Мікросистеми, Електроніка та Акустика: науково-технічний журнал, Т. 29, № 1(126)

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https://ela.kpi.ua/handle/123456789/66895

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Перегляд Мікросистеми, Електроніка та Акустика: науково-технічний журнал, Т. 29, № 1(126) за Ключові слова "classification"

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    Application of k-Nearest Neighbors Method for Drug Concentraiton and Cardiotoxicity Classification Using Extracellular Field Potentials and Reconstructed Action Potentials of Cardiac Cells
    (КПІ ім. Ігоря Сікорського, 2024) Shpotak, M. O.; Ivanushkina, N. H.
    Micro-electrode array (MEA) systems are important for measuring extracellular field potentials (FP) of cardiac cells, which is a crucial step in cardiotoxicity assessment. However, without modification, the MEA system is only capable of recording FPs. This limits the number of parameters for cardiotoxicity assessment only to FP parameters, while the action potential (AP) parameters remain unused. To address this issue the MEA systems are often modified to use electroor optoporation to record the local extracellular APs (LEAPs), which allows to reliably quantify the AP morphology. As an alternative to MEA modification and cell membrane stimulation the AP can be reconstructed mathematically.This study explores how using additional parameters from reconstructed action potentials (RAPs), derived from FPs, can improve the accuracy of k-NN machine learning models for drug concentration and potential cardiotoxicity classification. The k-NN classifier was trained using combinations of FP and RAP parameters. The k-NN models were evaluated using five-fold stratified cross-validation and cross-channel validation. Their performances were compared using error rate, macro precision, macro recall and macro F1 score accuracy metrics. The results indicated that ncorporating RAP parameters into the feature set increased the F1 score of k-NN model for DMSO concentration classification by up to 10.78% compared to the training set with only FP features.
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    Determination of Signs of Sleep Apnea Using Machine Learning Methods in Combination with Reducing the Dimensionality of Heart Rate Variability Features
    (КПІ ім. Ігоря Сікорського, 2024) Samsonenko, A. S.; Popov, A. O.
    Obstructive Sleep Apnea Syndrome (OSAS) is a clinically significant disorder characterized by recurrent episodes of upper airway obstruction, manifesting as either apnea or hypopnea, predominantly occurring at the pharyngeal level. Despite the preservation of respiratory muscle function during these episodes, OSAS poses considerable health risks, including cardiovascular complications and cognitive impairment. In recent years, a growing body of literature has explored novel methodologies to discern and diagnose OSAS, with a particular focus on cardiac activity analysis through Heart Rate Variability (HRV). This study contributes to the existing literature by conducting a comprehensive HRV analysis aimed at identifying indicative patterns of sleep apnea. The analysis incorporates diverse parameters within both time and frequency domains, facilitating a nuanced understanding of the complex interplay between cardiac dynamics and respiratory disruptions during sleep. In an effort to enhance the interpretability of the data, various scaling and dimensionality reduction techniques, such as Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), were applied. The dataset utilized in this investigation comprises records from 70 patients, sourced from the Apnea-ECG Database on the Physionet platform. To discern the optimal classification model, several machine learning algorithms were employed after the dimensionality reduction, including k-Nearest Neighbors (k-NN), logistic regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting. Intriguingly, the results demonstrate a remarkable 100% accuracy across all classifiers when utilizing the UMAP dimensionality reduction method. A distinctive feature of the proposed methodology lies in its amalgamation of machine learning techniques with HRV parameters post-dimensionality reduction. This approach not only enhances the interpretability of the complex physiological data but also underscores the potential applicability of the developed model in real-world scenarios for the detection of OSAS. The robustness of the proposed approach, as evidenced by its high accuracy rates, positions it as a promising tool for advancing diagnostic capabilities in the realm of sleep medicine. Future research endeavors may further refine and validate this methodology, paving the way for its integration into clinical practice and contributing to the broader landscape of sleep disorder diagnostics.