Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth

Athaya, Tasbiraha; Choi, Sunwoong

doi:10.3390/bios12080655

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…Clinicians' mixed views on telemedicine are echoed in other global and Australian studies 27–30 . Advances in remote assessments of patients in surgical disciplines suggest that some diagnostic and assessment challenges can be addressed, assisted by guidance documents, technical innovations and digital literacy 3,31–39 . However, most clinicians largely favoured in‐person consultations for many clinical problems, citing the value of physical cues 40,41 .…”

Section: Discussionmentioning

confidence: 99%

Telemedicine in specialist outpatient care during COVID‐19: a qualitative study

Sengupta,

Pettigrew,

Jenkins

2023

Internal Medicine Journal

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BackgroundCOVID‐19 accelerated healthcare changes, introducing various telehealth services. Work is needed to determine the suitability of telemedicine in the post‐pandemic era.AimsTo explore perceptions and experiences of telemedicine among patients and providers (clinicians and health administrators) who were involved in telemedicine appointments in hospital outpatient clinics in 2020–2022.Design, setting and participantsQualitative study: semi‐structured interviews were conducted with 37 participants (16 patients and 21 providers) in various hospital specialist outpatient clinics in a New South Wales local health district.ResultsPatients were generally satisfied with telemedicine consultations, especially during COVID restrictions, because of the convenience of accessing care from home and minimising the risk of COVID exposure. However, patients considered that the inability to receive a physical examination was a significant disadvantage of telemedicine. Providers had ambivalent perceptions and expressed concerns about mis‐ and under‐diagnoses because of the inability to conduct physical examinations. They considered telemedicine suitable for review appointments but noted an associated increased workload and stressed the need for sustainable funding models (Medicare items). Both patients and providers recognised the need for education/training and better integration of telemedicine platforms into existing infrastructure to facilitate an optimal hybrid model of care.ConclusionDespite expressing some concerns over its limitations, patients valued telemedicine for its convenience and for meeting their needs during the pandemic. While acknowledging that patients experienced some benefits from telemedicine, clinicians expressed concerns about potential missed diagnoses, uncertain clinical outcomes and lack of administrative and technological infrastructure. The ultimate test of telemedicine will be its impact on clinical outcomes versus longstanding models of in‐person care.

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Section: Discussionmentioning

confidence: 99%

Telemedicine in specialist outpatient care during COVID‐19: a qualitative study

Sengupta,

Pettigrew,

Jenkins

2023

Internal Medicine Journal

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“…In the study of non-invasive blood pressure estimation using PPG signals, U-Net performed very well, so we chose U-Net as the model architecture for this study [27][28][29]. U-Net is a convolutional neural network architecture originally proposed by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in a 2015 paper [30].…”

Section: Model Constructionmentioning

confidence: 99%

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform

Xu,

Tang,

Chen

2023

Sensors

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We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was −0.04 L/min with a −1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between −20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.

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