Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives

Qin, Keke; Huang, Wu; Zhang, Tao; Tang, Shiqi

doi:10.1007/s10462-022-10353-8

Cited by 10 publications

(3 citation statements)

References 295 publications

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“…4) Learning schemes: Extensive research efforts are devoted to utilizing either deep learning or machine learning [46,51] for training models. However, despite the availability of well-known off-the-shelf image-based models, there is very limited work on exploiting transfer learning in BP prediction from rPPG/CPPG signals.…”

Section: ) Skewed Data Histogrammentioning

confidence: 99%

“…Based on the extracted PPG/rPPG, many vital signs can be extracted from the measured blood volume changes in skin vessels. Thanks to publically available biomedical datasets [42][43][44][45], machine/deep learning techniques [46] can be applied for inferring the underlying relationship between the shape of the PPG signal and other vital signs. By turning on our main interest in BP estimation from PPG/rPPG signal, extracting a clean facial rPPG signal (with an accepted signal-to-noise ratio) is an extremely challenging operation.…”

Section: Introductionmentioning

confidence: 99%

“…So, many researchers are working on rPPGbased BP estimation from different perspectives. Despite hundreds of related papers [46,51] and emerging benchmark reporting about rPPG extraction and deep/machine learningbased BP prediction [52][53][54], uncovering new features is a non-stopping operation [55][56][57]. Also, it has many issues and challenges [58].…”

Section: Introductionmentioning

confidence: 99%

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Video-based beat-by-beat blood pressure monitoring via transfer deep-learning

Omer,

Salah,

Hassan

et al. 2024

Appl Intell

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Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, continuously revealing new aspects characterizes that field as a rich research topic. It includes a diverse number of critical points represented in signal denoising, data cleaning, employed features, feature format, feature selection, feature domain, model structure, problem formulation (regression or classification), and model combinations. It is worth noting that extensive research efforts are devoted to utilizing different variants of machine learning and deep learning models while transfer learning is not fully explored yet. So, in this paper, we are introducing a per-beat rPPG-to-BP mapping scheme based on transfer learning. An interesting representation of a 1-D PPG signal as a 2-D image is proposed for enabling powerful off-the-shelf image-based models through transfer learning. It resolves limitations about training data size due to strict data cleaning. Also, it enhances model generalization by exploiting underlying excellent feature extraction. Moreover, non-uniform data distribution (data skewness) is partially resolved by introducing logarithmic transformation. Furthermore, double cleaning is applied for training contact PPG data and testing rPPG beats as well. The quality of the segmented beats is tested by checking some of the related quality metrics. Hence, the prediction reliability is enhanced by excluding deformed beats. Varying rPPG quality is relaxed by selecting beats during intervals of the highest signal strength. Based on the experimental results, the proposed system outperforms the state-of-the-art systems in the sense of mean absolute error (MAE) and standard deviation (STD). STD for the test data is decreased to 5.4782 and 3.8539 for SBP and DBP, respectively. Also, MAE decreased to 2.3453 and 1.6854 for SBP and DBP, respectively. Moreover, the results for BP estimation from real video reveal that the STD reaches 8.027882 and 6.013052 for SBP and DBP, respectively. Also, MAE for the estimated BP from real videos reaches 7.052803 and 5.616028 for SBP and DBP, respectively. Graphical abstract Proposed camera-based blood pressure monitoring system

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Section: ) Skewed Data Histogrammentioning

confidence: 99%