Evidence has been retrieved through MEDLINE and Cochrane Libray about the treatment for patients with advanced Parkinson’s disease who suffered from on-off, dyskinesia and depression after chronic use of L-dopa. All of the evidence has been evaluated. Methods of evidence-based treatment were drawn up according to the evidence, clinciams’ experiences and patients’ preferences. All symptoms of the patient have been improved obviously.
For speech detection in Parkinson’s patients, we proposed a method based on time-frequency domain gradient statistics to analyze speech disorders of Parkinson’s patients. In this method, speech signal was first converted to time-frequency domain (time-frequency representation). In the process, the speech signal was divided into frames. Through calculation, each frame was Fourier transformed to obtain the energy spectrum, which was mapped to the image space for visualization. Secondly, deviations values of each energy data on time axis and frequency axis was counted. According to deviations values, the gradient statistical features were used to show the abrupt changes of energy value in different time-domains and frequency-domains. Finally, KNN classifier was applied to classify the extracted gradient statistical features. In this paper, experiments on different speech datasets of Parkinson’s patients showed that the gradient statistical features extracted in this paper had stronger clustering in classification. Compared with the classification results based on traditional features and deep learning features, the gradient statistical features extracted in this paper were better in classification accuracy, specificity and sensitivity. The experimental results show that the gradient statistical features proposed in this paper are feasible in speech classification diagnosis of Parkinson’s patients.
Parkinson’s disease is a neurodegenerative disease that mostly occurs in middle-aged and elderly people. It is characterized by progressive loss of dopaminergic neurons in the substantia nigra and aggregation of Lewy bodies, resulting in a series of motor symptoms and non-motor symptoms. Depression is the most important manifestation of non-motor symptoms, which seriously affects the quality of life of patients. Clinicians often use antidepressant drugs to improve the depressive symptoms of patients with Parkinson 's disease, but it is still urgent to solve the problems of drug side effects and drug resistance caused by such methods. Repetitive transcranial magnetic stimulation is a safe and non-invasive neuromodulation technique that can change the excitability of the corticospinal tract, induce the release of dopamine and other neurotransmitters, and further improve the depressive symptoms of patients with Parkinson 's disease. Based on this, this paper discusses and summarizes the research progress on the efficacy and potential mechanism of repetitive transcranial magnetic stimulation for improving depression in Parkinson 's disease at home and abroad, in order to provide reference for related clinical application research.
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.
ObjectiveTo systematically review the efficacy and safety of selective serotonin reuptake inhibitors (SSRIs) in the treatment of Parkinson's disease patients with depression. MethodsThe Cochrane Library (Issue 5, 2014), PubMed, EMbase, CNKI, VIP and WanFang Data databases were searched from inception to May 2014 for randomized controlled trials (RCTs) investigating the efficacy and safety of SSRIs for Parkinson's disease patients with depression. Two reviewers independently screened literature according to the inclusion and exclusion criteria, extracted data, and assessed the methodological quality of included studies. Then meta-analysis was performed using RevMan 5.2 software. ResultsA total of 12 RCTs were included. The results of meta-analysis showed that the efficacy of SSRIs was better than placebo (RR=2.18, 95%CI 1.60 to 2.97, P<0.000 01) and the dropouts rates of SSRIs were higher than placebo (OR=3.02, 95%CI 1.04 to 8.79, P=0.04). However, the incidence rate of adverse events between the SSRIs group and the placebo group was not statistically different. ConclusionCurrent evidence indicates that SSRIs are effective for the Parkinson's disease patients with depression. Because of the limitation of quantity and quality of included studies, large-scale multi-center RCTs are required to confirm these findings.
Objective To evaluate the effectiveness of repetitive transcranial magnetic stimulation (rTMS) for treating dysfunction in patients with Parkinson’s disease (PD). Methods We searched the Cochrane Library (Issue 1, 2010), MEDLINE, EMbase, CBMdisc, and CNKI from the date of the database establishment to April 2010. Randomized controlled trials (RCTs) of rTMS for patients with PD were collected. The quality of the included RCTs was critically appraised and data were extracted by two reviewers independently. Meta-analyses were conducted for the eligible RCTs. Results Eight RCTs were included. The pooled results of the first 2 RCTs showed that, there was no significant difference compared with control group about treating PD patients with clinical motor dysfunction by high-frequency rTMS 10 days later (WMD= –4.75, 95%CI –13.73 to 4.23). The pooled analysis of another 3 studies showed that, no significant difference were found about improving symptoms with treatment of low-frequency rTMS for 1 month compared with control group (WDM= –8.51, 95%CI –18.48 to 1.46). The pooled analysis of last 3 studies showed that, patient with treatment of low-frequency rTMS for 3 months, had been significantly improved in clinical symptoms such as neurological, behavior and emotional state, clinical motor function, and activities of daily living (WDM= –5.79, 95%CI –8.44 to –1.13). The frontal or motor cortex rTMS manifested as low frequency (≤1Hz), high intensity (≥90% RMT), multi-frequency (≥3 times) and long time (≥3 months) had a positive effect on the clinical symptoms of patients with PD and also had a long-term effect. Conclusions rTMS can improve clinical symptoms and dysfunction of the patients with PD.
Objective To assess the changes in depression symptoms in patients with Parkinson’s disease (PD) receiving combined treatment of deep brain stimulation (DBS) and antiparkinsonian drug therapy (DT) compared with under DT alone. Methods Related literature was retrieved from electronic databases, including PubMed, Cochrane Library, Embase, China National Knowledge Infrastructure, Wanfang Data, and VIP databases. Stata 14.0 software was used for statistical analysis. Network meta-analysis was performed using frequentist model to compare different interventions with each other. Results Five cohort studies and seven randomized controlled trials (RCTs) were included. The total number of participants was 1241. Assessed by the Beck Depression Inventory (BDI) score as the primary outcome, patients who received DT alone showed worse outcome in depression as compared to those who received subthalamic nucleus (STN)-DBS plus DT [standardized mean difference (SMD)=0.30, 95% confidence interval (CI) (0.01, 0.59), P<0.05], and there was no significant difference between the patients receiving globus pallidus interna (GPi)-DBS plus DT and those receiving STN-DBS plus DT [SMD=–0.12, 95%CI (–0.41, 0.16), P>0.05] or those receiving DT alone [SMD=–0.42, 95%CI (–0.84, 0.00), P>0.05]. Assessed by BDI-Ⅱ as the primary outcome, patients who received DT alone showed worse outcome in depression than those who received STN-DBS plus DT [SMD=0.29, 95%CI (0.05, 0.54), P<0.05]; compared with STN-DBS plus DT and DT alone, GPi-DBS plus DT was associated with better improvement in depression [SMD=–0.26, 95%CI (–0.46, –0.06), P<0.05; SMD=–0.55, 95%CI (–0.88, –0.23), P<0.05]. The ranking results of surface under the cumulative ranking curves showed that DBS plus DT had a better superiority in depression symptoms, and GPi-DBS was better than STN-DBS. Conclusion Compared with DT, STN-DBS plus DT is more likely to improve the depressive symptoms of PD patients, and GPi-DBS may be better than STN-DBS.