A new system for cuffless blood pressure measurement

Ankishan, H.; Ari, Fikret; Celik, Haydar; Frederick, Blaise B.; Hocke, Lia M.; Erdogan, Sinem Burcu; Unal, Mehmet Altay

doi:10.1016/j.apacoust.2023.109615

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…Rong et al [12] developed a deep learning model consisting of a signal pre-processing module and an MTFF neural network module. H. Ankishan [13] developed a mathematical model and system that uses physiological signals and voice messages to calculate systolic blood pressure (SBP) and diastolic blood pressure (DBP). Hu et al [14] proposed a global hybrid multi-scale convolutional network to obtain multi-scale information by summing three branches over attention weights on multiple scales.…”

Section: Introductionmentioning

confidence: 99%

ACNN-BiLSTM: A Deep Learning Approach for Continuous Noninvasive Blood Pressure Measurement Using Multi-Wavelength PPG Fusion

Cui,

Dong,

Zhuang

et al. 2024

Bioengineering

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As an essential physiological indicator within the human body, noninvasive continuous blood pressure (BP) measurement is critical in the prevention and treatment of cardiovascular disease. However, traditional methods of blood pressure prediction using a single-wavelength Photoplethysmographic (PPG) have bottlenecks in further improving BP prediction accuracy, which limits their development in clinical application and dissemination. To this end, this study proposed a method to fuse a four-wavelength PPG and a BP prediction model based on the attention mechanism of a convolutional neural network and bidirectional long- and short-term memory (ACNN-BiLSTM). The effectiveness of a multi-wavelength PPG fusion method for blood pressure prediction was evaluated by processing PPG signals from 162 volunteers. The study compared the performance of the PPG signals with different individual wavelengths and using a multi-wavelength PPG fusion method in blood pressure prediction, assessed using mean absolute error (MAE), root mean squared error (RMSE) and AAMI-related criteria. The experimental results showed that the ACNN-BiLSTM model achieved a better MAE ± RMSE for a systolic BP and diastolic BP of 1.67 ± 5.28 and 1.15 ± 2.53 mmHg, respectively, when using the multi-wavelength PPG fusion method. As a result, the ACNN-BiLSTM blood pressure model based on multi-wavelength PPG fusion could be considered a promising method for noninvasive continuous BP measurement.

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Section: Introductionmentioning

confidence: 99%