No abstract available.
Diagnosis* ; Parkinson Disease*

No abstract available.
Diagnosis, Differential* ; Parkinson Disease, Secondary*

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

Humans ; Parkinson Disease/diagnosis* ; Machine Learning

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

BACKGROUND AND PURPOSE: Cognitive and gait disturbance are common symptoms in Parkinson's disease (PD). Although the relationship between cognitive impairment and gait dysfunction in PD has been suggested, specific gait patterns according to cognition are not fully demonstrated yet. Therefore, the aim of this study was to investigate gait patterns in PD patients with or without cognitive impairment. METHODS: We studied 86 patients at an average of 4.8 years after diagnosis of PD. Cognitive impairment was defined as scoring 1.5 standard deviation below age- and education-specific means on the Korean version of the Mini-Mental State Examination (K-MMSE). Three-dimensional gait analysis was conducted for all patients and quantified gait parameters of temporal-spatial data were used. Relationships among cognition, demographic characteristics, clinical features, and gait pattern were evaluated. RESULTS: Cognitive impairment was observed in 41 (47.7%) patients. Compared to patients without cognitive impairment, patients with cognitive impairment displayed reduced gait speed, step length, and stride length. Among K-MMSE subcategories, “registration,”“attention/calculation,” and “visuospatial function” were significantly associated with speed, step length, and stride length. However, age, disease duration, Hoehn-Yahr (HY) stage, or Unified Parkinson's Disease Rating Scale (UPDRS) motor score was not significantly related to any gait analysis parameter. CONCLUSIONS: Our present study shows that cognitive impairment is associated with slow and short-stepped gait regardless of HY stage or UPDRS motor score, suggesting that cognitive impairment may serve as a surrogate marker of gait disturbance or fall in PD patients.
Biomarkers ; Cognition ; Cognition Disorders* ; Diagnosis ; Gait* ; Humans ; Parkinson Disease*

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

Parkinson's disease is a chronic debilitating condition, the prevalence of which has not been fully established in Papua New Guinea. We describe a cluster of 9 cases of the disease, restricted to two generations of one family, and the key ideas and beliefs held within the family regarding disease aetiology. Many of the concerns and feelings of guilt expressed by family members were alleviated following supportive listening and culturally appropriate counselling, explanation and advice from trained health professionals assisted by bilingual family facilitators. This is the first time that such a family has been reported in Papua New Guinea and may warrant more detailed assessment. Addressing patient and community perceptions of disease aetiology should be at the heart of health promotion initiatives and counselling.
Cluster Analysis ; Developing Countries ; Female ; Health Knowledge, Attitudes, Practice ; New Guinea ; epidemiology ; Parkinson Disease - diagnosis ; Parkinson Disease - epidemiology*

Age Factors ; Disease Progression ; Humans ; Parkinson Disease ; complications ; diagnosis ; drug therapy

Tremor is defined as involuntary, rhythmic, and sinusoidal movement. The rate, location, amplitude, and constancy vary depending on the specific type of tremor and its severity. Etiologies and treatment of tremors differ according to the type of tremor. It is helpful to determine whether the tremor is present at rest, with posture-holding, with action or with intention maneuvers. Rest tremor is most typically present in patients with Parkinson's disease. Physiologic tremors and essential tremors are common forms of postural tremor. Intention tremor is typically present in cerebellar lesions. Associated neurological symptoms and signs are also helpful for differential diagnosis. Not all patients with hand tremor have Parkinson's disease. Rest tremor, bradykinesia, rigidity, and loss of postural reflex are cardinal signs of Parkinson's disease. Careful observation of the patient is the key point of diagnosis in patients with tremor.
Diagnosis ; Diagnosis, Differential ; Essential Tremor ; Hand* ; Humans ; Hypokinesia ; Intention ; Parkinson Disease* ; Reflex ; Tremor*