Machine learning and electrocardiography signal-based minimum calculation time detection for blood pressure detection

Basit Öğe Kaydı
dc.authorid0000-0001-8461-1404en_US
dc.authorid0000-0003-3067-4770en_US
dc.authorid0000-0002-0636-8645en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-7201-6963en_US
dc.contributor.authorNour, Majid
dc.contributor.authorKandaz, Derya
dc.contributor.authorUçar, Muhammed Kürşad
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlhudhaif, Adi
dc.date.accessioned2023-08-14T08:42:03Z
dc.date.available2023-08-14T08:42:03Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractObjective. Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time required for calculating systolic and diastolic blood pressure based on the Electrocardiography (ECG) signal. Methodology. The study includes ECG recordings of five individuals taken from the IEEE database, measured during daily activity. For the study, each signal was divided into epochs of 2-4-6-8-10-12-14-16-18-20 seconds. Twenty-five features were extracted from each epoched signal. The dimension of the dataset was reduced by using Spearman's feature selection algorithm. Analysis based on metrics was carried out by applying machine learning algorithms to the obtained dataset. Gaussian process regression exponential (GPR) machine learning algorithm was preferred because it is easy to integrate into embedded systems. Results. The MAPE estimation performance values for diastolic and systolic blood pressure values for 16-second epochs were 2.44 mmHg and 1.92 mmHg, respectively. Conclusion. According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.en_US
dc.identifier.citationNour, M., Kandaz, D., Ucar, M. K., Polat, K., & Alhudhaif, A. (2022). Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection. Computational and Mathematical Methods in Medicine, 2022.en_US
dc.identifier.doi10.1155/2022/5714454
dc.identifier.endpage32en_US
dc.identifier.issn1748-670X
dc.identifier.issn1748-6718
dc.identifier.pmid35903432en_US
dc.identifier.scopus2-s2.0-85135332474en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1155/2022/5714454
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11508
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000884322600007en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofComputational and Mathematical Methods in Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPulse Transit-Timeen_US
dc.subjectMeasuring Devicesen_US
dc.subjectHypertensionen_US
dc.subjectPreventionen_US
dc.subjectAdultsen_US
dc.subjectPhotoplethysmographyen_US
dc.titleMachine learning and electrocardiography signal-based minimum calculation time detection for blood pressure detectionen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
majid-nour.pdf
Boyut:
4.09 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin/Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu