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.Information 2020, 11, 93 2 of 18 blood volume changes, which occur between the systolic and diastolic phases of the cardiac cycle. The essential frequency of the AC component depends on the heart rate and is covered onto the DC component [11]. An LED is a light source that can be used to illuminate blood vessels so minor perfusion changes can be supervised on the photodetectors [12]. Perfusion is measured as the degree at which blood is distributed to tissue [13].

The photoplethysmography (PPG) method for continuous noninvasive measurements of blood pressure (BP) offers a more comfortable solution than conventional methods. The main challenge in using the PPG method is that its accuracy is greatly influenced by motion artifacts. In addition, the characteristics of PPG vary depending on physiological conditions; hence, the system must be calibrated to adjust for such changes. We attempt to address these limitations and propose a novel method for the classification of BP using a bidirectional long short-term memory (BLSTM) network with time-frequency (TF) analysis based on PPG signals. The TF analysis extracts information from PPG signals using a short-time Fourier transform (STFT) in the time domain to produce two features, namely, the instantaneous frequency and spectral entropy. Training the BLSTM network using TF features significantly improves the classification performance and decreases the training time. We classify 900 PPG waveform segment samples from 219 adult subjects into three classification levels: normotension (NT), prehypertension (PHT) and hypertension (HT). The results show that the proposed method is successful in the classification of BP with accuracy, sensitivity, and speciticity values of 97.33%, 100%, and 94.87%, respectively. The F1 scores of three BP classifications were 97.29%, 97.39%, and 93.93%, respectively. A comparison of current and previous approaches to the classification of BP is accomplished. Our proposed method achieves a higher accuracy than convolutional neural networks (CNNs), k-nearest neighbors (KNN), bagged tree, logistic regression, and AdaBoost tree methods.

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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 Datascope TM. 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.

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