Recognizing diseases with multivariate physiological signals by a DeepCNN-LSTM network

The usage of multivariate time series to identify diseases plays an important role in the medical field, as it can help medical staff to improve diagnose accuracy and reduce medical costs. Current research shows that deep Convolutional Neural Networks (CNN) can automatically capture features from raw data and Long Short-Term Memory (LSTM) networks can manage and learn temporal dependence between time series data such as physiological signals. In this work, we propose a deep learning framework called DeepCNN-LSTM by combining the CNN and LSTM to leverage their respective advantages for disease recognition, allowing itself to characterize complex temporal varieties with multiple autoencoded features. In particular, we use stationary wavelet transform together with median filter to preprocess low-frequency signal data, and introduce sliding window to segment physiological time series before model training for performance improvement on the training speed as well as the accuracy for recognizing diseases. In addition, we validate our model on a hybrid benchmark dataset collecting from MIMIC and Fantasia databases and set up four kinds of comparative experiments. Empirical evaluations on the benchmark dataset demonstrate that the proposed model outperforms other competitive approaches.