scholarly journals Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification

Biosensors ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 101 ◽  
Author(s):  
Yongbo Liang ◽  
Zhencheng Chen ◽  
Rabab Ward ◽  
Mohamed Elgendi
Keyword(s):  
Blood Pressure ◽  
Signal Processing ◽  
Feature Extraction ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Risk Stratification ◽  
Physiological Parameter ◽  
National Committee ◽  
Hypertension Risk ◽  
Arterial Blood

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.

scholarly journals A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

2021 ◽  
Author(s):  
Annunziata Paviglianiti ◽  
Vincenzo Randazzo ◽  
Stefano Villata ◽  
Giansalvo Cirrincione ◽  
Eros Pasero
Keyword(s):  
Blood Pressure ◽  
Neural Networks ◽  
Deep Learning ◽  
Arterial Blood Pressure ◽  
Blood Pressure Measurement ◽  
National Standards ◽  
Detection Methods ◽  
Physiological Parameter ◽  
Arterial Blood ◽  
Learning Techniques

AbstractContinuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino.

Effects of age, gender, and blood pressure on myogenic responses of mesenteric arteries from C57BL/6 mice

2002 ◽  
Vol 282 (1) ◽  
pp. H380-H388 ◽  
Author(s):  
Robert Gros ◽  
Ryan Van Wert ◽  
Xiaomang You ◽  
Eric Thorin ◽  
Mansoor Husain
Keyword(s):  
Blood Pressure ◽  
Perfusion Pressure ◽  
Inverse Correlation ◽  
Mesenteric Arteries ◽  
Physiological Parameter ◽  
Experimental Hypertension ◽  
Set Point ◽  
Arterial Blood ◽  
Old Mice

The myogenic response (MR) may represent an important physiological parameter underlying arterial blood pressure (BP). We studied the effects of age, gender, and BP on the MR of mesenteric arteries from 8- to 52-wk-old mice. Increasing age and BP are associated with an increase in the perfusion pressure at which tone develops (myogenic set point). An inverse correlation exists between age and extent (magnitude) of the MR in male ( r 2 = 0.93, P = 0.0087) and female mice ( r 2 = 0.90, P = 0.013) as well as between BP and extent of the MR in male ( r 2 = 0.96, P = 0.0036) and female ( r 2 = 0.90, P = 0.014) mice. In contrast, the strength of the MR (slope of active diameter-pressure relationship) and phenylephrine-mediated constriction did not differ among these groups. Although gender had no effect on MR at any perfusion pressure or age, only male mice showed significant salt-induced hypertension and an associated increase in the set point and reduction in the extent of the MR. The set point and extent of the MR is linked to the in vivo pressure during development and experimental hypertension.

scholarly journals Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

2021 ◽  
Vol 17 (14) ◽  
pp. 103-118
Author(s):  
Mohammed Enamul Hoque ◽  
Kuryati Kipli
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Retinal Image ◽  
Feature Recognition ◽  
Image Features ◽  
Complex Data ◽  
Retinal Image Segmentation

Image recognition and understanding is considered as a remarkable subfield of Artificial Intelligence (AI). In practice, retinal image data have high dimensionality leading to enormous size data. As the morphological retinal image datasets can be analyzed in an expansive and non-invasive way, AI more precisely Deep Learning (DL) methods are facilitating in developing intelligent retinal image analysis tools. The most recently developed DL technique, Convolutional Neural Network (CNN) showed remarkable efficiency in identifying, localizing, and quantifying the complex and hierarchical image features that are responsible for severe cardiovascular diseases. Different deep layered CNN architectures such as LeeNet, AlexNet, and ResNet have been developed exploiting CNN morphology. This wide variety of CNN structures can iteratively learn complex data structures of different datasets through supervised or unsupervised learning and perform exquisite analysis for feature recognition independently to diagnose threatening cardiovascular diseases. In modern ophthalmic practice, DL based automated methods are being used in retinopathy screening, grading, identifying, and quantifying the pathological features to employ further therapeutic approaches and offering a wide potentiality to get rid of ophthalmic system complexity. In this review, the recent advances of DL technologies in retinal image segmentation and feature extraction are extensively discussed. To accomplish this study the pertinent materials were extracted from different publicly available databases and online sources deploying the relevant keywords that includes retinal imaging, artificial intelligence, deep learning and retinal database. For the associated publications the reference lists of selected articles were further investigated.

Sign in / Sign up

Export Citation Format

Share Document