Методические основы экспериментальных исследований функционирования инфузионных насосов в медицине

Analysis of the test results of infusion pumps that are used for administering solutions to patients in resuscitation units, intensive care units, as well as during transportation was performed in the paper. The process of drug administration in the neurohumoral system is critical, so the problem of determining the dosage accuracy of the solution volumes, administered to a patient is very important. The tests of infusion pumps to determine the dosage accuracy, according to the international standards ISO 606601-2-24 and ISO 606601-1-8 were carried out in the paper. Based on the results, conducted in clinics, the errors of the injected volumes in accordance with existing standards were calculated. As a result of data processing, the incompliance of the current dosage volumes with guidelines was found. To prevent administering a doubtful amount of drug, prediction model using artificial neural networks was developed. This model, together with a computerized information system will help to predict the unauthorized administration of drugs over time and allow the medical staff to prevent a critical situation.В качестве биомедицинской аппаратуры рассматривается инфузионный насос для инфузий. Представлен анализ результатов испытаний инфузионных насосов, оценена погрешность результатов воспроизведения объёма вводимого раствора и скорости его доставки. При проведении испытаний было обнаружено несоответствие заданного объёма вводимого раствора и его нормированного значения. Оценка погрешности результатов испытаний осуществлена в соответствии с международными стандартами.

An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering

Traditional intelligent fault diagnosis methods take advantage of diagnostic expertise but are labor-intensive and time-consuming. Among various unsupervised feature extraction methods, sparse filtering computes fast and has less hyperparameters. However, the standard sparse filtering has poor generalization ability and the extracted features are not so discriminative by only constraining the sparsity of the feature matrix. Therefore, an improved sparse filtering with L1 regularization (L1SF) is proposed to improve the generalization ability by improving the sparsity of the weight matrix, which can extract more discriminative features. Based on Fourier transformation (FFT), L1SF, softmax regression, a new three-stage intelligent fault diagnosis method of rotating machinery is developed. It first transforms time-domain samples into frequency-domain samples by FFT, then extracts features in L1-regularized sparse filtering and finally identifies the health condition in softmax regression. Meanwhile, we propose employing different activation functions in the optimization of L1SF and feedforward for considering their different requirements of the non-saturating and anti-noise properties. Furthermore, the effectiveness of the proposed method is verified by a bearing dataset and a gearbox dataset respectively. Through comparisons with the standard sparse filtering and L2-regularized sparse filtering, the superiority of the proposed method is verified. Finally, an interpretation of the weight matrix is given and two useful sparse properties of weight matrix are defined, which explain the effectiveness of L1SF

Backpropagation-Based Non Linear PCA for Biomedical Applications

Machine learning methodologies such as artificial neural networks (ANN), fuzzy logic or genetic programming, as well as principal component analysis (PCA) and intelligent control have been recently introduced in medicine. ANNs imitate the structure and workings of the human brain by means of mathematical models able to adapt several parameters. ANNs learn the input/output behavior of a system through a supervised or an unsupervised learning algorithm. In this work, we present and demonstrate a new pre-processing algorithm able to improve the performance of an ANN in the processing of biomedical datasets. The algorithm was tested analyzing lung function and fractional exhaled nitric oxide differences in the breath in children with allergic bronchial asthma and in normal population. Classification obtained using non linear PCA based on the new algorithm shows a better precision in separating asthmatic and control subjects