Abstract
Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.
Highlights
Cardiovascular diseases (CVDs) have become a major contributor to human mortality [1].According to the latest statistics produced by the World Health Organization (WHO), the mortality rate from CVDs will rise from 246 people per one million in 2015 to 264 people per one million in 2030 [2,3].It is known that abnormal blood pressure can produce many complications for the heart, kidneys, and other vital organs, causing irreversible injury
Data recordings with a sampling rate of 125 Hz containing arterial blood pressure (ABP) and PPG signals were collected from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) physiological database
Raw ABP signals, which were used for the extraction of blood pressure categories, were not processed
Summary
Cardiovascular diseases (CVDs) have become a major contributor to human mortality [1].According to the latest statistics produced by the World Health Organization (WHO), the mortality rate from CVDs will rise from 246 people per one million in 2015 to 264 people per one million in 2030 [2,3].It is known that abnormal blood pressure can produce many complications for the heart, kidneys, and other vital organs, causing irreversible injury. A more invasive blood pressure measurement method, which is the gold standard, involves inserting a catheter into an artery to conduct real-time monitoring. This approach is only suitable for critical patients and carries the risk of infection. The most common technique used for blood pressure measurement is Korotkoff’s sound and oscillographic method [6]. Both of these techniques require the use of an upper arm cuff and utilize the cuff pressure and release process to detect systolic and diastolic pressure
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