Department of Biomedical EngineeringLund University 221 00 Lund Sweden.
Department of MedicineKarolinska Institutet 171 77 Stockholm Sweden.
IEEE J Transl Eng Health Med. 2024 May 7;12:480-487. doi: 10.1109/JTEHM.2024.3397739. eCollection 2024.
Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT.
The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable.
The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively.
The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector.Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.
非持续性室上性心动过速（nsSVT）与心房颤动（AF）的发生风险较高相关，因此，nsSVT 的检测可以提高 AF 筛查效率。然而，由于使用手持式设备记录的心电图信号质量较低以及可能模拟 nsSVT 节律特征的异位搏动的存在，检测受到了挑战。
本研究提出了一种新的用于单导联、30 秒 ECG 的 nsSVT 检测器，基于这样的假设，即 nsSVT 发作中的搏动具有相似的形态，这意味着由于异位搏动或噪声/伪影而具有不同形态的搏动的发作被排除在外。支持向量机用于在滑动窗口中对连续的 5 个搏动序列进行分类，以获得相似的形态。由于缺乏足够的训练数据，使用具有不同信噪比的模拟 ECG 对分类器进行训练。在随后的步骤中，应用一组节律标准来确保类似的搏动序列的发作持续时间和心率是可以接受的。
使用 StrokeStop II 数据库评估了所提出的检测器的性能，其敏感性、特异性和阳性预测值分别为 84.6%、99.4%和 18.5%。
结果表明，使用所提出的检测器可以显著降低专家审查负担（降低 6 倍）。临床和转化意义：专家审查负担的降低表明，在 AF 筛查中进行 nsSVT 检测可以大大提高效率。
