VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients

Lee, Hyung Chul; Park, Yoonsang; Yoon, Soo Bin; Yang, Seung Min; Park, Dongnyeok; Jung, Chul-Woo

doi:10.1038/s41597-022-01411-5

Cited by 69 publications

(41 citation statements)

References 21 publications

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“…This limitation makes the analysis that rely on the synchronization of ECG, PPG or ABP waveforms (e.g., extraction of the pulse arrival time), unreliable. Unlike the MIMIC database, however, waveform synchronization is secured in the VitalDB dataset ( 33 ). Therefore, we suggest using segments from VitalDB for the analysis requiring precise inter-signal alignment among ECG, PPG or ABP.…”

Section: Discussionmentioning

confidence: 99%

“…In the remaining databases, only the MIMIC-II and MIMIC-III waveform databases matched subsets and the VitalDB database contain subject identification and demographic information. Since the MIMIC-III matched subset is a superset of the MIMIC-II matched subset ( 32 ), the MIMIC-III matched subset ( 32 ) and the VitalDB database ( 33 , 37 ) were selected as the original sources.…”

Section: Methodsmentioning

confidence: 99%

“…The MIMIC-III matched subset v1.0 ( 32 ) includes 22,317 records from 10,282 patients who stayed in the critical care unit (ICU) of the Beth Israel Deaconess Medical Center. The VitalDB database ( 33 ) contains 6,388 records from 6,090 ICU patients who underwent surgeries in the Seoul National University Hospital. Records in these two datasets were checked and retrieved using the WaveForm DataBase (WFDB) toolbox ( 38 ), and the VitalDB Python library, under the following protocols:…”

Section: Methodsmentioning

confidence: 99%

“…Motivated by addressing these issues, here, we present PulseDB, the largest cleaned dataset to date, consisting of ECG, PPG and arterial BP (ABP) waveforms retrieved from the MIMIC-III waveform database matched subset ( 32 ) and the VitalDB waveform database ( 33 ). PulseDB offers the following features:

With 14,570 h of ECG, PPG and ABP waveforms (stored in

10

-s segments) from 5,361 subjects, PulseDB is the largest cleaned dataset to date.…”

Section: Introductionmentioning

confidence: 99%

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PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods

Wang

Mohseni²,

Kilgore³

et al. 2023

Front. Digit. Health

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There has been a growing interest in developing cuff-less blood pressure (BP) estimation methods to enable continuous BP monitoring from electrocardiogram (ECG) and/or photoplethysmogram (PPG) signals. The majority of these methods have been evaluated using publicly-available datasets, however, there exist significant discrepancies across studies with respect to the size, the number of subjects, and the applied pre-processing steps for the data that is eventually used for training and testing the models. Such differences make conducting performance comparison across models largely unfair, and mask the generalization capability of various BP estimation methods. To fill this important gap, this paper presents “PulseDB,” the largest cleaned dataset to date, for benchmarking BP estimation models that also fulfills the requirements of standardized testing protocols. PulseDB contains 1) 5,245,454 high-quality 10-s segments of ECG, PPG, and arterial BP (ABP) waveforms from 5,361 subjects retrieved from the MIMIC-III waveform database matched subset and the VitalDB database; 2) subjects’ identification and demographic information, that can be utilized as additional input features to improve the performance of BP estimation models, or to evaluate the generalizability of the models to data from unseen subjects; and 3) positions of the characteristic points of the ECG/PPG signals, making PulseDB directly usable for training deep learning models with minimal data pre-processing. Additionally, using this dataset, we conduct the first study to provide insights about the performance gap between calibration-based and calibration-free testing approaches for evaluating generalizability of the BP estimation models. We expect PulseDB, as a user-friendly, large, comprehensive and multi-functional dataset, to be used as a reliable source for the evaluation of cuff-less BP estimation methods.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

With 14,570 h of ECG, PPG and ABP waveforms (stored in

10

-s segments) from 5,361 subjects, PulseDB is the largest cleaned dataset to date.…”

Section: Introductionmentioning

confidence: 99%

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PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods

Wang

Mohseni²,

Kilgore³

et al. 2023

Front. Digit. Health

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“…Harnessing the intraoperative physiologic data, including waveforms, will allow gaining further insights. Recently, a large vital signs repository of perioperative patient data became publicly available [41]. This dataset contains high-resolution multiparameter data from 6388 cases, including waveform and numeric data of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory results.…”

Section: Future Directionsmentioning

confidence: 99%

Development and implementation of databases to track patient and safety outcomes

Mukasa

Kovacheva

2022

Current Opinion in Anaesthesiology

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Purpose of reviewRecent advancements in big data analytical tools and large patient databases have expanded tremendously the opportunities to track patient and safety outcomes.We discuss the strengths and limitations of large databases and implementation in practice with a focus on the current opportunities to use technological advancements to improve patient safety.Recent findingsThe most used sources of data for large patient safety observational studies are administrative databases, clinical registries, and electronic health records. These data sources have enabled research on patient safety topics ranging from rare adverse outcomes to large cohort studies of the modalities for pain control and safety of medications. Implementing the insights from big perioperative data research is augmented by automating data collection and tracking the safety outcomes on a provider, institutional, national, and global level. In the near future, big data from wearable devices, physiological waveforms, and genomics may lead to the development of personalized outcome measures.SummaryPatient safety research using large databases can provide actionable insights to improve outcomes in the perioperative setting. As datasets and methods to gain insights from those continue to grow, adopting novel technologies to implement personalized quality assurance initiatives can significantly improve patient care.

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Group Delay Effect Analysis Between Arterial Blood Pressure and Photoplethysmography Waveforms

Evdochim,

Dobrescu,

Dobrescu

et al. 2024

IFMBE Proceedings

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VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients

Cited by 69 publications

References 21 publications

PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods

PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods

Development and implementation of databases to track patient and safety outcomes

Group Delay Effect Analysis Between Arterial Blood Pressure and Photoplethysmography Waveforms

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