Krasteva Vessela, Jekova Irena, Leber Remo, Schmid Ramun, Abächerli Roger
Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad G Bonchev Str. Bl 105, 1113 Sofia, Bulgaria.
Physiol Meas. 2016 Aug;37(8):1273-97. doi: 10.1088/0967-3334/37/8/1273. Epub 2016 Jul 25.
False intensive care unit (ICU) alarms induce stress in both patients and clinical staff and decrease the quality of care, thus significantly increasing both the hospital recovery time and rehospitalization rates. In the PhysioNet/CinC Challenge 2015 for reducing false arrhythmia alarms in ICU bedside monitor data, this paper validates the application of a real-time arrhythmia detection library (ADLib, Schiller AG) for the robust detection of five types of life-threatening arrhythmia alarms. The strength of the application is to give immediate feedback on the arrhythmia event within a scan interval of 3 s-7.5 s, and to increase the noise immunity of electrocardiogram (ECG) arrhythmia analysis by fusing its decision with supplementary ECG quality interpretation and real-time pulse wave monitoring (quality and hemodynamics) using arterial blood pressure or photoplethysmographic signals. We achieved the third-ranked real-time score (79.41) in the challenge (Event 1), however, the rank was not officially recognized due to the 'closed-source' entry. This study shows the optimization of the alarm decision module, using tunable parameters such as the scan interval, lead quality threshold, and pulse wave features, with a follow-up improvement of the real-time score (80.07). The performance (true positive rate, true negative rate) is reported in the blinded challenge test set for different arrhythmias: asystole (83%, 96%), extreme bradycardia (100%, 90%), extreme tachycardia (98%, 80%), ventricular tachycardia (84%, 82%), and ventricular fibrillation (78%, 84%). Another part of this study considers the validation of ADLib with four reference ECG databases (AHA, EDB, SVDB, MIT-BIH) according to the international recommendations for performance reports in ECG monitors (ANSI/AAMI EC57). The sensitivity (Se) and positive predictivity (+P) are: QRS detector QRS (Se, +P) > 99.7%, ventricular ectopic beat (VEB) classifier VEB (Se, +P) = 95%, and ventricular fibrillation detector VFIB (P + = 94.8%) > VFIB (Se = 86.4%), adjusted to the clinical setting requirements, giving preference to low false positive alarms.
重症监护病房（ICU）的误报警会给患者和医护人员带来压力，降低护理质量，从而显著延长患者的住院康复时间并提高再住院率。在2015年旨在减少ICU床边监测数据中误发性心律失常警报的生理信号挑战赛（PhysioNet/CinC Challenge）中，本文验证了实时心律失常检测库（ADLib，席勒公司）在可靠检测五种危及生命的心律失常警报方面的应用。该应用的优势在于能在3秒至7.5秒的扫描间隔内对心律失常事件立即给出反馈，并通过将其判定结果与补充的心电图质量解读以及利用动脉血压或光电容积脉搏波信号进行的实时脉搏波监测（质量和血流动力学）相融合，提高心电图心律失常分析的抗噪声能力。我们在挑战赛（事件1）中获得了实时评分第三名（79.41），然而，由于参赛作品为“闭源”，该排名未得到官方认可。本研究展示了对警报判定模块的优化，使用了诸如扫描间隔、导联质量阈值和脉搏波特征等可调参数，随后实时评分有所提高（80.07）。在针对不同心律失常的盲法挑战赛测试集中报告了性能（真阳性率、真阴性率）：心脏停搏（83%，96%）、极度心动过缓（100%，90%）、极度心动过速（98%，80%）、室性心动过速（84%，82%）和心室颤动（78%，84%）。本研究的另一部分根据心电图监测器性能报告的国际建议（ANSI/AAMI EC57），使用四个参考心电图数据库（AHA、EDB、SVDB、MIT - BIH）对ADLib进行了验证。灵敏度（Se）和阳性预测值（+P）分别为：QRS波检测器QRS（Se，+P）＞99.7%，室性早搏（VEB）分类器VEB（Se，+P） = 95%，心室颤动检测器VFIB（P + = 94.8%）＞VFIB（Se = 86.4%），并根据临床设置要求进行了调整，优先考虑低误报率。
