Cuffless blood pressure estimation based on composite neural network and graphics information

Qiu, Ye; Liu, Dongdong; Yang, Guoyu; Qi, Dezhen; Lu, Yuer; He, Qingzu; Qian, Xiangyu; Li, Xiang; Cao, Yuping; Shuai, Jianwei

doi:10.1016/j.bspc.2021.103001

Cited by 20 publications

(12 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Baker et al [75] BioMed Research International Some other researches have introduced an attention to assign appropriate weights between different input channels or input vectors; therefore, the deep learning model can focus on more meaningful information and then improve the prediction accuracy. Qiu et al [77] incorporated the squeeze and excitation block (SE module) in a 25-layer ResNet, which assigned weights to channel dimensions and improved channel attention. The hybrid model took PPG and ECG signals as input and predicted blood pressure on two datasets that included 1216 and 40 subjects from the MIMIC dataset, respectively.…”

Section: Blood Pressure Estimation Methods Based On Deepmentioning

confidence: 99%

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Qin

Wang

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.

show abstract

Section: Blood Pressure Estimation Methods Based On Deepmentioning

confidence: 99%

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Qin

Wang

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Recently, in the field of complex medical pattern recognition tasks, such as the visual diagnosis of diabetic retinopathy [63] and dermatosis [74], deep learning has displayed identical performance with that of medical specialists. Deep learning methods have been applied to the sleep stage classification of EEG [75,76], depression [77][78][79], and proteomics [80][81][82]. The deep learning models have been considered as a practical solution for the assessment of sleep arousals [83].…”

Section: Microarousal Detection With Deep Learning Methodsmentioning

confidence: 99%

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Qian

Qiu

et al. 2021

Brain Sciences

Self Cite

View full text Add to dashboard Cite

Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.

show abstract

“…Eom et al proposed CNN-based BP estimation. Due to a very small population number (only 15), the small standard deviation is not a significant improvement [ 57 ]. Athaya and Choi proposed [ 58 ] a PPG-based deep learning technique (U net) to measure blood pressure with a significantly small standard deviation, but the population was only 100.…”

Section: Introductionmentioning

confidence: 99%

“…Second, in rare cases when they have chosen to test the technique with separate datasets, the size of those datasets was very small. Third, since deep learning takes care of feature extraction and selection without human intervention, it is always difficult to explain the relationship between any feature and outcome [ 57 , 61 , 62 , 63 , 64 , 65 ].…”

Section: Introductionmentioning

confidence: 99%

Cuffless Blood Pressure Measurement Using Linear and Nonlinear Optimized Feature Selection

Mamun

Alouani

2022

Diagnostics

View full text Add to dashboard Cite

The cuffless blood pressure (BP) measurement allows for frequent measurement without discomfort to the patient compared to the cuff inflation measurement. With the availability of a large dataset containing physiological waveforms, now it is possible to use them through different learning algorithms to produce a relationship with changes in BP. In this paper, a novel cuffless noninvasive blood pressure measurement technique has been proposed using optimized features from electrocardiogram and photoplethysmography based on multivariate symmetric uncertainty (MSU). The technique is an improvement over other contemporary methods due to the inclusion of feature optimization depending on both linear and nonlinear relationships with the change of blood pressure. MSU has been used as a selection criterion with algorithms such as the fast correlation and ReliefF algorithms followed by the penalty-based regression technique to make sure the features have maximum relevance as well as minimum redundancy. The result from the technique was compared with the performance of similar techniques using the MIMIC-II dataset. After training and testing, the root mean square error (RMSE) comes as 5.28 mmHg for systolic BP and 5.98 mmHg for diastolic BP. In addition, in terms of mean absolute error, the result improved to 4.27 mmHg for SBP and 5.01 for DBP compared to recent cuffless BP measurement techniques which have used substantially large datasets and feature optimization. According to the British Hypertension Society Standard (BHS), our proposed technique achieved at least grade B in all cumulative criteria for cuffless BP measurement.

show abstract

Cuffless blood pressure estimation based on composite neural network and graphics information

Cited by 20 publications

References 35 publications

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Cuffless Blood Pressure Measurement Using Linear and Nonlinear Optimized Feature Selection

Contact Info

Product

Resources

About