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Abstract Background A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs. Method Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors. Result The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration. Conclusion The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.

In this study, we investigated a control algorithm for a semi-active prosthetic knee based on reinforcement learning (RL). Model-free reinforcement Q-learning control with a reward shaping function was proposed as the voltage controller of a magnetorheological damper based on the prosthetic knee. The reward function was designed as a function of the performance index that accounts for the trajectory of the subject-specific knee angle. We compared our proposed reward function to a conventional single reward function under the same random initialization of a Q -matrix. We trained this control algorithm to adapt to several walking speed datasets under one control policy and subsequently compared its performance with that of other control algorithms. The results showed that our proposed reward function performed better than the conventional single reward function in terms of the normalized root mean squared error and also showed a faster convergence trend. Furthermore, our control strategy converged within our desired performance index and could adapt to several walking speeds. Our proposed control structure has also an overall better performance compared to user-adaptive control, while some of its walking speeds performed better than the neural network predictive control from existing studies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Hutabarat, Ekkachai, Hayashibe and Kongprawechnon.)

Molecular interactions of plumbagin inclusion complexes with β-cyclodextrin (BCD), dimethyl--cyclodextrin (MBCD), and hydroxypropyl-β-cyclodextrin (HPBCD) were investigated by semi-empirical, Parameterization Method 6 and 7 (PM6, and PM7) in the aqueous phase using polarizable continuum calculations. The results revealed two different binding modes of the plumbagin molecule inside the BCD cavity with a negative value of the complexation energy. In conformation-I, the hydroxyl phenolic group of plumbagin was placed in the BCD cavity near the narrow-side of the host molecule. In the other model, conformation-II, the methyl quinone group of plumbagin was placed in the cavity of BCD near the narrow-side of the host molecule. The higher the negative value of the complexation energy, the more favorable is the pathway of inclusion-complex formation., Competing Interests: The authors declare no conflict of interest.