DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model

• Published in: Medical & biological engineering & computing Vol. 62; no. 12; pp. 3687 - 3708
• Main Authors: Raju, S. M. Taslim Uddin, Dipto, Safin Ahmed, Hossain, Md Imran, Chowdhury, Md. Abu Shahid, Haque, Fabliha, Nashrah, Ayesha Tun, Nishan, Araf, Khan, Md Mahamudul Hasan, Hashem, M. M. A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V
• Subjects: Adult; Algorithms; Artificial neural networks; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Blood pressure; Blood Pressure - physiology; Blood Pressure Determination - instrumentation; Blood Pressure Determination - methods; Computer Applications; Deep Learning; diastolic blood pressure; domain; Ensemble learning; Feature extraction; Feature selection; Female; health services; Human Physiology; Humans; Hypertension; Imaging; instrumentation; Machine learning; Male; Middle Aged; Neural networks; Neural Networks, Computer; Noise reduction; Original Article; Photoplethysmography - instrumentation; Photoplethysmography - methods; Pressure measurement; Pulse Wave Analysis - instrumentation; Pulse Wave Analysis - methods; Radiology; Signal processing; Signal Processing, Computer-Assisted; systolic blood pressure; Telemedicine; Young Adult; Feature extraction; Blood pressure; Deep neural networks; Feature selection algorithm; Photoplethysmogram
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-024-03157-1

Continuous blood pressure (BP) provides essential information for monitoring one’s health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination ( R 2 ) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP. Graphical abstract