Simultaneous Human Health Monitoring and Time-Frequency Sparse Representation Using EEG and ECG Signals

In the field of human health monitoring, intelligent diagnostic methods have drawn much attention recently to tackle the health problems and challenges faced by patients. In this paper, an efficient and flexible diagnostic method is proposed, which enables the simultaneous use of a machine learning method and sparsity-based representation technique. Specifically, the proposed method is based on a convolutional neural network (CNN) and generalized minimax-concave (GMC) method. First, measured potential signals, for instance, electroencephalogram (EEG) and electrocardiogram (ECG) signals are directly inputted into the designed network based on CNN for health conditions classification. The designed network adopts small convolution kernels to enhance the performance of feature extraction. In the training process, small batch samples are applied to improve the generalization of the model. Meanwhile, the "Dropout" strategy is applied to overcome the overfitting problem in fully connected layers. Then, for a record of the interested EEG or ECG signal, the sparse representation of useful time-frequency features can be estimated via the GMC method. Case studies of seizure detection and arrhythmia signal analysis are adopted to verify the performance of the proposed method. The experimental results demonstrate that the proposed method can effectively identify different health conditions and maximally enhance the sparsity of time-frequency features.