No abstract

The Poincaré plot emerges as an effective tool for assessing cardiovascular autonomic regulation. It displays nonlinear characteristics of heart rate variability (HRV) from electrocardiographic (ECG) recordings and gives a global view of the long range of ECG signals. In the telemedicine or computer-aided diagnosis system, it would offer significant auxiliary information for diagnosis if the patterns of the Poincaré plots can be automatically classified. Therefore, we developed an automatic classification system to distinguish five geometric patterns of the Poincaré plots from four types of cardiac arrhythmias. The statistics features are designed on measurements and an ensemble classifier of three types of neural networks is proposed. Aiming at the difficulty to set a proper threshold for classifying the multiple categories, the threshold selection strategy is analyzed. 24 h ECG monitoring recordings from 674 patients, which have four types of cardiac arrhythmias, are adopted for recognition. For comparison, Support Vector Machine (SVM) classifiers with linear and Gaussian kernels are also applied. The experiment results demonstrate the effectiveness of the extracted features and the better performance of the designed classifier. Our study can be applied to diagnose the corresponding sinus rhythm and arrhythmia substrates disease automatically in the telemedicine and computer-aided diagnosis system.

No abstract

No abstract

No abstract

Electronic noses recognize odors using sensor arrays, and usually face difficulties for odor complicacy, while animals have their own biological sensory capabilities for various types of odors. By implanting electrodes into the olfactory bulb of mammalian animals, odors may be recognized by decoding the recorded neural signals, in order to construct a bioelectronic nose. This paper proposes a spiking neural network (SNN)-based odor recognition method from spike trains recorded by the implanted electrode array. The proposed SNN-based approach exploits rich timing information well in precise time points of spikes. To alleviate the overfitting problem, we design a new SNN learning method with a voltage-based regulation strategy. Experiments are carried out using spike train signals recorded from the main olfactory bulb in rats. Results show that our SNN-based approach achieves the state-of-the-art performance, compared with other methods. With the proposed voltage regulation strategy, it achieves about 15% improvement compared with a classical SNN model.

No abstract

Background: Knee osteoarthritis (KOA) has become a public health problem. Several systematic reviews (SRs) have reported that duloxetine may be an effective treatment for improving pain and depressive symptoms in patients with KOA.Aim: To evaluate the available results and provide scientific evidence for the efficacy and safety of duloxetine for KOA.Methods: A comprehensive search strategy was conducted across eight databases from inception to 31 December 2021. Two researchers independently selected eligible studies, collected data and evaluated those included SRs’ quality. For assessing methodological quality, the Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR 2) was employed. Risk of Bias in Systematic Reviews (ROBIS) was used to assess the risk of bias. Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) was utilized for assessing reporting quality. In addition, the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used to determine primary outcome indicators’ evidence quality.Results: Totally 6 SRs were contained in this overview. After assessment based on AMSTAR 2, ROBIS, and PRISMA, unsatisfactory results in terms of methodological quality, risk of bias as well as reporting quality, were obtained. Limitations included a search of grey literature, the reasons for selecting the study type, an excluded study list and the specific reasons, reporting bias assessment, and reporting of potential sources of conflict of interest. According to the GRADE results, the evidence quality was high in 0, moderate in 5, low in 19, and very low in 36. Limitations were the most commonly downgraded factor, followed by publication bias and inconsistency.Conclusion: Duloxetine may be an effective treatment for improving pain and depressive symptoms in KOA patients with acceptable adverse events. However, due to the low quality of the available evidence, the original study design and the quality of evidence from SRs should be further improved, so as to provide strong scientific evidence for definitive conclusions.Systematic Review Registration: PROSPERO; (http://www.crd.york.ac.uk/PROSPERO/), identifier (CRD42021289823).