Humans ; Parkinson Disease/diagnosis* ; Machine Learning

At present, the incidence of Parkinson's disease (PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram (EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band ( P = 0.034) and State5 of Gamma frequency band ( P = 0.010) could be used to differentiate health controls and off-medication Parkinson's disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson's disease.
Humans ; Parkinson Disease/diagnosis* ; Quality of Life ; Cluster Analysis ; Electroencephalography ; Healthy Volunteers

The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson's disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson's disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.
Humans ; Aged ; Parkinson Disease/diagnosis* ; Aging ; Algorithms ; Gait ; Lower Extremity

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.
Cluster Analysis ; Humans ; Parkinson Disease/diagnosis* ; Speech

BACKGROUND: Reports evaluating the efficacy of transcranial sonography (TCS) for the differential diagnosis of Parkinson disease (PD) and other movement disorders in China are scarce. Therefore, this study aimed to assess the application of TCS for the differential diagnosis of PD, multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and essential tremor (ET) in Chinese individuals. METHODS: From 2017 to 2019, 500 inpatients treated at the Department of Dyskinesia, Beijing Tiantan Hospital, Capital Medical University underwent routine transcranial ultrasound examination. The cross-sections at the midbrain and thalamus levels were scanned, and the incidence rates of substantia nigra (SN) positivity and the incidence rates of lenticular hyperechoic area were recorded. The echo of the SN was manually measured. RESULTS: Of the 500 patients, 125 were excluded due to poor signal in temporal window sound transmission. Among the 375 individuals with good temporal window sound transmission, 200 were diagnosed with PD, 90 with ET, 50 with MSA, and 35 with PSP. The incidence rates of SN positivity differed significantly among the four patient groups (χ2 = 121.061, P < 0.001). Between-group comparisons were performed, and the PD group showed a higher SN positivity rate than the ET (χ2 = 94.898, P < 0.017), MSA (χ2 = 57.619, P < 0.017), and PSP (χ2 = 37.687, P < 0.017) groups. SN positivity showed a good diagnostic value for differentiating PD from the other three movement diseases, collectively or individually. The incidences of lenticular hyperechoic area significantly differed among the four patient groups (χ2 = 38.904, P < 0.001). Next, between-group comparisons were performed. The lenticular hyperechoic area was higher in the PD group than in the ET (χ2 = 6.714, P < 0.017) and MSA (χ2 = 18.680, P < 0.017) groups but lower than that in the PSP group (χ2 = 0.679, P > 0.017). CONCLUSION SN positivity could effectively differentiate PD from ET, PSP, and MSA in a Chinese population.
Diagnosis, Differential ; Humans ; Multiple System Atrophy/diagnostic imaging* ; Parkinson Disease/diagnostic imaging* ; Substantia Nigra/diagnostic imaging* ; Supranuclear Palsy, Progressive

Aged ; Aged, 80 and over ; Alzheimer Disease/diagnosis* ; Animals ; Biomarkers/blood* ; Cognitive Dysfunction ; Female ; Humans ; Male ; Membrane Glycoproteins/blood* ; Mental Status and Dementia Tests ; Mice ; Middle Aged ; Models, Animal ; Morris Water Maze Test ; Parkinson Disease/diagnosis* ; ROC Curve ; Receptors, Immunologic/blood* ; Sensitivity and Specificity

Aiming at the limitations of clinical diagnosis of Parkinson's disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor's detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.
Electroencephalography ; Humans ; Intelligence ; Parkinson Disease/diagnosis* ; Polysomnography ; REM Sleep Behavior Disorder/diagnosis*

