Key result
Deep learning models, particularly GRU and Bi-LSTM using time-domain features, achieved better performance than traditional machine learning methods for blood pressure estimation using PPG signals.
Why the study?
Blood pressure cuffs are impractical for continuous monitoring, making biosignal-based blood pressure estimation necessary yet challenging.
Which feature extraction techniques and machine learning algorithms perform best for blood pressure estimation using photoplethysmography (PPG) signals?
Which feature extraction techniques and machine learning algorithms perform best for blood pressure estimation using photoplethysmography (PPG) signals?
Deep learning models, specifically GRU and Bi-LSTM, using time-domain features provide the best performance for continuous blood pressure estimation from PPG signals.
May support DL for PPG-based BP estimation; leaves open prospective validation before clinical adoption.
Cardiovascular related diseases are the most significant health concern around the globe. The most crucial health indicator is blood pressure because it gives essential information about the health of a patient’s heart. Cardiovascular diseases can be detected early and prevented if blood pressure is monitored continuously and regularly. Blood pressure cuffs, which are widely used to control blood flow in the arm or wrist when measuring blood pressure, are not practical for continuous blood pressure measurement. However, biosignals can be used for blood pressure estimation; but it is still critical and challenging. In this paper, we conducted a comprehensive analysis of feature extraction techniques for blood pressure estimation by using PPG signals. The feature extraction techniques were further divided into three subgroups to analyse the significance of each group. Group A includes time-based features; group B presents statistical feature extraction, and group C presents frequency domain-based features. The analysis employed several machine learning algorithms and compared their performance from many perspectives for the first time, to the best of our knowledge. The experimental results from two publicly available datasets demonstrated that the set of features belonging to group A were more reliable than other techniques for blood pressure estimation. We found that deep learning models achieved better performance than all traditional machine learning methods. We also found that the GRU model and Bi-LSTM achieved the best performance for time-domain features for blood pressure estimation. We believe the findings of this benchmark study will help researchers choose the most appropriate method for feature extraction and machine learning algorithms.
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Maqsood et al. (2021) studied Blood pressure estimation. Deep learning models (GRU and Bi-LSTM) with time-domain features vs. Traditional machine learning methods and other feature groups was evaluated on Blood pressure estimation performance. Deep learning models, particularly GRU and Bi-LSTM using time-domain features, achieved better performance than traditional machine learning methods for blood pressure estimation using PPG signals.
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