The present invention discloses a
feature extraction and
state recognition method for one-dimensional physiological
signal based on depth learning. The method comprises: establishing a
feature extraction and
state recognition analysis model DBN of a on-dimensional physiological
signal based on depth learning, wherein the DBN model adopts a "pre-training+fine-tuning" training process, and in a pre-training stage, a first RBM is trained firstly and then a well-trained node is used as an input of a second RBM, and then the second RBM is trained, and so forth; and after training of all RBMs is finished, using a BP
algorithm to fin-tune a network, and finally inputting an eigenvector output by the DBN into a Softmax classifier, and determining a state of an individual that is incorporated into the one-dimensional physiological
signal. The method provided by the present invention effectively solves the problem that in the conventional one-dimensional physiological
signal classification process, feature inputs need to be selected manually so that classification precision is low; and through non-linear mapping of the deep confidence network, highly-separable features / feature combinations are automatically obtained for classification, and a better classification effect can be obtained by keeping optimizing the structure of the network.