OBJECTIVES: Parkinson disease (PD) is frequently associated with olfactory disorder at early stage, which is caused by deposition of Lewy bodies emerging from the olfactory bulb to higher olfactory centers. Early detection of olfactory disorder in the patients with PD may lead to the early diagnosis and treatment for this refractory disease. METHODS: Visual analog scale (VAS), Jet Stream Olfactometry, and Japanese smell identification test, Open Essence (OE), were carried out on 39 patients with PD. Thirty-one patients with postviral olfactory disorder (PVOD), which was caused by the olfactory mucosal dysfunction, were also enrolled in this study as control. RESULTS: There were no significant differences in detection thresholds (2.2 vs. 1.4, P=0.13), recognition thresholds (3.9 vs. 3.5, P=0.39) and OE (4.8 vs. 4.2, P=0.47) between PVOD and PD, while VAS scores of PVOD and PD were significantly different (2.0 and 6.2, P<0.01). In OE, significant differences were observed in the accuracy rates of menthol (68% vs. 44%, P=0.04) and Indian ink (42% vs. 15%, P=0.01) between PVOD and PD. Of particular interest, patients with PVOD tended to select “no detectable,” while patients with PD tended to select wrong alternative other than “no smell detected.” CONCLUSION: Discrepancy between VAS and OE, and high selected rates of wrong alternative other than “undetectable” in OE might be significant signs of olfactory dysfunction associated with PD.
Asian Continental Ancestry Group ; Early Diagnosis ; Humans ; Ink ; Lewy Bodies ; Menthol ; Olfactometry ; Olfactory Bulb ; Parkinson Disease ; Rivers ; Smell ; Visual Analog Scale

PURPOSE: To assess the urodynamic findings in patients with Parkinson disease (PD) with overactive bladder symptoms. METHODS: We performed a retrospective chart review of all PD patients who were seen in an outpatient clinic for lower urinary tract symptoms (LUTS) between 2010 and 2017 in a single-institution. Only patients who complained of overactive bladder (OAB) symptoms and underwent a video-urodynamic study for these symptoms were included. We excluded patients with neurological disorders other than PD and patients with voiding LUTS but without OAB symptoms. RESULTS: We included 42 patients (29 men, 13 women, 74.5±8.1 years old). Seven patients (16.7%) had a postvoid residual (PVR) bladder volume >100 mL and only one reported incomplete bladder emptying. Detrusor overactivity (DO) was found in all 42 patients (100%) and was terminal in 19 (45.2%) and phasic in 22 patients (52.4%). Eighteen patients had detrusor underactivity (DU) (42.3%). Later age of PD diagnosis was the only parameter associated with DU (P=0.02). Patients with bladder outlet obstruction (BOO) were younger than patients without BOO (70.1 years vs. 76.5 years, P=0.004), had later first sensation of bladder filling (173.5 mL vs. 120.3 mL, P=0.02) and first involuntary detrusor contraction (226.4 mL vs. 130.4 mL, P=0.009). CONCLUSIONS: DO is almost universal in all patients with PD complaining of OAB symptoms (97.1%). However, a significant percentage of patients also had BOO (36.8%), DU (47%), and increased PVR (16.7%) indicating that neurogenic DO may not be the only cause of OAB symptoms in PD patients.
Ambulatory Care Facilities ; Diagnosis ; Female ; Humans ; Lower Urinary Tract Symptoms ; Male ; Nervous System Diseases ; Parkinson Disease ; Parkinsonian Disorders ; Retrospective Studies ; Sensation ; Urinary Bladder ; Urinary Bladder Neck Obstruction ; Urinary Bladder, Overactive ; Urinary Incontinence ; Urodynamics

BACKGROUND AND PURPOSE: Associations between alterations in body mass index (BMI) and cognitive function have been reported in Parkinson's disease (PD). We investigated whether the BMI at a PD diagnosis is associated with cognitive decline and the future development of dementia. METHODS: We recruited 70 patients with de novo PD who underwent neuropsychological testing every 3 years and were followed up for more than 6 years. We classified patients into the following three groups based on their BMI at the diagnosis: under-/normal weight (n=21), overweight (n=22), and obese (n=27). We evaluated differences in the rate of cognitive decline over time among the groups using linear mixed models and the conversion rate to dementia using survival analysis. RESULTS: The obese patients with PD showed a slower deterioration of global cognitive function as well as language and memory functions than did the under-/normal-weight group during the 6-year follow-up. The three BMI groups showed different rates of conversion to dementia (log-rank test: p=0.026). The combined overweight and obese group showed a lower risk of developing dementia compared with the under-/normal-weight group (hazard ratio= 0.36, 95% CI=0.12–0.82, p=0.046). CONCLUSIONS: We have demonstrated that a higher-than-normal BMI at the time of a PD diagnosis has a protective effect against the deterioration of cognitive function and the conversion to dementia.
Body Mass Index ; Cognition ; Dementia ; Diagnosis ; Follow-Up Studies ; Humans ; Memory ; Neuropsychological Tests ; Overweight ; Parkinson Disease