A New Image Blood Pressure Sensor Based on PPG, RRT, BPTT, and Harmonic Balancing

Huang, Sheng‐Chieh; Hung, Pei-Hsuan; Hong, Chung-Hung; Wang, Huimin

doi:10.1109/jsen.2014.2329676

Cited by 34 publications

(13 citation statements)

References 15 publications

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“…Most recently, Huang and colleagues developed a blood pressure transport theory for noninvasively monitoring systolic and diastolic blood pressures (SBPs and DBPs). Through simple calibration, this method could be implemented with any IPPG systems (e.g., smartphone-based IPPG) for blood pressure assessment [106]. Compared to the commercial Omron sphygmomanometer, this method achieved satisfactory levels of accuracy in determining both SBPs and DBPs based upon the standards (mean < ±5 mmHg, S.D.…”

Section: Recent Advances In Ippgmentioning

confidence: 99%

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

Sun

Thakor

2016

IEEE Trans. Biomed. Eng.

465

238

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Photoplethysmography (PPG) is a non-invasive optical technique for detecting microvascular blood volume changes in tissues. Its ease of use, low cost and convenience make it an attractive area of research in the biomedical and clinical communities. Nevertheless, its single spot monitoring and the need to apply a PPG sensor directly to the skin limit its practicality in situations such as perfusion mapping and healing assessments or when free movement is required. The introduction of fast digital cameras into clinical imaging monitoring and diagnosis systems, the desire to reduce the physical restrictions, and the possible new insights that might come from perfusion imaging and mapping inspired the evolution of conventional PPG technology to imaging PPG (IPPG). IPPG is a noncontact method that can detect heart-generated pulse waves by means of peripheral blood perfusion measurements. Since its inception, IPPG has attracted significant public interest and provided opportunities to improve personal healthcare. This study presents an overview of the wide range of IPPG systems currently being introduced along with examples of their application in various physiological assessments. We believe that the widespread acceptance of IPPG is happening, and it will dramatically accelerate the promotion of this healthcare model in the near future.

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Section: Recent Advances In Ippgmentioning

confidence: 99%

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

Sun

Thakor

2016

IEEE Trans. Biomed. Eng.

465

238

View full text Add to dashboard Cite

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“…5,16,17 Photoplethysmography (PPG) sensors have been used for wireless monitoring of heart rate 23 and blood pressure. 24 PPG sensors are advantageous because they can be manufactured in a small size, have been shown to work as a wearable technology, and implantation is not necessary for monitoring. These sensors can monitor high-volume blood flow, such as in an arteriovenous fistula.…”

Section: Discussionmentioning

confidence: 99%

A Novel Technology for Free Flap Monitoring: Pilot Study of a Wireless, Biodegradable Sensor

Oda

Beker

Kaizawa

et al. 2019

J Reconstr Microsurg

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Background Accurate monitoring of free flap perfusion after complex reconstruction is critical for early recognition of flap compromise. Surgeons use a variety of subjective and objective measures to evaluate flap perfusion postoperatively. However, these measures have some limitations. We have developed a wireless, biodegradable, and flexible sensor that can be applied to real-time postoperative free flap monitoring. Here we assess the biocompatibility and function of our novel sensor. Methods Seven Sprague–Dawley (SD) rats were used for biocompatibility studies. The sensor was implanted around the femoral artery near the inguinal ligament on one leg (implant side) and sham surgery was performed on the contralateral leg (control side). At 6 and 12 weeks, samples were harvested to assess the inflammation within and around the implant and artery. Two animals were used to assess sensor function. Sensor function was evaluated at implantation and 7 days after the implantation. Signal changes after venous occlusion were also assessed in an epigastric artery island flap model. Results In biocompatibility studies, the diameter of the arterial lumen and intima thickness in the implant group were not significantly different than the control group at the 12-week time point. The number of CD-68 positive cells that infiltrated into the soft tissue, surrounding the femoral artery, was also not significantly different between groups at the 12-week time point. For sensor function, accurate signaling could be recorded at implantation and 7 days later. A change in arterial signal was noted immediately after venous occlusion in a flap model. Conclusion The novel wireless, biodegradable sensor presented here is biocompatible and capable of detecting arterial blood flow and venous occlusion with high sensitivity. This promising new technology could combat the complications of wired sensors, while improving the survival rate of flaps with vessel compromise due to its responsive nature.

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“…where y is a one-hot encoding vector and indicates the truly probability distribution. Our model is trained by a Back-Propagation Through Time (BPTT) algorithm [40] in the learning process.…”

Section: Predicting Processmentioning

confidence: 99%

A Time-Aware Graph Neural Network for Session-Based Recommendation

2020

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Recently, Graph Neural Networks (GNNs) have attracted increasing attention in the field of session-based recommendation, due to its strong ability on capturing complex interactive transitions within sessions. However, existing GNN-based models either lack the use of user's long-term historical behaviors or fail to address the impact of collaborative filtering information from neighbor users on the current session, which are both important to boost recommendation. In addition, previous work only focuses on the sequential relations of interactions while neglects the time interval information which can imply the correlations between different interactions. To tackle these problems, we propose a Time-Aware Graph Neural Network (TA-GNN) for session-based recommendation. Specifically, we first construct a user behavior graph by linking the interacted items of the same user according to their corresponding time order. A time-aware generator is designed to model the correlations between different nodes of the user behavior graph by considering the time interval information. Moreover, items from the neighbor sessions of the current session are selected to build a neighborhood graph. Then the two graphs are respectively processed by two different modules to learn the representation of the current session, which is applied to produce the final recommendation list. Comprehensive experiments show that our model outperforms state-of-the-art baselines on three real world datastes. We also investigate the performance of TA-GNN on different numbers of historical interactions and on different session length, finding that our model presents consistently advantages under different conditions.

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A New Image Blood Pressure Sensor Based on PPG, RRT, BPTT, and Harmonic Balancing

Cited by 34 publications

References 15 publications

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

A Novel Technology for Free Flap Monitoring: Pilot Study of a Wireless, Biodegradable Sensor

A Time-Aware Graph Neural Network for Session-Based Recommendation

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