A CNN-LSTM model using PPG signals classified normotension versus hypertension with 67.76% accuracy, which was slightly higher than for normotension versus prehypertension.
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.
Blood pressure (BP) is a key indication that needs to be checked on a regular basis. For maintaining a healthy life normal blood pressure is essential, and a continuous change can cause serious problems related to health such as hypertension, deadly cardio-vascular disorders and kidney failure. Hypertension is one of the leading causes of death in the globe. For the early diagnosis and prevention of fatal occurrences, an effective unobstructed technique is required for BP monitoring on continuous basis. Here, we present a new categorization approach for BP based on PPG signal and CNN-LSTM model. Classification is performed on different levels between Normotension (NT) and Prehypertension (PHT) and between Normotension (NT) and Hypertension (HT). The findings reveal that the classification of normotension vs hypertension yields more accuracy 67.76%, which is slightly higher than normotension vs prehypertension.
Gupta et al. (Sat,) conducted a other in 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.