scholarly journals Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2167 ◽  
Author(s):  
Nicolas Aguirre ◽  
Edith Grall-Maës ◽  
Leandro J. Cymberknop ◽  
Ricardo L. Armentano
Keyword(s):  
Blood Pressure ◽  
Deep Learning ◽  
Attention Mechanism ◽  
Wireless Devices ◽  
Demographic Information ◽  
Use Of Data ◽  
Arterial Blood ◽  
The Mean ◽  
And Gender ◽  
Deep Learning Model

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.

scholarly journals Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5606
Author(s):  
Yung-Hui Li ◽  
Latifa Nabila Harfiya ◽  
Kartika Purwandari ◽  
Yue-Der Lin
Keyword(s):  
Blood Pressure ◽  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Blood Pressure Monitoring ◽  
Time Estimation ◽  
Learning Model ◽  
Accurate Estimation ◽  
Arterial Blood ◽  
Deep Learning Model

Blood pressure monitoring is one avenue to monitor people’s health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet’s multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.

scholarly journals Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

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.

scholarly journals On the Pathology of the dropsy produced by obstruction of the superior and inferior venœ and the portal vein.— Preliminary communication

1907 ◽  
Vol 79 (532) ◽  
pp. 267-283 ◽  
Keyword(s):  
Blood Pressure ◽  
Arterial Pressure ◽  
Portal Vein ◽  
Mean Arterial Blood Pressure ◽  
Normal Limit ◽  
The Body ◽  
Diastolic Filling ◽  
Arterial Blood ◽  
Preliminary Communication ◽  
The Mean

In August, 1903, I published a paper in the ‘Journal of Pathology’(1) in which I demonstrated a method experimentally producing uncompensated hear disease in an animal, which was compatible with life. This method consisted in diminishing the size of the pericardial sac by stitches, so that the diastolic filling of the heart was impeded. The main symptoms of this condition were dropsy and diminution in the amount of urine excreted. As the immediate result of this interference with the action of the heart, there occurred a rise of pressure throughout the whole systemic venous system extending as far back as the capillaries, and a fall of the mean arterial blood-pressure. Further, I found that the pressure in all the veins fell to the normal limit again within the space of about one hour, and that subsequently when dropsy was being produced, the vanous pressure in all parts of the body was normal, and the arterial pressure had almost recovered itself.

scholarly journals An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1867
Author(s):  
Tasbiraha Athaya ◽  
Sunwoong Choi
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Health ◽  
Estimation Method ◽  
Absolute Error ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Arterial Blood ◽  
Blood Pressure Waveform ◽  
The Mean ◽  
Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

HAEMODYNAMIC CHANGES AND ADRENAL FUNCTION IN MAN DURING INDUCED HYPOGLYCAEMIA

1963 ◽  
Vol 44 (3) ◽  
pp. 430-442 ◽  
Author(s):  
B. Arner ◽  
P. Hedner ◽  
T. Karlefors ◽  
H. Westling
Keyword(s):  
Blood Pressure ◽  
Cardiac Output ◽  
Mean Arterial Blood Pressure ◽  
Peripheral Vascular ◽  
Adrenocortical Activity ◽  
Arterial Blood ◽  
Haemodynamic Changes ◽  
The Mean ◽  
Temporary Rise ◽  
Catechol Amines

ABSTRACT Observations were made on healthy volunteers during insulin induced hypoglycaemia (10 cases) and infusion of adrenaline (3 cases) or cortisol (1 case). In all cases a rise in the cardiac output was registered during insulin hypoglycaemia. The mean arterial blood pressure was relatively unchanged and the calculated peripheral vascular resistance decreased in all cases. A temporary rise in plasma corticosteroids was observed. After infusion of adrenaline similar circulatory changes were observed but no rise in plasma corticosteroids was found. Infusion of cortisol caused an increased plasma corticosteroid level but no circulatory changes. It is concluded that liberation of catechol amines and increased adrenocortical activity following hypoglycaemia are not necessarily interdependent.

scholarly journals THE ASSAY OF HYPERTENSIN FROM THE ARTERIAL BLOOD OF NORMOTENSIVE AND HYPERTENSIVE HUMAN BEINGS

1952 ◽  
Vol 95 (6) ◽  
pp. 523-529 ◽  
Author(s):  
Joseph R. Kahn ◽  
Leonard T. Skeggs ◽  
Norman P. Shumway ◽  
Paul E. Wisenbaugh
Keyword(s):  
Blood Pressure ◽  
Essential Hypertension ◽  
Normal Blood ◽  
Moderate Degree ◽  
Human Beings ◽  
Normal Blood Pressure ◽  
Arterial Blood ◽  
The Mean ◽  
Mean Concentration ◽  
Benign Group

