Novel computation of pulse transit time from multi-channel PPG signals by wavelet transform

Pielmuş, Alexandru-Gabriel; Pflugradt, Maik; Tigges, Timo; Klum, Michael; Feldheiser, Aarne; Hunsicker, Oliver; Orglmeister, Reinhold

doi:10.1515/cdbme-2016-0047

Cited by 14 publications

(11 citation statements)

References 12 publications

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“…Consequently, the same time accuracy would generate an error of 4.5% for subjects over 70 years. PWV depends on the heart rate and breathing, but relative changes of the PWV depend on the current blood pressure of the user [45,46]. All blood vessels can expand or contract as a result of sympathovagal activity; however, since most of the vascular resistance occurs in small blood vessels, changes caused by blood pressure are more visible in the finger PPG [45].…”

Section: Span: Synergy Of Information From Wearables and Iot Sensorsmentioning

confidence: 99%

“…Original PPG, filtered signal, and identified heart beats are shown in Figure 6. More sophisticated processing methods, such as wavelet transform [45], would provide better accuracy and assessment.…”

Section: Span: Synergy Of Information From Wearables and Iot Sensorsmentioning

confidence: 99%

“…We encountered several potential issues that can limit applicability of the proposed approach: (a) Motion artifacts can significantly influence quality of PPG; (b) PPG sampling frequency on the smartwatch is low (135 Hz), although sampling frequency on the smart bottle can be more than 1000 Hz; (c) hand position influences measured latency [45]; and d) frequency of smartwatch clocks can drift, which influences synchronization of signals. Our embedded sensor provides much more robust monitoring, since they are specialized only for the monitoring task, while smartwatches also represent a general computing/communication platform.…”

Section: Span: Synergy Of Information From Wearables and Iot Sensorsmentioning

confidence: 99%

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Wearables Meet IoT: Synergistic Personal Area Networks (SPANs)

Jovanov

2019

Sensors

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Wearable monitoring and mobile health (mHealth) revolutionized healthcare diagnostics and delivery, while the exponential increase of deployed “things” in the Internet of things (IoT) transforms our homes and industries. “Things” with embedded activity and vital sign sensors that we refer to as “smart stuff” can interact with wearable and ambient sensors. A dynamic, ad-hoc personal area network can span multiple domains and facilitate processing in synergistic personal area networks—SPANs. The synergy of information from multiple sensors can provide: (a) New information that cannot be generated from existing data alone, (b) user identification, (c) more robust assessment of physiological signals, and (d) automatic annotation of events/records. In this paper, we present possible new applications of SPANs and results of feasibility studies. Preliminary tests indicate that users interact with smart stuff—in our case, a smart water bottle—dozens of times a day and sufficiently long to collect vital signs of the users. Synergistic processing of sensors from the smartwatch and objects of everyday use may provide user identification and assessment of new parameters that individual sensors could not generate, such as pulse wave velocity (PWV) and blood pressure. As a result, SPANs facilitate seamless monitoring and annotation of vital signs dozens of times per day, every day, every time the smart object is used, without additional setup of sensors and initiation of measurements. SPANs creates a dynamic “opportunistic bubble” for ad-hoc integration with other sensors of interest around the user, wherever they go. Continuous long-term monitoring of user’s activity and vital signs can provide better diagnostic procedures and personalized feedback to motivate a proactive approach to health and wellbeing.

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Section: Span: Synergy Of Information From Wearables and Iot Sensorsmentioning

confidence: 99%

Section: Span: Synergy Of Information From Wearables and Iot Sensorsmentioning

confidence: 99%

Section: Span: Synergy Of Information From Wearables and Iot Sensorsmentioning

confidence: 99%

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Wearables Meet IoT: Synergistic Personal Area Networks (SPANs)

Jovanov

2019

Sensors

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“…Previous cuff-less and PTT-based studies are typically classified into three categories [4], [10]: 1) adopting only an electrocardiogram (ECG) signal [26], [27], 2) adopting both ECG and PPG signals [28]- [32], and 3) adopting only a PPG signal [22], [23], [33]. The research adopting the approaches of 1) and 2) can be difficult to apply in daily life, since the ECG signal is derived from sensors attached to the body.…”

Section: Introductionmentioning

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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“…In a precursory publication we have investigated the dependency of the pulse arrival and transit time (PAT, PTT) on the ABP, with results suggesting that PTT is a more reliable indicator for changes thereof [2]. However, the proposed method relied on the use of a reflective PPG sensor, whose signal quality was heavily influenced by sensor positioning, movement and skin composition of the subjects.…”

Section: Introductionmentioning

confidence: 99%

Correlation of arterial blood pressure to synchronous piezo, impedance and photoplethysmographic signal features

Alexandru-Gabriel

Osterland

Klum

et al. 2017

Current Directions in Biomedical Engineering

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In this paper we investigate which pulse wave pick-up technologies are well suited for blood pressure trend estimation. We use custom built hardware to acquire electrocardiographic, applanation-tonometric, photo-and impedance-plethysmographic signals during low intensity workouts. Beat-to-beat features and pulse wave runtimes are correlated to the reference arterial blood pressure. Temporal lag adjustment is performed to determine the latency of feature response. Best results are obtained for systolic arterial blood pressure. These suggest that every subject has a range of well-performing features, but it is not consistent among all. Spearman Rho values reach in excess of 0.8, with their significance being validated by p-values lower than 0.01.

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Novel computation of pulse transit time from multi-channel PPG signals by wavelet transform

Cited by 14 publications

References 12 publications

Wearables Meet IoT: Synergistic Personal Area Networks (SPANs)

Wearables Meet IoT: Synergistic Personal Area Networks (SPANs)

Cuff-Less Blood Pressure Monitoring System Using Smartphones

Correlation of arterial blood pressure to synchronous piezo, impedance and photoplethysmographic signal features

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