Cardiology knowledge free ECG feature extraction using generalized tensor rank one discriminant analysis

Applications based on electrocardiogram (ECG) signal feature extraction and classification are of major importance to the autodiagnosis of heart diseases. Most studies on ECG classification methods have targeted only 1or 2-lead ECG signals. This limitation results from the unavailability of real clinical 12-lead ECG data, which would help train the classification models. In this study, we propose a new tensor-based scheme, which is motivated by the lack of effective feature extraction methods for direct tensor data input. In this scheme, an ECG signal is represented by third-order tensors in the spatial-spectral-temporal domain after using short-time Fourier transform on the raw ECG data. To overcome the limitations of tensor rank one discriminant analysis (TR1DA) inherited from linear discriminant analysis, we introduced a generalized tensor rank one discriminant analysis (GTR1DA). This approach involves considering the distribution of the data points near the classification boundary to calculate better projection tensors. The experimental results showed that the proposed method achieves greater classification accuracy than other vectorand tensor-based methods. Finally, GTR1DA features a better convergence property than the original TR1DA.