Effect of cognitive training on episodic memory retrieval in amnestic mild cognitive impairment patients: study protocol for a clinical randomized controlled trial

Abstract Background Mild cognitive impairment (MCI) is a transition state between asymptomatic stage and dementia. Amnestic MCI (aMCI) patients who mainly present with memory deficits are highly likely to progress to Alzheimer’s disease (AD). At present, no broadly effective drug therapy is available to prevent the progression from memory deficit to dementia. Cognitive control training, which has transfer effects on multiple cognitive capacities including memory function in healthy old adults, has not yet been applied to aMCI. Methods/Design In this single-center, randomized double-blind placebo-controlled study, 70 aMCI patients will be recruited and randomly assigned to the training and control groups. The intervention is an Internet-based cognitive control training program performed for 30 min daily, five days per week, for 12 consecutive weeks. Neuropsychological assessment and structural and functional magnetic resonance imaging (MRI) will be performed at baseline and outcome. Primary outcomes are changes of episodic memory retrieval function. Secondary outcome measures are neuroplasticity changes measured by functional and structural MRI. Discussion In this study, an Internet-based cognitive control training program is adopted to investigate whether cognitive control training can enhance the retrieval of episodic memory in aMCI patients. The combination of multi-modal MRI and neuropsychological tests could have a good sensitivity in evaluating the effects of cognitive control training and could also uncover the underlying neural underpinning. Trial registration ClinicalTrials.gov, NCT03133052. Registered on 21 April 2017

An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE

The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing