Regression or Classification? Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications

Schrumpf, Fabian; Rudi, Serdack, Paul; Fuchs, Mirco

doi:10.1109/cvprw56347.2022.00236

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…We also observed consistently better performance when training a personalized model, as the subject-specific instances of rest and activity scenario helped the model calibrate to a specific subject. This in line with previous work [ 6 , 32 ] and confirmed the difficulty of training a robust general BP estimation model. This can additionally be seen in Figs.…”

Section: Resultssupporting

confidence: 92%

“…The errors are fairly normally distributed regardless of the scenario. This is a known problem in literature dealing with BP prediction, where vast majority of cases is centered around the mean value, meaning that the predictor achieves low numerical errors, but is expected to only work well in typical or normal BP ranges [ 6 , 32 ]. We observed consistent performance in our experiments, showing that the approach is robust across a broad range of BP values and hemodynamic states.…”

Section: Resultsmentioning

confidence: 99%

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Generalized channel separation algorithms for accurate camera-based multi-wavelength PTT and BP estimation

Slapničar,

Wang,

Luštrek

2024

Biomed. Opt. Express

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Single-site multi-wavelength (MW) pulse transit time (PTT) measurement was recently proposed using contact sensors with sequential illumination. It leverages different penetration depths of light to measure the traversal of a cardiac pulse between skin layers. This enabled continuous single-site MW blood pressure (BP) monitoring, but faces challenges like subtle skin compression, which importantly influences the PPG morphology and subsequent PTT. We extended this idea to contact-free camera-based sensing and identified the major challenge of color channel overlap, which causes the signals obtained from a consumer RGB camera to be a mixture of responses in different wavelengths, thus not allowing for meaningful PTT measurement. To address this, we propose novel camera-independent data-driven channel separation algorithms based on constrained genetic algorithms. We systematically validated the algorithms on camera recordings of palms and corresponding ground-truth BP measurements of 13 subjects in two different scenarios, rest and activity. We compared the proposed algorithms against established blind source separation methods and against previous camera-specific physics-based method, showing good performance in both PTT reconstruction and BP estimation using a Random Forest regressor. The best-performing algorithm achieved mean absolute errors (MAEs) of 3.48 and 2.61 mmHg for systolic and diastolic BP in a leave-one-subject-out experiment with personalization, solidifying the proposed algorithms as enablers of novel contact-free MW PTT and BP estimation.

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Section: Resultssupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Generalized channel separation algorithms for accurate camera-based multi-wavelength PTT and BP estimation

Slapničar,

Wang,

Luštrek

2024

Biomed. Opt. Express

View full text Add to dashboard Cite

show abstract

“…While pre-trained models can be fine-tuned for efficient performance, it is not always necessary to use highly trained models [ 34 , 35 ]. However, the performance of CNN models may vary based on the hyperparameter values, such as the model depth, optimizer, loss function, and preprocessing steps [ 36 , 37 ]. In addition, it is important to consider the limitations of the current dataset, which contained data on a small number of subjects, and the controlled laboratory environment in which the data were recorded.…”

Section: Discussionmentioning

confidence: 99%

Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter

Trirongjitmoah,

Promking,

Kaewdang

et al. 2024

Heliyon

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“…The impact of data cleaning is highlighted in [86,87]. The problem may be formulated as a regression or classification problem [88]. BP classification can be simply divided into three classes [16,85] namely, normotensive (NT), prehypertensive (PHT), and hypertensive (HT).…”

Section: A Machine Learning and Deep Learningmentioning

confidence: 99%

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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Regression or Classification? Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications

Cited by 8 publications

References 33 publications

Generalized channel separation algorithms for accurate camera-based multi-wavelength PTT and BP estimation

Generalized channel separation algorithms for accurate camera-based multi-wavelength PTT and BP estimation

Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter

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

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