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

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

Humans ; Parkinson Disease/diagnosis* ; Machine Learning

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

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

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*

In order to detect freezing of gait of Parkinson's patients automatically, a system based on inertial measurement unit to detect freezing of gait for Parkinson's patients is established. The two inertial measurement units are respectively fixed on the left and right ankles of the patient to be measured, the freezing index is calculated by windowed Fourier transform, the freezing threshold is calculated based on the freezing index during normal walking, and the freezing index and the freezing threshold are compared to complete the detection of freezing of gait. The experimental results show that the number of freezing of gait occurrences in Parkinson's patients is accurately detected, and it has high sensitivity and specificity, which can assist doctors to objectively assess the patient's condition.
Diagnostic Equipment ; standards ; Gait Disorders, Neurologic ; diagnosis ; etiology ; Humans ; Parkinson Disease ; complications ; Sensitivity and Specificity ; Walking

Methods for achieving diagnosis of Parkinson's disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classification accuracy of the proposed method is significantly improved on two public datasets and the increasement of mean accuracy is up to 25.44%. This method not only effectively improves the classification accuracy of PD speech dataset, but also increases the sample utilization rate, providing a new idea for the diagnosis of PD.
Algorithms ; Data Mining ; Humans ; Machine Learning ; Parkinson Disease ; diagnosis ; Speech

PURPOSE: Discontinuation of offending drugs can prevent drug-induced parkinsonism (DIP) before it occurs and reverse or cure it afterwards. The aim of this study was to investigate the prevalence of DIP and the utilization of offending drugs through an analysis of representative nationwide claims data. MATERIALS AND METHODS: We selected DIP patients of ages ranging from 40 to 100 years old with the G21.1 code from the Korean National Service Health Insurance Claims database from 2009 to 2015. The annual standardized prevalence of DIP was explored from 2009 to 2015. Trends were estimated using the compound annual growth rate (CAGR) and the Cochran-Armitage test for DIP over the course of 6 years. Additionally, the utilization of offending drugs was analyzed. RESULTS: The annual prevalence of DIP was 4.09 per 100000 people in 2009 and 7.02 in 2015 (CAGR: 9.42%, p<0.001). Levosulpiride use before and after DIP diagnosis showed a clear trend for decreasing utilization (CAGR: −5.4%, −4.3% respectively), whereas the CAGR for itopride and metoclopramide increased by 12.7% and 6.4%, respectively. In 2015, approximately 46.6% (858/1840 persons) of DIP patients were prescribed offending drugs after DIP diagnosis. The most commonly prescribed causative drug after DIP diagnosis was levosulpiride. CONCLUSION: The prevalence of DIP has increased. To prevent or decrease DIP, we suggest that physicians reduce prescriptions of benzamide derivatives that have been most commonly used, and that attempts be made to find other alternative drugs. Additionally, the need for continuing education about offending drugs should be emphasized.
Diagnosis ; Education, Continuing ; Humans ; Insurance, Health ; Korea ; Metoclopramide ; Parkinson Disease ; Parkinsonian Disorders ; Prescriptions ; Prevalence