![]() ![]() Previously, we developed the Vital Recorder program, a data capture software that records time-synchronized high-resolution data from various anesthesia devices including patient monitors, anesthesia machines, brain monitors, cardiac monitors, target-controlled infusion pumps, and rapid infusion system 9. In general, obtaining high-quality vital signs data in surgical patients is considered technically difficult or very expensive. Electronic medical records (EMR) systems and automated anesthesia records (AAR) are important sources of biosignal big datasets, however, they have limited capabilities because (1) most EMR systems and AARs only store low time resolution data that are insufficient for interpretation of dynamic physiological changes during surgery (2) essential waveform data such as electrocardiography, photoplethysmography, electroencephalography, and airway pressure waves are not stored on most systems due to cost or technical limitations, and (3) current recording systems do not fully support integrated recording of data from multiple devices 7, 8. However, the lack of large-scale, high-resolution biosignal data required for machine learning has been a major obstacle to the development or improvement of biosignal algorithms. The relationship between various vital signs was also elucidated using artificial intelligence resulting in practical high-performance algorithms in the medical field 5, 6. Recent advances in machine learning technologies such as one-dimensional convolutional neural network allowed more accurate interpretation of the complex time-series biosignals 4. ![]() Numerous studies have shown that these secondary parameters are useful for optimizing patient care during surgery and greatly improve postoperative outcomes 1, 2, 3. ![]() Modern anesthesia widely adopts advanced patient monitors that present a variety of secondary parameters such as electroencephalogram-based anesthesia depth index, arterial pressure-derived cardiac output, and electrocardiography and photoplethysmography-based analgesia index. These vital signs are usually used as-is, but sometimes converted into clinically useful secondary parameters developed through mathematical, engineering, and medical algorithms. Intraoperative vital signs such as electrocardiography, blood pressure, percutaneous oxygen saturation, and body temperature are objective measures of physiologic function and are tracked with high-acuity patient monitors during surgery and anesthesia. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development. The dataset can be freely accessed and analysed using application programming interfaces and Python library. All data is stored in the public cloud after anonymization. This dataset contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. ![]() Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |