A Multihead ConvLSTM for Time Series Classification in eHealth Industry 4.0

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.