Parkinson’s disease patients have early vocal cord damage, and their voiceprint characteristics differ significantly from those of healthy individuals, which can be used to identify Parkinson's disease. However, the samples of the voiceprint dataset of Parkinson's disease patients are insufficient, so this paper proposes a double self-attention deep convolutional generative adversarial network model for sample enhancement to generate high-resolution spectrograms, based on which deep learning is used to recognize Parkinson’s disease. This model improves the texture clarity of samples by increasing network depth and combining gradient penalty and spectral normalization techniques, and a family of pure convolutional neural networks (ConvNeXt) classification network based on Transfer learning is constructed to extract voiceprint features and classify them, which improves the accuracy of Parkinson’s disease recognition. The validation experiments of the effectiveness of this paper’s algorithm are carried out on the Parkinson’s disease speech dataset. Compared with the pre-sample enhancement, the clarity of the samples generated by the proposed model in this paper as well as the Fréchet inception distance (FID) are improved, and the network model in this paper is able to achieve an accuracy of 98.8%. The results of this paper show that the Parkinson’s disease recognition algorithm based on double self-attention deep convolutional generative adversarial network sample enhancement can accurately distinguish between healthy individuals and Parkinson’s disease patients, which helps to solve the problem of insufficient samples for early recognition of voiceprint data in Parkinson’s disease. In summary, the method effectively improves the classification accuracy of small-sample Parkinson's disease speech dataset and provides an effective solution idea for early Parkinson's disease speech diagnosis.
Objective To systematically review the effectiveness and safety of total hip arthroplasty (THA) vs. total hip resurfacing arthroplasty (THRA) in patients with osteoarthritis of the hip joint. Methods We electronically searched databases including PubMed, The Cochrane Library (Issue 3, 2012), EMbase, PloS, national institutes of clinical test library of the United States, national joint replacement registration network of Australia, British national joint registration network, British orthopaedic association network (BOA), Canada orthopaedic association (COA), American Academy of Orthopedic Surgeons network (AAOS), German Institute of Medical Documentation and Information network (DIMDI) for randomized controlled trials (RCTs) on the comparison between THA and THRA for osteoarthritis of the hip joint from inception to November, 2012. References of the included studies were also retrieved. Two reviewers independently screened literature according to the inclusion and exclusion criteria, extracted data and assessed the quality of the included studies. Then, meta-analysis was performed using RevMan 5.1 software. Results Six RCTs (10 articles) involving 518 cases of surgery (THA: n=259; THRA: n=259) were identified. The risk of bias of 8 articles was moderate and that of the other 2 articles was low. The results of meta-analysis showed that, compared to THRA, THA brings greater improvements of femoral offset within 2 years after surgery (MD=6.60, 95%CI 5.53 to 7.68, P=0.25). There were no significant differences between the two groups in Merle d’Aubigné Postel, WOMAC score, UCLA score, SF-36 Health Survey Questionnaire, walking speed, step length, walking cadence, postoperative leg length discrepancy, and complication rates. All these conclusions are based on early postoperative results. Conclusion Current evidence shows that, THA brings greater improvements of femoral offset than THRA, and no significant differences between the two groups were found in the other indicators.