Hypertensin has been assayed in the blood of patients with normal blood pressure and in those with essential hypertension in both the benign and malignant phases. 250 ml. samples of arterial blood were obtained, chemically purified, and concentrated to a volume of 1 ml. These extracts were then assayed in anesthetized rats. The concentrations of hypertensin in the blood of patients with the malignant phase of essential hypertension were found to be greatly increased. The concentrations of hypertensin found in patients with benign hypertension had a moderate degree of overlapping with those found in the normotensive group, but the mean concentration of hypertensin in the former group was twice that of the controls. Although these results are statistically significant, the amounts of hypertensin recovered in the benign group are so small that no conclusions can be drawn as to its effectiveness in producing vasoconstriction in these patients.

Vasodilation of cat cerebral arterioles by prostaglandins D2, E2, G2, and I2

1979 ◽  
Vol 237 (3) ◽  
pp. H381-H385 ◽  
Author(s):  
E. F. Ellis ◽  
E. P. Wei ◽  
H. A. Kontos
Keyword(s):  
Blood Pressure ◽  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Arterial Blood Pressure ◽  
Standard Error ◽  
Arterial Blood ◽  
Cerebral Arterioles ◽  
The Mean ◽  
Dose Dependent ◽  
The Brain

To determine the possible role that endogenously produced prostaglandins may play in the regulation of cerebral blood flow, the responses of cerebral precapillary vessels to prostaglandins (PG) D2, E2, G2, and I2 (8.1 X 10(-8) to 2.7 X 10(-5) M) were studied in cats equipped with cranial windows for direct observation of the microvasculature. Local application of PGs induced a dose-dependent dilation of large (greater than or equal to 100 microns) and small (less than 100 microns) arterioles with no effect on arterial blood pressure. The relative vasodilator potency was PGG2 greater than PGE2 greater than PGI2 greater than PGD2. With all PGs, except D2, the percent dilation of small arterioles was greater than the dilation of large arterioles. After application of prostaglandins in a concentration of 2.7 X 10(-5) M, the mean +/- standard error of the percent dilation of large and small arterioles was, respectively, 47.6 +/- 2.7 and 65.3 +/- 6.1 for G2, 34.1 +/- 2.0, and 53.6 +/- 5.5 for E2, 25.4 +/- 1.8, and 40.2 +/- 4.6 for I2, and 20.3 +/- 2.5 and 11.0 +/- 2.2 for D2. Because brain arterioles are strongly responsive to prostaglandins and the brain can synthesize prostaglandins from its large endogenous pool of prostaglandin precursor, prostaglandins may be important mediators of changes in cerebral blood flow under normal and abnormal conditions.

Vasomotor waves of arterial blood pressure after cessation of cardiopulmonary bypass in man

Perfusion ◽  
1990 ◽  
Vol 5 (4) ◽  
pp. 261-266
Author(s):  
V. Vainionpää ◽  
A. Hollme'n ◽  
J. Timisjärvi
Keyword(s):  
Blood Pressure ◽  
Cardiopulmonary Bypass ◽  
Arterial Pressure ◽  
Venous Pressure ◽  
Systemic Arterial Pressure ◽  
Heart Patients ◽  
Arterial Blood ◽  
Pulmonary Arterial ◽  
The Mean ◽  
Epicardial Pacing

The occurrence of vasomotor waves during cardiopulmonary bypass (CPB) is a recognized phenomenon. The lesser known oscillation of arterial pressure after cessation of CPB was observed in 18 open-heart patients. The duration of an oscillatory wave was 13.5±5.0 seconds, the amplitude 6.1 ±2.6mmNg and the mean arterial pressure 76.5± 10.7mmHg. Inter-and also intraindividual variations in frequency and amplitude of the oscillation, however, did occur. In 13 patients, this oscillation occurred during ventricular epicardial pacing. The oscillation continued until the end of the operation in eight patients; in others, the oscillation was of shorter duration. An oscillation of pulmonary arterial pressure (PAP) was simultaneously observed in nine patients (eight with pacemaker) and central venous pressure (CVP) oscillation in eight patients (all with pacemaker). The duration of a wave was the same as in systemic arterial pressure and the amplitudes were 1.5-3.0mmHg in PAP and 1.0-2.0mmHg in CVP. These arterial vasomotor waves, seen here after CPB, largely resemble those observed during perfusion in man and also the Mayerwaves explored in experimental animals. The pacing rhythm seems to favourthe appearance of those blood pressure oscillations.

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