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 "621.38"

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  • Ескіз
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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.
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    Evaluation of the Limitation of Operational Parameters of the IEEE 802.11 ac Network in the 20MHz Channel
    (КПІ ім. Ігоря Сікорського, 2024) Omelianets, O. O.; Lazebnyi, V. S.
    IEEE 802.11 wireless network technologies are widely used to create corporate and personal local networks for data exchange and access to Internet resources. The main principle of operation of IEEE 802.11 networks is the principle of competitive access, according to which all wireless network users have the same access rights to the information transmission environment. This method of access leads to the occurrence of collisions in networks with a large number of users, which complicates the process of network functioning and leads to the degradation of quality indicators. The purpose of the study is to estimate the limit values of the operational characteristics of the IEEE 802.11 ac wireless network in the mode with the highest transmission rate (MCS8) in a frequency channel of 20 MHz with one spatial stream, provided that the network has a significant number of active stations with a saturated load. An alternative model of processes in IEEE 802.11 networks based on the concept of a virtual competitive window is used for research. According to the concept of virtual contention window (VCW), the process of data transmission in a network with competitive access is considered as a quasistationary process. Numerical data were obtained and graphs of channel bandwidth, transmission delay, and delay nonuniformity were given in the presence of one to sixteen active stations with a saturated load in the network, in the case of transmission of frames with a data volume of 512 or 1500 bytes. The maximum possible bandwidth of the channel with a frequency band of 20 MHz (68.387 bit/s) was determined, in the case of using frames with the maximum load (11454 bytes) provided by the standard. Estimated data on the number of collisions occurring in a network with a saturated load and the number of frames transmitted at various stages of channel access are also provided. The frame transmission delay increases almost proportionally to the number of active stations and varies from 0.605 ms to 5.293 ms, in the case of loading all data frames of 512 bytes, and from 0.785 to 6.41 ms, in the case of a load of 1500 bytes, for changes in the number of active stations in the network from 2 to 16. The unevenness of the delay exceeds the average delay and grows non-linearly, in the case of an increase in the number of active stations from 1 to 6 (CWmin=15), and linearly — with a further increase in the number of stations (over 6). The obtained results are useful for reasonable planning of wireless networks and configuration of network equipment parameters.