Non-invasive blood pressure estimation using phonocardiogram

Dastjerdi, Amirhossein Esmaili; Kachuee, Mohammad; Shabany, Mahdi

doi:10.1109/iscas.2017.8050240

Cited by 23 publications

(20 citation statements)

References 6 publications

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“…The advantage of this method is that the R-peak of the ECG signal is easy to detect, especially during resting conditions. Several studies use alternative cardiac signals for ECG, such as impedance plethysmography (IPG) [99,100], phonocardiogram (PCG) [101,102], or ballistocardiogram (BCG) [103,104]. To improve the user comfort, a peripheral PTT-based multi-wavelength method has been proposed using two or multiple PPG sensors [105][106][107].…”

Section: B Continuous Blood Pressure Monitoringmentioning

confidence: 99%

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding

Clifton

et al. 2021

IEEE Rev. Biomed. Eng.

194

155

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Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death.A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

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Section: B Continuous Blood Pressure Monitoringmentioning

confidence: 99%

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding

Clifton

et al. 2021

IEEE Rev. Biomed. Eng.

194

155

View full text Add to dashboard Cite

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“…It is shown that these parameters are related to the individual's age, height, and other physiological parameters [4]. Considering these parameters and using equations (1) and (2), the relationship between BP and PTT can be modeled as [4], [53]:…”

Section: B Ptt and Bp Estimationmentioning

confidence: 99%

Cuff-Less Blood Pressure Monitoring System Using Smartphones

et al. 2020

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Recently, smartphones with mobile health applications have become promising tools in the healthcare industry due to their convenience, ubiquity for patients, and the ability to gather data in real time. In this paper, we propose a novel non-invasive, portable, and cuff-less method for monitoring BP by only using the smartphones' camera. Our experiment uses pulse transit time (PTT) between two separate photoplethysmogram (PPG) signals to estimate the subjects' systolic blood pressure (SBP) and diastolic blood pressure (DBP). Our proposed method first measures the subject's PPG signals from his/her index fingers using the smartphones' camera. Then, filtering and peak detection algorithms of the proposed method reduce the motion and noise artifacts in the PPG signals. Finally, the proposed method estimates SBP and DBP based on a linear regression model which was trained and tested on 30 trials with six healthy subjects. We evaluated the proposed method by comparing BP values of the proposed method with those of the reference (or gold-standard) device in terms of mean absolute error (MAE), standard deviation of error (SD), and R-squared (R 2) value of the cross-validation. Experimental results show that the proposed method estimates the average of MAE ± SD is 2.07 ± 2.06 mm Hg for SBP estimation, and 2.12 ± 1.85 mm Hg for DBP estimation. These estimates are lower than accurate BP estimation standard (5 ± 8 mmHg). INDEX TERMS Hypertension, blood pressure, cuff-less, smartphone, photoplethysmogram (PPG), pulse transit time (PTT).

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“…To address the problems raised with the manual analysis of ECG signals, many studies in the literature explored using machine learning techniques to accurately detect the anomalies in the signal [3], [4]. Most of these approaches involve a preprocessing phase for preparing the signal (e.g., passing it through band-pass filters, etc).…”

Section: Introductionmentioning

confidence: 99%

ECG Heartbeat Classification: A Deep Transferable Representation

Kachuee

Fazeli

Sarrafzadeh

2018

2018 IEEE International Conference on Healthcare Informatics (ICHI)

Self Cite

335

215

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Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats. While there are many commonalities between different ECG conditions, the focus of most studies has been classifying a set of conditions on a dataset annotated for that task rather than learning and employing a transferable knowledge between different tasks. In this paper, we propose a method based on deep convolutional neural networks for the classification of heartbeats which is able to accurately classify five different arrhythmias in accordance with the AAMI EC57 standard. Furthermore, we suggest a method for transferring the knowledge acquired on this task to the myocardial infarction (MI) classification task. We evaluated the proposed method on PhysionNet's MIT-BIH and PTB Diagnostics datasets. According to the results, the suggested method is able to make predictions with the average accuracies of 93.4% and 95.9% on arrhythmia classification and MI classification, respectively.

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Non-invasive blood pressure estimation using phonocardiogram

Cited by 23 publications

References 6 publications

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Cuff-Less Blood Pressure Monitoring System Using Smartphones

ECG Heartbeat Classification: A Deep Transferable Representation

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