A Multihead ConvLSTM for Time Series Classification in eHealth Industry 4.0 | Zendy

Yilin Wang | Zendy; Le Sun | Zendy; Dandan Peng | Zendy

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A Multihead ConvLSTM for Time Series Classification in eHealth Industry 4.0

Author(s) -

Yilin Wang,

Le Sun,

Dandan Peng

Publication year - 2022

Publication title -

wireless communications and mobile computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.42

H-Index - 64

eISSN - 1530-8677

pISSN - 1530-8669

DOI - 10.1155/2022/8773900

Subject(s) - computer science , artificial intelligence , machine learning , medical classification , health care , convolutional neural network , deep learning , data mining , pattern recognition (psychology) , economic growth , economics , medicine , nursing

Healthcare time series classification is to classify the collected human physiological information based on artificial intelligence technologies. The main purpose is to use pattern recognition technology to enable machines to analyze characteristics of human physiological signals based on deep learning in electronic health (E-health) industry 4.0. Healthcare time series classification can analyze various physiological information of the human body, make correct disease treatments, and reduce medical costs. In this paper, we propose a multiple-head convolutional LSTM (MCL) model for healthcare time series classification. MCL is a convolutional LSTM (ConvLSTM) model with multiple heads. It can extract both time and spatial features of healthcare data and increase the number of features to achieve more accurate classification results.

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