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Background: Heart sound segmentation is a long-standing problem in heart analysis, and it is mainly caused by noise interference and diversification of heart sounds. Faced with the challenging of heart sound segmentation, a more applicable segmentation model was studied. Methods: In this process, the optimal modified Log-spectral amplitude and wavelet were used to suppress the noise in the heart sound, and used the duration-dependent hidden Markov model based on personalized Gaussian mixture model (PGMM-DHMM) to segment the fundamental heart sound (FHS) and the non-fundamental heart sound (non-FHS). Then used the optimized Mel frequency cepstral coefficient (MFCC) to realize the classification of S1 and S2 heart sound frames through the Convolutional neural network (CNN) classifier, which can avoid the errors caused by the ambiguity of the time domain features. Results: PGMM-DHMM can segment FHS more effectively, and the accuracy is 94.3%. The CNN classifier obtained the best results in the S1 and S2 classifications, the accuracy is 90.92%, the precision of S1 is 90.76%, the recall is 91.05%, the F-measure is 90.9%, and the precision of S2 is 91.07%, the recall is 90.79%, the F-measure is 90.93%. The final segmentation accuracy is 92.92%. In addition, the experimental results further indicate that CNN has more robust performance when classifying abnormal S1 and abnormal S2. Conclusions: The PGMM-DHMM model can better segment FHS and Non-FHS. The optimization of MFCC improves the classification effect of S1 and S2, and the improvement effect by the CNN classifier is significant, especially for abnormal heart sounds. The proposed algorithm is better than other algorithms at this stage.

Background: Suan-Zao-Ren Decoction (SZRD) has been widely used to treat neurological illnesses, like dementia, insomnia, and depression. However, mechanisms underlying SZRD’s improvement in cognitive function remains unclear. In this study, we examined SZRD’s effect on APP/PS1 transgenic mice as well as mechanisms associated with SZRD’s action in alleviating neuroinflammation and improving the synaptic plasticity. Methods: The APP/PS1 mice were treated with different dosages of SZRD (12.96 and 25.92 g/kg/d, in L-SZRD and H-SZRD groups, respectively) for four weeks. Morris water maze was conducted to determine changes in behaviors of the mice after the treatment. Meanwhile, in the samples of the hippocampus, Nissl staining and Golgi-Cox staining were used to detect the synaptic plasticity, Western blot (WB) was employed to test expressions of Aβ1-42, APP, ADAM10, BACE1, PS1, IDE, IBA1, GFAP, PSD95 and SYN, as well as the expressions of JAK2, STAT3 and their phosphorylation patterns to detect involvement of JAK2/STAT3 pathway. Besides, we examined the serum contents of IL-1β, IL-6, and TNF-α using ELISA.Results: Compared to the APP/PS1 mice without any treatment, SZRD, especially the L-SZRD, significantly ameliorated cognitive impairment of the APP/PS1 mice with decreases in the loss of neurons and Aβ plaque deposition as well as improvement of synaptic plasticity in the hippocampus (all PPConclusion: The SZRD, especially the L-SZRD, may improve the cognitive impairment and ameliorate the neural degeneration in APP/PS1 transgenic mice through decreasing Aβ accumulation and inhibiting neuroinflammation via JAK2/STAT3 pathway.