TY - JOUR A1 - Chumachenko, Dmytro A1 - Butkevych, Mykola A1 - Lode, Daniel A1 - Frohme, Marcus A1 - Schmailzl, Kurt J. G. A1 - Nechyporenko, Alina T1 - Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data JF - Sensors N2 - Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world’s population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks. KW - myocardial infraction KW - heart rate variability KW - 10-second heart rate variability KW - diagnostics KW - machine learning KW - k-nearest neighbor classifier KW - radial basis function KW - decision tree KW - random forest Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-16521 SN - 1424-8220 VL - 22 IS - 18 PB - MDPI ER - TY - JOUR A1 - Pfeil, Juliane A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Hufert, Frank T. A1 - Schulze, Katja T1 - Examination of blood samples using deep learning and mobile microscopy JF - BMC Bioinformatics N2 - Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards. Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-15802 SN - 1471-2105 VL - 23 PB - BioMed Central ER - TY - CHAP A1 - Nechyporenko, Alina A1 - Hubarenko, Yevhen A1 - Hubarenko, Maryna A1 - Kalnytska, Violeta A1 - Alekseeva, Victoriia A1 - Gargin, Vitaliy ED - Shakhovska, Nataliya ED - Chrétien, Stéphane ED - Izonin, Ivan ED - Campos, Jaime T1 - Automatic Creation of Masks for Marking Histological Images of the Epithelium of the Paranasal Sinuses T2 - Proceedings of the 5th International Conference on Informatics & Data-Driven Medicine, Lyon, France, November 18 - 20, 2022 N2 - The article discusses the approach to solving the problem of reducing time spent on the preparation of medical images for teaching neural networks, by reducing the time of creating masks for images. The task is considered on the example of processing images of the mucous membrane of the paranasal sinus. The specifics of the task did not allow effectively using existing software solutions. During the study, a software solution was proposed, which made it possible to radically reduce the time of creating masks for images. The article also analyzes the shortcomings of the automated creation of masks, as well as the directions of their solution. The loss of time due to the adjustment of the color palette can be reduced even more to 1-2 minutes, the average deviation is 7.61%. KW - neural network KW - mask KW - microscopic image KW - epithelium KW - inflammatory change Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19024 UR - https://ceur-ws.org/Vol-3302/paper6.pdf VL - 3302 SP - 108 EP - 119 PB - RWTH Aachen ER - TY - CHAP A1 - Chumachenko, Dmytro A1 - Nechyporenko, Alina A1 - Frohme, Marcus ED - Shakhovska, Nataliya ED - Chrétien, Stéphane ED - Izonin, Ivan ED - Campos, Jaime T1 - Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning T2 - Proceedings of the 5th International Conference on Informatics & Data-Driven Medicine, Lyon, France, November 18 - 20, 2022 N2 - The new coronavirus COVID-19 has been spreading worldwide for almost three years. The global community has developed effective measures to contain and control the pandemic. However, new factors are emerging that are driving the dynamics of COVID-19. One of these factors was the escalation of Russia's war in Ukraine. This study aims to test the hypothesis of the influence of migration flows caused by the Russian war in Ukraine on the dynamics of the epidemic process in Germany. For this, a model of the COVID-19 epidemic process was built based on the polynomial regression method. The model's adequacy was tested 30 days before the start of the escalation of the Russian war in Ukraine. To assess the impact of the war on the dynamics of COVID-19, the model was used to calculate the forecast of cumulative new and fatal cases of COVID-19 in Germany in the first 30 days after the start of the escalation of the Russian war in Ukraine. Modeling showed that migration flows from Ukraine are not a critical factor in the growth of the dynamics of the incidence of COVID-19 in Germany, but they influenced the number of cases. The next stage of the study is the development of more complex models for a detailed analysis of population dynamics, identifying factors influencing the epidemic process in the context of the Russian war in Ukraine, and assessing their information content. KW - epidemic model KW - machine learning KW - polynomial regression KW - war KW - COVID-19 KW - infectious disease simulation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19074 UR - https://ceur-ws.org/Vol-3302/short4.pdf VL - 3302 SP - 78 EP - 85 PB - RWTH Aachen ER -