (electrocardiogram). Unlike the other types of data, applying deep learning to bio-signal data, which is in the form of time series of real numbers, has many issues that need to be resolved in preprocessing, learning, and analysis. Such issues include leaving feature selection, learning parts that are black boxes, difficulties in recognizing and identifying effective features, high computational complexities, etc. In this paper, to solve these issues, we provide an encoding-based Wave2vec time series classifier model, which combines signal-processing and deep learning-based natural language processing techniques. To demonstrate its advantages, we provide the results of three experiments conducted with EEG data of the University of California Irvine, which are a real-world benchmark bio-signal dataset. After converting the bio-signals (in the form of waves), which are a real number time series, into a sequence of symbols or a sequence of wavelet patterns that are converted into symbols, through encoding, the proposed model vectorizes the symbols by learning the sequence using deep learning-based natural language processing. The models of each class can be constructed through learning from the vectorized wavelet patterns and training data. The implemented models can be used for prediction and diagnosis of diseases by classifying the new data. The proposed method enhanced data readability and intuition of feature selection and learning processes by converting the time series of real number data into sequences of symbols. In addition, it facilitates intuitive and easy recognition, and identification of influential patterns. Furthermore, real-time large-capacity data analysis is facilitated, which is essential in the development of real-time analysis diagnosis systems, by drastically reducing the complexity of calculation without deterioration of analysis performance by data simplification through the encoding process. View Full-Text
Keywords: electroencephalography (EEG); bio-signal; prediction of brain disease; Wave2vec; alcoholism; dementia; sequence classification; deep learning electroencephalography (EEG); bio-signal; prediction of brain disease; Wave2vec; alcoholism; dementia; sequence classification; deep learning
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Kim, S.; Kim, J.; Chun, H.-W. Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease. Int. J. Environ. Res. Public Health 2018, 15, 1750.

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