180 related articles for article (PubMed ID: 35965760)
1. Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing.
Sadad T; Bukhari SAC; Munir A; Ghani A; El-Sherbeeny AM; Rauf HT
Comput Intell Neurosci; 2022; 2022():1672677. PubMed ID: 35965760
[TBL] [Abstract][Full Text] [Related]
2. A novel CS-NET architecture based on the unification of CNN, SVM and super-resolution spectrogram to monitor and classify blood pressure using photoplethysmography.
Pankaj ; Kumar A; Komaragiri R; Kumar M
Comput Methods Programs Biomed; 2023 Oct; 240():107716. PubMed ID: 37542944
[TBL] [Abstract][Full Text] [Related]
3. IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud.
Mir MH; Jamwal S; Mehbodniya A; Garg T; Iqbal U; Samori IA
J Healthc Eng; 2022; 2022():7713939. PubMed ID: 35432824
[TBL] [Abstract][Full Text] [Related]
4. Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches.
Khalid SG; Zhang J; Chen F; Zheng D
J Healthc Eng; 2018; 2018():1548647. PubMed ID: 30425819
[TBL] [Abstract][Full Text] [Related]
5. Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study.
Kwon S; Hong J; Choi EK; Lee E; Hostallero DE; Kang WJ; Lee B; Jeong ER; Koo BK; Oh S; Yi Y
JMIR Mhealth Uhealth; 2019 Jun; 7(6):e12770. PubMed ID: 31199302
[TBL] [Abstract][Full Text] [Related]
6. Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study.
Karolcik S; Manginas V; Chanh HQ; Daniels J; Giang NT; Huyen VNT; Hoang MTV; Phan Nguyen Quoc K; Hernandez B; Ming DK; Nguyen Van H; Phan TQ; Trieu HT; Luong Thi Hue T; Holmes AH; Thwaites L; Phan Vinh T; Yacoub S; Georgiou P;
EBioMedicine; 2024 Jun; 104():105164. PubMed ID: 38815363
[TBL] [Abstract][Full Text] [Related]
7. PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM.
Mahardika T NQ; Fuadah YN; Jeong DU; Lim KM
Diagnostics (Basel); 2023 Aug; 13(15):. PubMed ID: 37568929
[TBL] [Abstract][Full Text] [Related]
8. Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification.
Liang Y; Chen Z; Ward R; Elgendi M
Biosensors (Basel); 2018 Oct; 8(4):. PubMed ID: 30373211
[TBL] [Abstract][Full Text] [Related]
9. Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach.
Pal M; Parija S; Mohapatra RK; Mishra S; Rabaan AA; Al Mutair A; Alhumaid S; Al-Tawfiq JA; Dhama K
Biomed Res Int; 2022; 2022():3113119. PubMed ID: 35915793
[TBL] [Abstract][Full Text] [Related]
10. Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments.
Aldhyani THH; Alkahtani H
Sensors (Basel); 2022 Jun; 22(13):. PubMed ID: 35808184
[TBL] [Abstract][Full Text] [Related]
11. Optimized deep neural network models for blood pressure classification using Fourier analysis-based time-frequency spectrogram of photoplethysmography signal.
Pankaj ; Kumar A; Kumar M; Komaragiri R
Biomed Eng Lett; 2023 Nov; 13(4):739-750. PubMed ID: 37872982
[TBL] [Abstract][Full Text] [Related]
12. Robust PPG motion artifact detection using a 1-D convolution neural network.
Goh CH; Tan LK; Lovell NH; Ng SC; Tan MP; Lim E
Comput Methods Programs Biomed; 2020 Nov; 196():105596. PubMed ID: 32580054
[TBL] [Abstract][Full Text] [Related]
13. IoT-based wearable health monitoring device and its validation for potential critical and emergency applications.
Wu JY; Wang Y; Ching CTS; Wang HD; Liao LD
Front Public Health; 2023; 11():1188304. PubMed ID: 37397724
[TBL] [Abstract][Full Text] [Related]
14. Sensor-Location-Specific Joint Acquisition of Peripheral Artery Bioimpedance and Photoplethysmogram for Wearable Applications.
Metshein M; Abdullayev A; Gautier A; Larras B; Frappe A; Cardiff B; Annus P; Land R; Märtens O
Sensors (Basel); 2023 Aug; 23(16):. PubMed ID: 37631647
[TBL] [Abstract][Full Text] [Related]
15. A machine learning approach for hypertension detection based on photoplethysmography and clinical data.
Martinez-Ríos E; Montesinos L; Alfaro-Ponce M
Comput Biol Med; 2022 Jun; 145():105479. PubMed ID: 35398810
[TBL] [Abstract][Full Text] [Related]
16. Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk.
Shah A; Ahirrao S; Pandya S; Kotecha K; Rathod S
Front Public Health; 2021; 9():762303. PubMed ID: 34746087
[TBL] [Abstract][Full Text] [Related]
17. COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology.
Nayan NA; Jie Yi C; Suboh MZ; Mazlan NF; Periyasamy P; Abdul Rahim MYZ; Shah SA
Front Public Health; 2022; 10():920849. PubMed ID: 35928478
[TBL] [Abstract][Full Text] [Related]
18. The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography.
Cano J; Fácila L; Gracia-Baena JM; Zangróniz R; Alcaraz R; Rieta JJ
Biosensors (Basel); 2022 May; 12(5):. PubMed ID: 35624590
[TBL] [Abstract][Full Text] [Related]
19. Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device.
Chiang PY; Chao PC; Tu TY; Kao YH; Yang CY; Tarng DC; Wey CL
Sensors (Basel); 2019 Aug; 19(15):. PubMed ID: 31382707
[TBL] [Abstract][Full Text] [Related]
20. Deep convolutional neural network-based signal quality assessment for photoplethysmogram.
Shin H
Comput Biol Med; 2022 Jun; 145():105430. PubMed ID: 35339844
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]