Перегляд за Автор "Popov, A. O."
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Документ Відкритий доступ Designing Minimalistic Powered Arm Orthosis for Brachial Plexus Injuries(КПІ ім. Ігоря Сікорського, 2024) Kotsiubailo, A. V.; Savchuk, A. V.; Omelkyna, D. V.; Shoferystov, S. Ye.; Lavrenko, Ia. I.; Tretiak, I. B.; Yakovenko, S. M.; Lysenko, O. M.; Popov, A. O.Elbow paresis, often resulting from brachial plexus injury, presents a significant challenge in the field of rehabilitation. To address this, we have developed a prototype powered orthosis that utilizes non-invasive surface electromyography (EMG) signals from neck muscles, such as the sternocleidomastoid, for intuitive control. This EMG-driven system allows for the precise manipulation of the elbow joint, covering the full physiological range of motion. The prototype’s design integrates an EMG signal processor with an orthosis action operator, creating a seamless interface between human intent and mechanical action. Healthy participants were able to use neck muscle contractions to control elbow rotation effectively, demonstrating the system’s potential for real-world application. The scaled EMG envelope directly influences the orthosis’s rotational actuator, ensuring responsive and accurate control. Through rigorous sensitivity analysis, we optimized the control algorithm by adjusting EMG window lengths, signal filtering, and thresholding parameters. This optimization process ensures that the system can adapt to individual user needs, providing personalized and efficient control. The real-time control achieved with this prototype marks a significant step forward in the development of biomedical rehabilitation devices. It not only offers a practical solution for those affected by elbow paresis but also lays the groundwork for future advancements in neuromechanical interfaces. Our ongoing research aims to refine this technology further, exploring the integration of signal processing algorithms to predict and adapt to user movements, thereby creating a more natural and intuitive user experience. The ultimate goal is to develop a fully functional orthosis that can be readily implemented in clinical settings, providing a non-invasive, effective solution for elbow rehabilitation.Документ Відкритий доступ 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.Документ Відкритий доступ Method for Blink Detection in Single Channel of Invasive Electromyogram Signal(КПІ ім. Ігоря Сікорського, 2021) Bobrov, A. L.; Borysenko, O. M.; Popov, A. O.Problem statement. Facial nerve damage is the cranial nervous system disorder often leading to facial muscle paralysis, which might be effectively restored using functional electrical stimulation of the fully or partially denervated circular muscle of the eye to achieve muscle contraction to close the eyelids. To control the invasive stimulation system, the automated detection of the blink event in the intact eye is used as a trigger. To achieve this, the new approach to single channel invasive electromyogram (EMG) signal analysis is proposed. Materials and Methods. The combined time-spectral approach to blink detection consists of the two stages, starting from the thresholding of filtered EMG signals in the sliding window, which is followed by comparing the total spectral power in the Fourier domain to minimum and maximum thresholds. If both conditions are met, the EMG in the current window is considered to contain the blink event. In the experiment, the EMG data recorded from the one male adult healthy volunteer is used, the signal contained an acceptable amount of artefacts and was recognized as reflecting the usual EMG. The true positive rate (TPR), positive predictive value (PPV), False Discovery Rate (FDR), and False Negative Rate (FNR) is used as a performance metrics. Results. In the result of applying the proposed blink detection algorithm with 500 ms duration of the time window and 100 ms overlap, the following performance metrics are obtained: TPR=93%, PPV=63%, FDR=7%, FNR=37%. Impact. Acceptable true positive rate of blink detection suggests the method is promising for wider applications in the clinical settings and might be incorporated in the prototypes of implanted systems for facial muscle paralysis restoration using functional electrical stimulation for further development.Документ Відкритий доступ Optimal Bin Number Selection for Mutual Information Calculation Between EEG and Cardiorhythmogram Signals(НТУУ "КПІ", 2014) Zhukov, M. A.; Popov, A. O.; Kharytonov, V. I.; Chaikovsky, I. A.; Жуков, М. А.; Попов, А. О.; Харитонов, В. І.; Чайковський, І. А.; Жуков, М. А.; Попов, А. А.; Харитонов, В. И.; Чайковский, И. А.Документ Відкритий доступ Вибiр оптимального порядку мультиварiативних авторегресiйних моделей електроенцефалограм при епілепсії(КПІ ім. Ігоря Сікорського, 2018) Котючий, I. В.; Попов, А. О.; Харитонов, В. I.; Kotiuchyi, I. V.; Popov, A. O.; Kharytonov, V. I.; Котючий, И. В.; Попов, А. О.; Харитонов, В. И.Документ Відкритий доступ Метод обробки трендiв бiологiчних сигналiв на основi вейвлет аналiзу(КПІ ім. Ігоря Сікорського, 2017) Бодiловський, О. К.; Попов, А. О.; Bodilovskyi, O. K.; Popov, A. O.; Бодиловский, О. К.; Попов, А. О.Документ Відкритий доступ Порівняння результатів прогнозування епілептичних нападів при використанні різних схем відведення ЕЕГ(КПІ ім. Ігоря Сікорського, 2017) Панічев, О. Ю.; Попов, А. О.; Харитонов, В. І.; Panichev, O. Yu.; Popov, A. O.; Kharytonov, V. I.; Паничев, О. Ю.; Попов, А. А.; Харитонов, В. И.Документ Відкритий доступ Реография с возможностью определения разности фаз(НТУУ "КПИ", 2013) Попов, А. А.; Чугуй, А. М.; Попов, А. О.; Чугуй, О. М.; Popov, A. O.; Chugui, O. M.