Cuffless Blood Pressure Measurement Using a Smartphone-Case Based ECG Monitor with Photoplethysmography in Hypertensive Patients

Sagirova, Zhanna; Kuznetsova, Natalia; Гогиберидзе, Н. А.; Gognieva, Daria; Suvorov, А. Yu.; Чомахидзе, П. Ш.; Omboni, Stefano; Saner, Hugo; Kopylov, Philipp

doi:10.3390/s21103525

Cited by 38 publications

(23 citation statements)

References 33 publications

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“…The performance of the two and three-channel approaches are similar, while the performance improves again by around 25% when all four signals are used in combination. The same pattern was seen for both SBP and DBP even though SBP performed worse than DBP in all cases, which is a typical observation from the literature as well [12][13][14][15][16]18,[20][21][22][23][24][25][26][27]64].…”

Section: Experiments 1: Train and Test On Uci Datasetsupporting

confidence: 79%

A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

Mahmud¹,

Ibtehaz²,

Khandakar³

et al. 2022

Sensors

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Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.

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Section: Experiments 1: Train and Test On Uci Datasetsupporting

confidence: 79%

A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

Mahmud¹,

Ibtehaz²,

Khandakar³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Using single biomedical signals such as ECG or PPG to estimate the BP including systolic blood pressure (SBP) and diastolic blood pressure (DBP) can increase the value of smart devices [3,23]. Therefore, the effects of ECG and PPG signals are examined and then three types of inputs were designed as I(ECG), I(PPG), and I(ECG, PPG).…”

Section: Results Of Feature Evaluation and Selectionmentioning

confidence: 99%

“…Cuff-based BP monitoring devices are commonly used to estimate BP, but these devices are inconvenient and uncomfortable for patients. To overcome some of these problems, many researchers developed cuffless BP estimations, which are based on pulse transit time (PTT), photoplethysmography (PPG), or electrocardiogram (ECG) [2][3][4][5]. Generally, PTT refers to the time it takes a pulse wave to travel between two arterial sites and can be calculated by using ECG and PPG [2].…”

Section: Introductionmentioning

confidence: 99%

Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation

et al. 2021

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Monitoring people’s blood pressure can effectively prevent blood pressure-related diseases. Therefore, providing a convenient and comfortable approach can effectively help patients in monitoring blood pressure. In this study, an attention mechanism-based convolutional long short-term memory (LSTM) neural network is proposed to easily estimate blood pressure. To easily and comfortably estimate blood pressure, electrocardiogram (ECG) and photoplethysmography (PPG) signals are acquired. To precisely represent the characteristics of ECG and PPG signals, the signals in the time and frequency domain are selected as the inputs of the proposed NN structure. To automatically extract the features, the convolutional neural networks (CNNs) are adopted as the first part of neural networks. To identify the meaningful features, the attention mechanism is used in the second part of neural networks. To model the characteristic of time series, the long short-term memory (LSTM) is adopted in the third part of neural networks. To integrate the information of previous neural networks, the fully connected networks are used to estimate blood pressure. The experimental results show that the proposed approach outperforms CNN and CNN-LSTM and complies with the Association for the Advancement of Medical Instrumentation standard.

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“…Such methods are essential for the clinical application of sensor arrays. Existing studies have shown that pulse wave abnormalities are closely associated with cardiovascular disorders, which provides strong evidence for the assessment of the physiological and pathological status of individuals [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders

Yao

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Study on the objectivity of pulse diagnosis is inseparable from the instruments to obtain the pulse waves. The single-pulse diagnostic instrument is relatively mature in acquiring and analysing pulse waves, but the pulse information captured by single-pulse diagnostic instrument is limited. The sensor arrays can simulate rich sense of the doctor’s fingers and catch multipoint and multiparameter array signals. How to analyse the acquired array signals is still a major problem in the objective research of pulse diagnosis. The goal of this study was to establish methods for analysing arrayed pulse waves and preliminarily apply them in hypertensive disorders. While a sensor array can be used for the real-time monitoring of twelve pulse wave channels, for each subject in this study, only the pulse wave signals of the left hand at the “guan” location were obtained. We calculated the average pulse wave (APW) per channel over a thirty-second interval. The most representative pulse wave (MRPW) and the APW were matched by their correlation coefficient (CC). The features of the MRPW and the features that corresponded to the array pulse volume (APV) parameters were identified manually. Finally, a clinical trial was conducted to detect these feature performance indicators in patients with hypertensive disorders. The independent-samples t-tests and the Mann–Whitney U-tests were performed to assess the differences in these pulse parameters between the healthy and hypertensive groups. We found that the radial passage (RP) APVh1, APVh3, APVh4, APVh3/h1 (P < 0.01), and APVh4/h1 (P < 0.05) were significantly higher in the hypertensive group than in the healthy group; the intermediate passage (IP) APVh4, APVh3/h1 (P < 0.05), and APVh4/h1 (P < 0.01) and the mean APVh3, APVh3/h1 (P < 0.05), and APVh4/h1 (P < 0.01) were significantly higher in the hypertensive group than in the healthy group, and the ulnar passage (UP) APVh4/h1 (P < 0.05) was clearly elevated in the hypertensive group. These results provide a preliminary validation of this novel approach for determining the APV by arrayed pulse wave analysis. In conclusion, we identified effective indicators of hypertensive vascular function. Traditional Chinese medicine (TCM) pulses comprise multidimensional information, and a sensor array could provide a better indication of TCM pulse characteristics. In this study, the validation of the arrayed pulse wave analysis demonstrates that the APV can reliably mirror TCM pulse characteristics.

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Cuffless Blood Pressure Measurement Using a Smartphone-Case Based ECG Monitor with Photoplethysmography in Hypertensive Patients

Cited by 38 publications

References 33 publications

A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation

Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders

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