Key result
The LSTM-Attention deep learning model using photoplethysmography signals identified hypertension with an accuracy of 99.1% and an F1-score of 99.1%.
Why the study?
To prevent and diagnose hypertension early, there is a demand to evaluate how non-invasive photoplethysmographic signals combined with deep learning algorithms can identify hypertension states.
Does a photoplethysmography-driven LSTM-Attention deep learning model accurately identify hypertension in adults?
Cross-Sectional (n=30)
No
Does a photoplethysmography-driven LSTM-Attention deep learning model accurately identify hypertension in adults?
Answers are grounded in this paper and cite their sources.
A novel deep learning model using non-invasive photoplethysmography signals can identify hypertension with high accuracy, offering a potential cost-effective screening tool.
To prevent and diagnose hypertension early, there has been a growing demand to identify its states that align with patients. This pilot study aims to research how a non-invasive method using photoplethysmographic (PPG) signals works together with deep learning algorithms. A portable PPG acquisition device (Max30101 photonic sensor) was utilized to (1) capture PPG signals and (2) wirelessly transmit data sets. In contrast to traditional feature engineering machine learning classification schemes, this study preprocessed raw data and applied a deep learning algorithm (LSTM-Attention) directly to extract deeper correlations between these raw datasets. The Long Short-Term Memory (LSTM) model underlying a gate mechanism and memory unit enables it to handle long sequence data more effectively, avoiding gradient disappearance and possessing the ability to solve long-term dependencies. To enhance the correlation between distant sampling points, an attention mechanism was introduced to capture more data change features than a separate LSTM model. A protocol with 15 healthy volunteers and 15 hypertension patients was implemented to obtain these datasets. The processed result demonstrates that the proposed model could present satisfactory performance (accuracy: 0.991; precision: 0.989; recall: 0.993; F1-score: 0.991). The model we proposed also demonstrated superior performance compared to related studies. The outcome indicates the proposed method could effectively diagnose and identify hypertension; thus, a paradigm to cost-effectively screen hypertension could rapidly be established using wearable smart devices.
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Yan et al. (2023) conducted a cross-sectional in Hypertension (n=30). Photoplethysmography (PPG) with LSTM-Attention deep learning model vs. Other machine learning models (LSTM, BiLSTM, SVM, KNN) was evaluated on Model accuracy for hypertension identification. The LSTM-Attention deep learning model using photoplethysmography signals identified hypertension with an accuracy of 99.1% and an F1-score of 99.1%.
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