CVD PREDICTION FROM HRV DERIVED FROM WEARABLE PPG

Nurdaulet Tasmurzayev; Dinara Turmakhanbet; Adilet Kakharov; Mukhamejan Aitkazin; Aliya Baidauletova; Mergul Kozhamberdiyeva

doi:10.26577/jpcsit202542

Авторы

Nurdaulet Tasmurzayev Al Farabi Kazakh National University, Almaty, Kazakhstan https://orcid.org/0000-0003-3039-6715 (неаутентифицированный)
Dinara Turmakhanbet Al Farabi Kazakh National University, Almaty, Kazakhstan https://orcid.org/0009-0004-8388-4979 (неаутентифицированный)
Adilet Kakharov Al Farabi Kazakh National University, Almaty, Kazakhstan https://orcid.org/0009-0005-3612-5678 (неаутентифицированный)
Mukhamejan Aitkazin Al Farabi Kazakh National University, Almaty, Kazakhstan https://orcid.org/0009-0004-0181-7351 (неаутентифицированный)
Aliya Baidauletova Al Farabi Kazakh National University, Almaty, Kazakhstan https://orcid.org/0009-0000-5510-3590 (неаутентифицированный)
Mergul Kozhamberdiyeva Al Farabi Kazakh National University, Almaty, Kazakhstan https://orcid.org/0009-0001-0429-7919 (неаутентифицированный)

DOI:

https://doi.org/10.26577/jpcsit202542

Ключевые слова:

ССЗ, ИБС, ВСР, Машинное обучение, ФПГ, носимый датчик

Аннотация

Сердечно-сосудистые заболевания являются основной причиной смерти в мире; ишемическая болезнь сердца (ИБС) – её наиболее распространённая и летальная форма, что обусловливает необходимость масштабируемого неинвазивного скрининга. Мы проверили, может ли однократная 60-минутная запись фотоплетизмографии (ФПГ) с помощью напальчникового устройства Zhurek отличить здоровый вегетативный контроль от нарушения регуляции, связанного с ИБС. Соответствие трёхканальному холтеровскому мониторированию было клинически приемлемым (ЧСС −0,601 уд./мин; SDNN +33,1 мс; RMSSD −4,8 мс). Были проанализированы сорокачасовые сеансы (20 здоровых людей в возрасте от 18 до 22 лет; 20 пациентов с ИБС, подтверждённой ангиографией) с использованием восьми показателей вариабельности сердечного ритма/демографических характеристик. Тесты Манна–Уитни выявили значимые различия для SDNN, LF, HF, Max_HR, ИМТ и возраста (p < 0,05), а двухкомпонентный PCA (дисперсия 49,5%) разделил когорты без меток. SHAP для модели CatBoost выявил LF и возраст как наиболее значимые положительные факторы, а HF – как защитный. Таким образом, часовая ППГ сохраняет диагностически значимые автономные сигнатуры, позволяя примерно в 24 раза сократить время мониторинга по сравнению с холтеровским мониторированием и поддерживая масштабируемую амбулаторную стратификацию риска ИБС.

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Биографии авторов

Nurdaulet Tasmurzayev, Al Farabi Kazakh National University, Almaty, Kazakhstan

Nurdaulet Tasmurzayev, PhD. Dr. Tasmurzayev is a Research Engineer at DigitAlem LLP (Almaty, Kazakhstan). He received his PhD (Candidate of Technical Sciences) in Intelligent Control Systems (Big Data and Machine Learning) from al-Farabi Kazakh National University in 2025, his M.Sc. in Intelligent Control Systems in 2022, and his B.Sc. in Automation and Control (Information Systems Department) in 2020. He has authored and co-authored 10 peer-reviewed journal and conference papers. Research interests: artificial intelligence and machine learning; intelligent control systems; Internet of Things (including industrial IoT); smart cities; intelligent building systems and automation; big-data analytics for control and monitoring. ORCID iD: 0000-0003-3039-6715.
Dinara Turmakhanbet, Al Farabi Kazakh National University, Almaty, Kazakhstan

Dinara Turmakhanbet is a 2025 B.Sc. graduate in Intelligent Control Systems from al-Farabi Kazakh National University (Almaty, Kazakhstan). Her academic interests include intelligent control systems, the Internet of Things (IoT), and applied machine learning. In this study, she contributed to data collection, literature review, and initial analysis. ORCID iD: 0009-0004-8388-4979.
Adilet Kakharov, Al Farabi Kazakh National University, Almaty, Kazakhstan

Adilet Kakharov is a fourth-year B.Sc. student in Intelligent Control Systems at al-Farabi Kazakh National University (Almaty, Kazakhstan). His academic interests include intelligent control, the applied machine learning and AI. In this study, he assisted with technical support, data processing, and visualization. ORCID iD: 0009-0005-3612-5678.
Mukhamejan Aitkazin, Al Farabi Kazakh National University, Almaty, Kazakhstan

Mukhamejan Aitkazin is a fourth-year B.Sc. student in Intelligent Control Systems at al-Farabi Kazakh National University (Almaty, Kazakhstan). His academic interests include intelligent control, the Internet of Things (IoT), and applied machine learning. In this study, he assisted with technical support, data processing, and visualization. ORCID iD: 0009-0004-0181-7351.
Aliya Baidauletova, Al Farabi Kazakh National University, Almaty, Kazakhstan

Aliya Baidauletova is a Candidate of Medical Sciences and a practicing somnologist and neurologist at al-Farabi Kazakh National University (Almaty, Kazakhstan, baidaulet123@gmail.com). She has extensive clinical and research experience in sleep medicine, neurology, and neurophysiological disorders. Her professional interests include sleep disorders, circadian rhythm regulation, neurological aspects of sleep pathology, and the application of modern diagnostic and monitoring technologies in clinical practice. ORCID iD: 0009-0000-5510-3590.
Mergul Kozhamberdiyeva, Al Farabi Kazakh National University, Almaty, Kazakhstan

Mergul Kozhamberdiyeva is a Candidate of Pedagogical Sciences and a practicing pedagogist at al-Farabi Kazakh National University (Almaty, Kazakhstan, kozhamberdiyeva.m@outlook.com). She has extensive clinical and research experience in sleep medicine, neurology, and neurophysiological disorders. Her professional interests include sleep disorders, circadian rhythm regulation, neurological aspects of sleep pathology, and the application of modern diagnostic and monitoring technologies in clinical practice. ORCID iD: 0009-0001-0429-7919.

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