机器学习用于婴儿哭声分类和病态哭声检测的应用:系统性审查
Sudhathai Sirithepmontree1,2, Nattasit Katchamat1,3, Sasitara Nuampa2
1School of Nursing, The University of Texas at Austin, Austin, TX, USA.
Science progress
|January 14, 2026
概括
机器学习准确地根据需求和病理学分类婴儿的哭声. 这项技术对早期检测和诊断充满希望,对于诸如聋等特定疾病具有很高的准确率.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 儿科 儿科 儿科
背景情况:
- 婴儿的哭泣是对需求和痛苦的关键沟通信号.
- 准确的哭声解释对于及时干预和诊断至关重要.
- 机器学习 (ML) 为复杂的声学模式提供了先进的分析能力.
研究的目的:
- 系统地审查和综合关于婴儿哭声分类中的ML应用的研究.
- 评估ML模型在识别婴儿需求相关和病理性哭声方面的准确性.
- 评估不同ML算法和cry分析特征的性能.
主要方法:
- 按照PRISMA指导方针进行系统的文献审查,在PROSPERO.中注册.
- 在PubMed,CINAHL,Embase和IEEE Xplore搜索了从2014年到2024年的研究.
- 使用QUADAS-2工具评估研究质量,并综合了来自17项纳入研究的发现.
主要成果:
- ML有效地将婴儿的哭声分为与需求相关的 (9 个亚型) 和病理性的 (6 个亚型).
- 分类准确度在44.5%至99.82%之间,根据ML模型和特征而有所不同.
- 获得的最高准确率:饥饿/疼痛哭声 (GMM) 的99.82%和聋检测的99.42%-99.82% (模糊模型/GMM).
结论:
- ML证明了准确的婴儿哭声分类和病理检测的巨大潜力.
- 未来的研究应该专注于各种数据集,现实世界的验证,并将哭声分析与生理信号相结合.
- 增强的ML模型可以提高诊断准确性和婴儿护理结果.
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相关概念视频
01:31Hearing
When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
01:31Respiratory System Abnormal Finding II: Palpation and Auscultation
In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities: