Evaluation for Parkinsonian Bradykinesia by deep learning modeling of kinematic parameters.

A wearable sensor system is available for monitoring of bradykinesia in patients with Parkinson's disease (PD), however, it remains unclear whether kinematic parameters would reflect clinical severity of PD, or would help clinical diagnosis of physicians. The present study investigated whether the classification model using kinematic parameters from the wearable sensor may show accordance with clinical rating and diagnosis in PD patients. Using the Inertial Measurement Units (IMU) sensor, we measured the movement of finger tapping (FT), hand movements (HM), and rapid alternating movements (RA) in 25 PD patients and 21 healthy controls. Through the analysis of the measured signal, 11 objective features were derived. In addition, a clinician who specializes in movement disorders viewed the test video and evaluated each of the Unified Parkinson's Disease Rating Scale (UPDRS) scores. In all items of FT, HM, RA, the correlation between the linear regression score obtained through objective features (angle, period, coefficient variances for angle and period, change rates of angle and period, angular velocity, total angle, frequency, magnitude, and frequency × magnitude) and the clinician's UPDRS score was analyzed, and there was a significant correlation (rho > 0.7, p < 0.001). PD patients and controls were classified by deep learning using objective features. As a result, it showed a high performance with an area under the curve (AUC) about as high as 0.9 (FT Total = 0.950, HM Total = 0.889, RA Total = 0.888, ALL Total = 0.926. This showed similar performance to the classification result of binary logistic regression and neurologist, and significantly higher than that of family medicine specialists. Our results suggest that the deep learning model using objective features from the IMU sensor can be usefully used to identify and evaluate bradykinesia, especially for general physicians not specializing in neurology.

An equivalent circuit based electro-vibro-acoustic model of a cylindrical transducer array.

Cylindrical transducer arrays are used in many applications such as SONAR, depth sounding, ultrasound imaging, etc. This study developed the equivalent circuit (EC)-based-model of a cylindrical array with theoretical acoustic coupling. The electro-mechanical-acoustic coupling impedance matrix was first constructed to determine the response of the array. The electrical and mechanical impedances of the individual transducers were obtained by the EC model. The acoustic radiation impedances were obtained by the theoretical model. The cylindrical array is modeled by coupling the EC model and acoustic radiation impedances. The acoustic transfer matrix was then constructed using the theoretical method to determine both the far-field and near-field acoustic responses. The characteristics of the transducer array were represented with the electrical admittance, velocity response as a function of the voltage, transmit voltage response, beam pattern, and normalized pressure curve. To verify the proposed model, the analysis results were successfully compared to those of the fully coupled finite element model. Due to its high accuracy and computational efficiency, the proposed EC-based-model is expected to be useful for the conceptual design stage, which requires frequent design changes.

Wave characteristics of a cylinder with periodic ribs.

This paper deals with wave characteristics of a cylinder with periodic ribs. The equation of motion of the stiffened cylinder is first derived using a receptance coupling method. The dispersion diagrams of the stiffened cylinder are then obtained in order to figure out the effects of the ribs on the wave propagation. Due to the effect of the ribs, the dispersion curves are found to be repeated along the axis of the wavenumber with the repetition period of 2π/d, where d is the rib distance. Also, dispersion curves are found to show a pass and stop band of the waves. The stop bands appeared at the wavenumber of half of the wavenumber periodicity. The stop band becomes wider as the increase of the circumferential order. The waves in the pass bands are propagating well through the ribs without decay. In contrast, the waves in the stop bands are not propagating, but decaying the magnitudes. The decay of the responses in the stop band increases as the circumferential order increase. The change of the rib stiffness causes the cut-on frequency to change and the modal order to jumble. The change of the rib stiffness also leads to generate a wave whose phase velocity is positive, while group velocity is negative.

Two-stage binary classifier for neuromuscular disorders using surface electromyography feature extraction and selection.

If surface electromyography (sEMG) can be used to determine neuromuscular disorders, it can diagnose conditions more easily than needle electromyography. In this study, sEMG during maximum voluntary isometric contraction and repetitive exercise was measured, and normal, myopathy, and neuropathy were classified with high accuracy using these signals. First, a two-stage binary classifier model was constructed to classify the patient group and the normal group and categorize the cases assigned to the patient group into myopathy and neuropathy groups. To this end, features related to muscle activity and muscle fatigue were extracted using activity analysis and frequency analysis of the sEMG signal. Since the features for high performance are different for each classifier, the features with statistical differences in the data of each class were selected for each classifier. The selected features and a two-stage binary classifier were distinguished with an accuracy of 86.9%. This shows an accuracy higher than 82.3%, which was found for the two-stage binary classifier without feature selection and 73.9% of the multi-classifier. Through this, the possibility of using sEMG to diagnose neuromuscular disorders was confirmed.