Detection of abnormal respiration using frequency-domain based LSTM

Abstract

Monitoring of respiratory status, which is an important vital sign, is essential for perioperative management. It has become increasingly important in recent years due to the spread of COVID-19 infection. To automate the respiratory monitoring, we have measured and analyzed the movement of thorax as a respiratory signal using a displacement sensor. In this study, we used Convolutional LSTM to discriminate normal and abnormal respiration based on temporal changes in frequency components obtained by complex wavelet transform. To improve the accuracy, we focused on signal preprocessing and network structure. We verified the usefulness of a network structure that combines quantization to reduce the number of patterns to be learned, blocking to determine the appropriate length of data for each learning iteration, and templating patterns by convolutional layers. In our experiment, the proposed method achieved high precision and recall of (99.8%, 99.6%) for normal respiration and (97.7%, 99.1%) for abnormal respiration.