论文标题
验证基于SVM的新生儿癫痫发作检测算法，以实现普遍性，非效率和临床功效
Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy
论文作者
论文摘要
新生儿癫痫发作检测算法（SDA）正在接近人类专家注释的基准。需要采取算法的概括性和不介绍性以及临床功效的措施来评估新生儿SDA性能的全部范围。我们在28个新生儿的独立数据集上验证了新生儿SDA。通过将原始训练集（交叉验证）的性能与其在验证集上的性能进行比较，可以测试概括性。通过评估SDA组合与两个人类专家注释之间的观察者间一致性来测试非效率。通过比较SDA和人类专家如何量化癫痫发作负担并确定脑电图中临床意义的癫痫发作时期来测试临床功效。训练和验证集之间的算法性能是一致的，而AUC中没有显着恶化（p> 0.05，n = 28）。 SDA输出不如人类专家的注释，但是，重新培训数据的多样性导致了非近端性能（$Δκ$ = 0.077，95％CI：-0.002-0.232，n = 18）。 SDA对癫痫发作负担的评估的准确性范围为89-93％，鉴定临床利益期为87％。拟议的SDA正在接近人类等效性，并提供了对脑电图的临床相关解释。
Neonatal seizure detection algorithms (SDA) are approaching the benchmark of human expert annotation. Measures of algorithm generalizability and non-inferiority as well as measures of clinical efficacy are needed to assess the full scope of neonatal SDA performance. We validated our neonatal SDA on an independent data set of 28 neonates. Generalizability was tested by comparing the performance of the original training set (cross-validation) to its performance on the validation set. Non-inferiority was tested by assessing inter-observer agreement between combinations of SDA and two human expert annotations. Clinical efficacy was tested by comparing how the SDA and human experts quantified seizure burden and identified clinically significant periods of seizure activity in the EEG. Algorithm performance was consistent between training and validation sets with no significant worsening in AUC (p>0.05, n =28). SDA output was inferior to the annotation of the human expert, however, re-training with an increased diversity of data resulted in non-inferior performance ($Δκ$=0.077, 95% CI: -0.002-0.232, n=18). The SDA assessment of seizure burden had an accuracy ranging from 89-93%, and 87% for identifying periods of clinical interest. The proposed SDA is approaching human equivalence and provides a clinically relevant interpretation of the EEG.
