CVD PREDICTION FROM HRV DERIVED FROM WEARABLE PPG

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

doi:10.26577/jpcsit202542

Автор(и)

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

DOI:

https://doi.org/10.26577/jpcsit202542

Ключові слова:

CVD, ИЖА, ЖСЖВ, машиналық оқыту, ФПГ, киілетін сенсор

Анотація

Жүрек-қантамыр аурулары дүние жүзінде өлімнің басты себебі болып табылады; Жүректің ишемиялық ауруы (ЖИА) оның ең таралған және өлімге әкелетін түрі болып табылады, масштабталатын, инвазивті емес скринингті ынталандырады. Біз «Жүрек» саусақ ұшының 60 минуттық фотоплетизмографиясы (PPG) жазбасы сау вегетативтік бақылауды IHD-мен байланысты реттелуден ажырата алатынын тексердік. Үш жетекші Холтер анықтамасымен келісім клиникалық тұрғыдан қолайлы болды (HR −0,601 б/мин; SDNN +33,1 мс; RMSSD −4,8 мс). Қырық сағаттық сеанстар (20 сау, 18-22 жас; 20 ангиографиямен расталған IHD) сегіз HRV/демографиялық ерекшеліктерді пайдалана отырып талданды. Mann-Whitney сынақтары SDNN, LF, HF, Max_HR, BMI және жас (p<0,05) және екі компонентті PCA (49,5% дисперсия) белгілері жоқ бөлінген когорттар үшін айтарлықтай айырмашылықтарды көрсетті. CatBoost моделіне арналған SHAP ең күшті оң үлес қосушылар ретінде LF мен жасты, ал қорғаныш ретінде ЖЖ-ны ерекшелеген. Осылайша, бір сағаттық PPG диагностикалық тұрғыдан пайдалы вегетативті қолтаңбаларды сақтайды, бұл Холтерге қарағанда ~24 × қысқа бақылауға мүмкіндік береді және масштабталатын амбулаторлық IHD тәуекел стратификациясын қолдайды.

Завантажити

Дані для завантаження поки недоступні.

Біографії авторів

автор Nurdaulet Tasmurzayev, афіліація 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.
автор Dinara Turmakhanbet, афіліація 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.
автор Adilet Kakharov, афіліація 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.
автор Mukhamejan Aitkazin, афіліація 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.
автор Aliya Baidauletova, афіліація 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.
автор Mergul Kozhamberdiyeva, афіліація 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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