Key result
CNN-LSTM model using PPG signals classifies normotension versus hypertension with ~68% accuracy.
Why the study?
An effective unobstructed technique is required for continuous blood pressure monitoring to facilitate early diagnosis and prevention of fatal complications from hypertension.
Can a CNN-LSTM model using PPG signals accurately categorize blood pressure into normotension, prehypertension, and hypertension?
Comparison
CNN-LSTM model classification of normotension vs prehypertension compared to normotension vs hypertension
Authors
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Moderate PPG accuracy precludes clinical adoption; leaves open feasibility of continuous non-invasive BP classification.
Can a CNN-LSTM model using PPG signals accurately categorize blood pressure into normotension, prehypertension, and hypertension?
A CNN-LSTM model using PPG signals shows moderate accuracy (67.76%) in distinguishing normotension from hypertension, indicating potential for continuous non-invasive blood pressure monitoring.
Gupta et al. (2022) studied Hypertension. CNN-LSTM model using PPG signal was evaluated on Classification accuracy for normotension vs hypertension. A CNN-LSTM model using PPG signals classified normotension versus hypertension with 67.76% accuracy, which was slightly higher than for normotension versus prehypertension.
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