Higher Order Derivative-Based Integrated Model for Cuff-Less Blood Pressure Estimation and Stratification Using PPG Signals

Gupta, Shresth; Singh, Anurag; Sharma, Abhishek; Tripathy, Rajesh Kumar

doi:10.1109/jsen.2022.3211993

Cited by 13 publications

(1 citation statement)

References 37 publications

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“…They performed optimization of several machine learning algorithms, including random forest (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR) to estimate the BP values. Their study successfully obtained Grade A on the British Hypertension Society (BHS) standard, which confirmed the robustness of the proposed study to estimate BP [ 29 ].…”

Section: Introductionsupporting

confidence: 56%

Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network

Fuadah

Lim

2022

Diagnostics

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Hypertension is a severe public health issue worldwide that significantly increases the risk of cardiac vascular disease, stroke, brain hemorrhage, and renal dysfunction. Early screening of blood pressure (BP) levels is essential to prevent the dangerous complication associated with hypertension as the leading cause of death. Recent studies have focused on employing photoplethysmograms (PPG) with machine learning to classify BP levels. However, several studies claimed that electrocardiograms (ECG) also strongly correlate with blood pressure. Therefore, we proposed a concatenated convolutional neural network which integrated the features extracted from PPG and ECG signals. This study used the MIMIC III dataset, which provided PPG, ECG, and arterial blood pressure (ABP) signals. A total of 14,298 signal segments were obtained from 221 patients, which were divided into 9150 signals of train data, 2288 signals of validation data, and 2860 signals of test data. In the training process, five-fold cross-validation was applied to select the best model with the highest classification performance. The proposed concatenated CNN architecture using PPG and ECG obtained the highest test accuracy of 94.56–95.15% with a 95% confidence interval in classifying BP levels into hypotension, normotension, prehypertension, hypertension stage 1, and hypertension stage 2. The result shows that the proposed method is a promising solution to categorize BP levels effectively, assisting medical personnel in making a clinical diagnosis.

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Section: Introductionsupporting

confidence: 56%