Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years,Parkinson’s Disease(PD)as a progressive syndrome of the nervous system has become highly prevalent worldwide.In this study,a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network(MLP)with the Biogeography-based Optimization(BBO)to classify PD based on a series of biomedical voice measurements.BBO is employed to determine the optimal MLP parameters and boost prediction accuracy.The inputs comprised of 22 biomedical voice measurements.The proposed approach detects two PD statuses:0-disease status and 1-good control status.The performance of proposed methods compared with PSO,GA,ACO and ES method.The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection.The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms,and consequently,it served as a promising new robust tool with excellent PD diagnosis performance.

A bionic approach for the mechanical and electrical decoupling of an MEMS capacitive sensor in ultralow force measurement

Capacitive sensors are efficient tools for biophysical force measurement,which is essential for the exploration of cellular behavior.However,attention has been rarely given on the influences of external mechanical and internal electrical interferences on capacitive sensors.In this work,a bionic swallow structure design norm was developed for mechanical decoupling,and the influences of structural parameters on mechanical behavior were fully analyzed and optimized.A bionic feather comb distribution strategy and a portable readout circuit were proposed for eliminating electrostatic interferences.Electrostatic instability was evaluated,and electrostatic decoupling performance was verified on the basis of a novel measurement method utilizing four complementary comb arrays and applicationspecific integrated circuit readouts.An electrostatic pulling experiment showed that the bionic swallow structure hardly moved by 0.770 nm,and the measurement error was less than 0.009% for the area-variant sensor and 1.118% for the gap-variant sensor,which can be easily compensated in readouts.The proposed sensor also exhibited high resistance against electrostatic rotation,and the resulting measurement error dropped below 0.751%.The rotation interferences were less than 0.330 nm and(1.829×10^(-7))°,which were 35 times smaller than those of the traditional differential one.Based on the proposed bionic decoupling method,the fabricated sensor exhibited overwhelming capacitive sensitivity values of 7.078 and 1.473 pF/μm for gap-variant and area-variant devices,respectively,which were the highest among the current devices.High immunity to mechanical disturbances was maintained simultaneously,i.e.,less than 0.369% and 0.058% of the sensor outputs for the gap-variant and area-variant devices,respectively,indicating its great performance improvements over existing devices and feasibility in ultralow biomedical force measurement.