A framework for automatic heart sound analysis without segmentation

<p>Abstract</p> <p>Background</p> <p>A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs.</p> <p>Method</p> <p>Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors.</p> <p>Result</p> <p>The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration.</p> <p>Conclusion</p> <p>The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.</p

Test system for defect detection in cementitious material with artificial neural network

This paper introduces a newly developed test system for defect detection, classification of number of defects andidentification of defect materials in cement-based products. With the system, the pattern of ultrasonic waves for each case ofspecimen can be obtained from direct and indirect measurements. The machine learning algorithm called artificial neuralnetwork classifier with back-propagation model is employed for classification and verification of the wave patterns obtainedfrom different specimens. By applying the system, the presence or absence of a defect in mortar can be identified. Moreover,the system is applied to identify the number and materials of defects inside the mortar. The methodology is explained and theclassification results are discussed. The effectiveness of the developed test system is evaluated. Comparison of the classification results between different input features with different number of training sets is demonstrated. The results show that thistechnique based on pattern recognition has a potential for practical inspection of concrete structures

Collaborative Nonlinear Model-Predictive Motion Planning and Control of Mobile Transport Robots for a Highly Flexible Production System

This study is based on a new approach for an advanced microproduction system or highly flexible production systems where all necessary production and assembly processes are connected in a very flexible way using autonomous mobile transport and handling robots. Each robot has to follow its planned paths while avoiding collisions with other robots. In addition, problem-specific constraints for a defined microproduction system, such as limitations of the velocity and accelerations of the robots, have to be fulfilled. This paper focuses on a two-level model predictive optimizing approach. On a global long-term level, simple dynamic models of the robots are used to compute optimal paths under differential constraints where a safety distance between all robots is achieved. Since many uncertainties and unforeseen events could occur, all robots also use a nonlinear model predictive control approach on a local real-time level. This control approach solves the path following and the collision avoidance problems in parallel, while also taking into account differential constraints of the single robots

A hybrid particle swarm optimization-SVM classification for automatic cardiac auscultation

Cardiac auscultation is a method for a doctor to listen to heart sounds, using a stethoscope, for examining the condition of the heart. Automatic cardiac auscultation with machine learning is a promising technique to classify heart conditions without need of doctors or expertise. In this paper, we develop a classification model based on support vector machine (SVM) and particle swarm optimization (PSO) for an automatic cardiac auscultation system. The model consists of two parts: heart sound signal processing part and a proposed PSO for weighted SVM (WSVM) classifier part. In this method, the PSO takes into account the degree of importance for each feature extracted from wavelet packet (WP) decomposition. Then, by using principle component analysis (PCA), the features can be selected. The PSO technique is used to assign diverse weights to different features for the WSVM classifier. Experimental results show that both continuous and binary PSO-WSVM models achieve better classification accuracy on the heart sound samples, by reducing system false negatives (FNs), compared to traditional SVM and genetic algorithm (GA) based SVM

Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities

Purpose - The purpose of the proposed research methodology is to control the trajectory tracking of EDRM and also to cancel out the effect of no-smooth nonlinearities, which affect the system performance badly. Design/methodology/approach - Robust adaptive neural network (RANN)-based backstepping control design methodology is presented in this paper. The proposed design methodology improves the trajectory tracking and running mean error. Findings - The running mean error results show that the convergence of the proposed RANN-based backstepping technique is very fast as compare to the conventional PD control and due to this proposed control technique, the EDRM follows its desired trajectory perfectly. Practical implications - The EDRM trajectory tracking performance increases which leads to a better working position of EDRM. Originality/value - The originality of this research article is 93 per cent