Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study

Vysotskaya, Nastassia; Will, Christoph; Servadei, Lorenzo; Maul, Noah; Mandl, Christian W.; Nau, Merlin A.; Harnisch, Jens; Maier, Andreas

doi:10.3390/s23084111

Cited by 9 publications

(5 citation statements)

References 41 publications

(103 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many anatomical variations measuring PTT: the fingertip can is used when pulse oximetry is used, the wrist for watch-type devices and the chest for longer-term monitoring patch devices. Besides PPG, applanation tonometry [ 100 ], radar [ 101 ], ultrasound [ 102 ], and pressure sensors [ 103 ] have been used for pulse waveform acquisition. Pulse arrival time (PAT) and PWV are similar concepts often used interchangeably.…”

Section: Emerging Technologiesmentioning

confidence: 99%

History and evolution of blood pressure measurement

Noh,

Kim,

Kang

et al. 2024

Clin Hypertens

View full text Add to dashboard Cite

Hypertension is the leading cause of morbidity and mortality worldwide. Hypertension mostly accompanies no symptoms, and therefore blood pressure (BP) measurement is the only way for early recognition and timely treatment. Methods for BP measurement have a long history of development and improvement. Invasive method via arterial cannulation was first proven possible in the 1800’s. Subsequent scientific progress led to the development of the auscultatory method, also known as Korotkoff’ sound, and the oscillometric method, which enabled clinically available BP measurement. However, hypertension management status is still poor. Globally, less than half of adults are aware of their hypertension diagnosis, and only one-third of them being treated are under control. Novel methods are actively investigated thanks to technological advances such as sensors and machine learning in addition to the clinical needs for easier and more convenient BP measurement. Each method adopts different technologies with its own specific advantages and disadvantages. Promises of novel methods include comprehensive information on out-of-office BP capturing dynamic short-term and long-term fluctuations. However, there are still pitfalls such as the need for regular calibration since most novel methods capture relative BP changes rather than an absolute value. In addition, there is growing concern on their accuracy and precision as conventional validation protocols are inappropriate for cuffless continuous methods. In this article, we provide a comprehensive overview of the past and present of BP measurement methods. Novel and emerging technologies are also introduced with respect to their potential applications and limitations. Graphical Abstract

show abstract

Section: Emerging Technologiesmentioning

confidence: 99%

History and evolution of blood pressure measurement

Noh,

Kim,

Kang

et al. 2024

Clin Hypertens

View full text Add to dashboard Cite

show abstract

“…Continuous vital signs monitoring can prevent life-threatening scenarios by providing early warnings before a decline of the patient's status [104]. In this sub-category of papers on value estimation we include papers dealing with the estimation of vital signs, such as HR (n = 21) [17], [22], [25], [28], [29], [52], [55]- [57], [62], [64], [74], [76], [82], [87], [91], [93], [96]- [98], BR [58], [66], [88], [89], [99], [102] (n = 6), both HR and BR [31], [34], [40], [53], [54], [92], [103] (n = 7), BP [65], [67], [75], [83], [85], [100], [101] (n = 7), oxygen saturation (SpO2) [84] (n = 1), and spyrometric indices [61] (n = 1) in a given windows of observation.…”

Section: A First Cluster Tasksmentioning

confidence: 99%

“…value estimation (n=6) [74], [76], [83], [85], [93], [99]; signal reconstruction (n=3) [36], [41], [69] MRE= N i (predictioni − truevaluei) value estimation (n=5) [61], [65], [83], [97], [110]; signal reconstruction (n=3) [30], [36], [44], [94]; physiological event detection (n=1…”

Section: ) Performance Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Nocera,

Senigagliesi,

Raimondi

et al. 2024

Preprint

View full text Add to dashboard Cite

In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, one hundred and ninety two papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n = 68 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features applied to four fields of interest, being RADAR-based diagnosis (n = 73), RADAR-based human behaviour monitoring (n = 21), RADAR-based biometrics authentication (n = 18) and RADAR-based affective computing (n = 9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n = 39), infection detection (n = 10), sleep stage classification (n = 9), heart disease detection (n = 8) and quality detection (n = 7). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n = 8), human detection (n = 7), human localisation (n = 4), human orientation (n = 2), and activities classification (n = 3).

show abstract

“…Among the most commonly used optimizers in the various prediction and classification cases, SGD, RMSprop, Adadelta, and Adam were selected for study. Therefore, we expanded our analysis using Adam, Adadelta, RMSprop, and SGD optimizers [10,[27][28][29][33][34][35][36]. We considered the merits of each optimizer, including the SGD [33], RMSprop [29], Adam [37], and Adadelta [29] formulae.…”

Section: Optimizer Learning Rate and Batch Sizementioning

confidence: 99%

PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM

Fuadah

Jeong

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients’ health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN–LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN–LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN–LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices.

show abstract

Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study

Cited by 9 publications

References 41 publications

History and evolution of blood pressure measurement

History and evolution of blood pressure measurement

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM

Contact Info

Product

Resources

About