Feature Selection and Recognition of Multivariate Physiological Signals Using Binary Firefly Algorithm Interest in pattern recognition of physiological signals is growing in recent years due to the rapid development of telemedicine and the Internet of Things. However, on account of high dimensionality, multivariate, and the large amount of data, feature extraction and selection of multiple physiological signals is still a hard nut to crack in pattern recognition. Therefore, a measuring experiment of multivariate physiological signals before and after physical exercises was firstly carried out. Signals of electrocardiogram (ECG), blood pressure and oxygen saturation are simultaneously measured for 30 subjects. Moreover, multivariate physiological signals are in turn processed by filtering, segmentation and morphological feature extraction based on heartbeat samples. A novel binary firefly algorithm (NBFA) is proposed in this paper to realize the automatic feature selection for multiple physiological signals. The NBFA evaluates each binary firefly according to both classification accuracy and the number of features. The movement of a firefly is updated by a bit conversion factor and a random mounting flying step. High classification results of 30 subjects show that morphological features can depict main characteristics of 12lead ECG signals. The NBFA performs superior to the PCA, PSO in the feature selection of multiple physiological signals. It can quickly find the small number of features as well as obtain high classification, which further reduces the computation load and data storage capacity in pattern recognition of multiple physiological signals.

MultiChannel Physiological Signal Emotion Recognition Based on ReliefF Feature Selection Emotion recognition plays a very important role nowadays. Emotional recognition of electroencephalogram (EEG) signals involves highdimensional EEG data, which is still a challenging task now. This paper presents an emotion recognition framework based on EEG and peripheral signals, and the ReliefF algorithm is used to select features of EEG and peripheral signals. The DEAP data set is used to process multichannel physiology signals. In this paper the human emotion is classified into two categories (happy, unhappy) and three categories (happy, neutral, unhappy), respectively. The performance of the proposed method is evaluated in conjunction with ReliefF algorithm. The best average accuracy of KNearest Neighbor (KNN) and random forest (RF) are 94.295% and 98.832% in the binary classification tasks, and 79.210% and 98.364% in the triple classification tasks, respectively. At the same time, we also have adopted F1score as the metric in the binary classification, which are 93.944% and 98.835%. The evaluation on the DEAP data set shows that using the ReliefF algorithm for feature selection can achieve good results on the issue of physiological emotion recognition.