首次标准化评估胎儿心音检测方法，揭示简单模型也可媲美复杂算法。
Standardized Evaluation of Fetal Phonocardiography Processing Methods
- 构建统一评测平台，对多种心音与心率检测方法进行公平对比。
- 最佳模型第一心音检测达97.6% F1分数，第二心音为91.4%，心率误差0.644。
- 强调需进一步统一评估标准，开源代码与数据已公开可用。
胎心音图可无创获取胎儿心率及直接心音数据，且不使用任何辐射。本文综述现有胎儿心音检测与心率估计方法，并通过统一基准平台和预选测试数据集进行对比。相比以往综述，本研究以标准化方式评估各方法，确保公平性。测试内容包括容错检测准确率、标签插入、删除、替换错误率，以及心率均方误差等统计指标。结果表明，尚无单一方法在所有测试中表现最优，简单方法性能可与复杂方法相当。最佳模型在第一心音检测中实现97.6% F1分数、97.4%阳性预测值，平均绝对误差12.2±8.0毫秒；第二心音检测最佳结果为91.4% F1分数、91.3%阳性预测值，平均绝对误差17.3±12.2毫秒；心率估计最佳方法达到0.644均方误差。研究结论强调需进一步推进胎儿心率与心音检测方法的评估标准化。测试流程与算法实现已开源：https://github.com/mulkr/standard-fpcg-evaluation。
原文摘要 · Abstract (English)
Motivation. Phonocardiography can give access to the fetal heart rate as well as direct heart sound data, and is entirely passive, using no radiation of any kind. Approach. We discuss the currently available methods for fetal heart sound detection and heart rate estimation and compare them using a common benchmarking platform and a pre-selected testing dataset. Compared to previous reviews, we evaluated the discussed methods in a standardized manner for a fair comparison. Our tests included tolerance-based detection accuracy, error rates for label insertions, deletions, and substitutions, and statistical measures for heart rate mean square error. Results. Based on our results, there is no definite best method that can achieve the highest scores in all of the tests, and simpler methods could perform comparably to more complex ones. The best model for first heart sound detection achieved 97.6% F1-score, 97.4% positive predictive value, and 12.2+-8.0 ms mean absolute error. In terms of second heart sound detection the best model had 91.4% F1-score, 91.3% positive predictive value, and 17.3+-12.2 ms mean absolute error. For fetal heart rate a 0.644 mean square error was achieved by the best method. Significance. Our main conclusion is that further standardization is required in fetal heart rate and heart sound detection method evaluation. The tests and algorithm implementations are openly available at: https://github.com/mulkr/standard-fpcg-evaluation.
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