Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classification accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the K-nearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPG–BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual pre-processing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.

The emergence of photoplethysmography for blood pressure estimation is offering a more convenient method. The elements of photoplethysmography waveform is crucial for blood pressure measurement. Several photoplethysmography elements are still not completely understood. The purpose of this study was to investigated corelation of photoplethysmography elements with blood pressure using statistical approach. Analysis of variance test (ANOVA) was conducted to see if there are any correlation between elements of photoplethysmography with blood pressure. This study used 10 volunteers without an ethical clearance. Photoplethysmography waveform and blood pressure measurements were taken through the patient monitor equipment DatascopeTM. As the result, value factor from the arithmetic is 35.67 and value factor from the table is 3.14. The value of F arithmetic (35.67) > F table (3.14). The correlation of diastolic time (Td) is negative with systolic arterial pressure (SAP) and the correlation of systolic amplitude (As) is positive with diastolic arterial pressure (DAP). The results showed elements of photoplethysmography can be used to estimation blood pressure. The emergence of photoplethysmography for blood pressure estimation is offering a more convenient method. The elements of photoplethysmography waveform is crucial for blood pressure measurement. Several photoplethysmography elements are still not completely understood. The purpose of this study was to investigated corelation of photoplethysmography elements with blood pressure using statistical approach. Analysis of variance test (ANOVA) was conducted to see if there are any correlation between elements of photoplethysmography with blood pressure. This study used 10 volunteers without an ethical clearance. Photoplethysmography waveform and blood pressure measurements were taken through the patient monitor equipment DatascopeTM. As the result, value factor from the arithmetic is 35.67 and value factor from the table is 3.14. The value of F arithmetic (35.67) > F table (3.14). The correlation of diastolic time (Td) is negative with systolic arterial pressure (SAP) and the correlation of systolic amplitude (As) is positive with diastolic arterial pressure (DAP). The results showed elements of photoplethysmography can be used to estimation blood pressure.