Optimal Design Of Cardiopulmonary Resuscitation Training Model And Effort On Quality Evaluation Of Chest Compression Operation

Cardiac Arrest(CA)has become one of the major medical problems that threaten human life and health.Correspondingly,cardiopulmonary resuscitation(CPR)is the only effective way that is used to rescue patients with CA and helps the blood return to spontaneous circulation.CPR training model is widely used for CPR training.However,there are shortcomings with the current CPR training model in the thoracic simulation and the quality evaluation of chest compression operation.Therefore,the improvement of CPR skills is unsatisfactory.To solve the problems above,on the basis of the current CPR training model,a novel mechanical simulation structure is designed based on the human thorax.Meanwhile,referring to the common methods in the field of quality evaluation of manual operation,the quality evaluation of chest compression operation based on the signal similarity measurement is proposed in this paper.These efforts are of great significance to improve the current CPR teaching equipment and better instruct the medical staff to carry out CPR training.The main contents of the paper are as follows:1.Based on the geometrical characteristics of the human thorax,the three-dimensional design of the model is completed in the CAD software SolidWorks.The mechanical property of the model is analyzed in the CAE software ANSYS and the fatigue life of the model is predicted in the ANSYS Fatigue Tool.To ensure that the mechanical property of the model designed in this paper is equivalent to the human thorax and the fatigue life meets the demand of practical clinical teaching,a design scheme with thorax-spring combination structure is established.In this paper,the optimized CPR training model is composed of steel straps simulating the human thorax with the thickness of 0.5mm and three compression springs.2.Pressure signals and compression depth signals during the chest compression process are acquired,and the signal processing method,FIR digital low-pass filter,is used to enhance signal-to-noise ratio.Five conventional features including the maximum compression depth,frequency,position,chest recoil ratio,compression duty cycle,and four mechanical features including maximum acceleration,average acceleration,maximum pressure,average pressure during compression process are identified as the influencing factors of the quality of chest compressions.The boundary value seeking algorithm based on wavelet transform is introduced to obtain the pressing wave's peaks,and in particularly,calculate the conventional and mechanical features.3.Through features extraction,the whole chest compression operation can be described with multivariate time series composed of time domain features.The similarity measurement algorithm is designed based on Cosine method and principal component analysis(PCA).The similarity between trainee's chest compression signals and high-quality chest compression signals is used for evaluating the quality of the trainee's operation.The similarity of chest compression operation among several groups is calculated.The effectiveness of the method is verified by results.