A Comparison of SVM and CNN-LSTM Based Approach for Detecting Smoke Inhalations from Respiratory signal

Wearable sensors have successfully been used in recent studies to monitor cigarette smoking events and analyze people's smoking behavior. Respiratory inductive plethysmography (RIP) has been employed to track breathing and to identify characteristic breathing pattern specific to smoking. Pattern recognition algorithms such as Support Vector Machine (SVM), Hidden Markov Model, Decision tree, or ensemble approaches have been used to identify smoke inhalations. However, no deep learning approaches, which have been proved effective to many time series datasets, have ever been tested yet. Hence, a Convolutional Neural Network (CNN) and Long Term Short Memory (LSTM) based approach is presented in this paper to detect smoke inhalations in the breathing signal. To illustrate the effectiveness of this deep learning approach, a traditional machine learning (SVM) based approach was used for comparison. On the validation dataset of 120 smoking sessions performed in a laboratory setting by 30 moderate-to-heavy smokers, the CNN-LSTM approach achieved an F1-score of 72% in leave-one-subject-out (LOSO) cross-validation method whereas the classical SVM approach scored 63%. These results suggest that deep learning-based approaches might provide a better analytical method for detection of smoke inhalations than more conventional machine learning approaches.