Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism.
Nicolas AguirreEdith Grall-MaësLeandro J CymberknopRicardo L ArmentanoPublished in: Sensors (Basel, Switzerland) (2021)
Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.
Keyphrases
- blood pressure
- deep learning
- hypertensive patients
- health information
- heart rate
- working memory
- cardiovascular disease
- genome wide
- rna seq
- single cell
- healthcare
- mental health
- convolutional neural network
- machine learning
- artificial intelligence
- public health
- blood glucose
- escherichia coli
- patient safety
- heart failure
- left ventricular
- social media
- big data
- computed tomography
- type diabetes
- pseudomonas aeruginosa
- metabolic syndrome
- magnetic resonance
- adipose tissue
- electronic health record
- emergency department
- cystic fibrosis
- coronary artery disease
- mass spectrometry
- high speed
- gene expression
- quality improvement
- diffusion weighted imaging