Discovering hidden information in biosignals from patients using artificial intelligence

Figures

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  Fig. 1.. Potentiality and usability of hidden information present in diverse types of biosignals. The characteristic of artificial intelligence that can extract valuable information allows the opportunity to acquire and use hidden information not perceived by humans. It provides a diagnosis that is more accurate, early detection of a clinical event, and new insight on disease and prescription of drugs.

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  Fig. 2.. An overview of the relationship between artificial intelligence, machine learning, and deep learning. The term artificial intelligence (AI) includes all software that mimics the cognitive functions of humans. Among the various algorithms in AI, machine learning algorithms (deep learning, in particular) that learn patterns in data are considered promising owing to their distinctive performance. CNN: convolutional neural networks, RNN: recurrent neural networks, DQN: deep Q netowrk.

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  Fig. 3.. Schematic illustration of the underlying principle of convolution in a convolutional neural network (CNN) and expression of a recursive neural network (RNN). (A) Convolution is a type of matrix multiplication; however, a CNN changes the weight values in the filter in the training process to maximize the valuable features that are useful to discriminate between labels. (B) RNN recursively uses the output of its network as the input for the next step, thus, a long expression (top) is summarized as a short one (bottom).

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  Fig. 4.. Two different approaches for extracting hidden information from biosignals in deep learning. (A) Convolution in a convolutional neural network fits the convolutional matrix to extract a feature map that is a type of matrix containing valuable information for discriminating between labels. (B) An autoencoder extracts hidden information useful for reconstruction of raw data from the data itself. Although the features are derived regardless of label information, principal information included in the raw signal is obtained that can be used in other machine learning algorithms to predict or detect labels.

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  Fig. 5.. Schematic of the entire process of an artificial intelligence (AI) research or project. As machine learning algorithms depend on the amount and quality of data, collecting proper datasets is a critical step to begin AI-related projects. Extracting valuable information from a huge dataset is cumbersome; hence, this step is conducted in the process of training either in a convolutional neural network or independently (Autoencoder).