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Objectives Patients with psychiatric disorders have an increased risk of cardiovascular pathologies. A bidirectional feedback model between the brain and heart exists widely in both psychotic and nonpsychotic disorders. The aim of this study was to compare heart rate variability (HRV) and pulse wave velocity (PWV) functions between patients with psychotic and nonpsychotic disorders and to investigate whether subgroups defined by HRV and PWV features improve the transdiagnostic psychopathology of psychiatric classification. Methods In total, 3448 consecutive patients who visited psychiatric or psychological health services with psychotic (N = 1839) and nonpsychotic disorders (N = 1609) and were drug-free for at least 2 weeks were selected. HRV and PWV indicators were measured via finger photoplethysmography during a 5-minute period of rest. Canonical variates were generated through HRV and PWV indicators by canonical correlation analysis (CCA). Results All HRV indicators but none of the PWV indicators were significantly reduced in the psychotic group relative to those in the nonpsychotic group. After adjusting for age, gender, and body mass index, many indices of HRV were significantly reduced in the psychotic group compared with those in the nonpsychotic group. CCA analysis revealed 2 subgroups defined by distinct and relatively homogeneous patterns along HRV and PWV dimensions and comprising 19.0% (subgroup 1, n = 655) and 80.9% (subgroup 2, n = 2781) of the sample, each with distinctive features of HRV and PWV functions. Conclusions HRV functions are significantly impaired among psychiatric patients, especially in those with psychosis. Our results highlight important subgroups of psychiatric patients that have distinct features of HRV and PWV which transcend current diagnostic boundaries.

Removing noise from the original image plays an important role in many important applications involving image-based medical diagnosis and visual material examination for public security, and so on. Among them, there have been several published methods to solve the related problem, however, each approach has its advantages, and limitations. This paper examines a new measure of denosing in space domain based on 2-D kernel regression which overcomes the difficulties found in other measures. The idea of this method mainly let the values of a row or a column from an image are taken as the measured results of a fitting function. The following step is to estimate the weight coefficients using least square method. Finally, we obtain an denoised image by resampling the estimated function, and the variable x denotes the coordinate of an image. Results of an experimental applications of this method analysis procedure are given to illustrate the proposed technique, and compared with the basic wavelet-thresholding algorithm for image denoising.

In view of the virtue and shortage of genetic algorithm and BP network, this paper proposes a new BP network training method based on improved genetic algorithm (IGA-BP). This algorithm uses hierarchical code, adaptive crossover and mutation, pruning similar chromosomes, dynamic supply new chromosomes and other operations, so the network structure and weight are optimized at the same time and the "premature" phenomenon is avoided. The simulation results show that the IGA-BP network architecture is simple, the convergence rate is quick, and has good approximation and generalization ability.