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影声传感器数据的动态建模用于分类人类血液样本
Argelia Pérez-Pacheco1, Roberto G Ramírez-Chavarría2, Rosa M Quispe-Siccha3
1Unidad de Investigación y Desarrollo Tecnológico (UIDT), Hospital General de México "Dr. Eduardo Liceaga", Dr. Balmis 148, 06720, Cuauhtémoc, Doctores, Ciudad de México, México. argeliapp@ciencias.unam.mx.
Medical & biological engineering & computing
|October 25, 2023
概括
这项研究引入了光声传感器数据的动态建模方案,使血液样本的自动分类成为可能. 这种新的方法准确地预测了五种血液类别,超过了用于检测血液病的传统技术.
科学领域:
- 生物医学光学 生物医学光学
- 信号处理 信号处理
- 血液学 血液学 血液学
背景情况:
- 光声学成像 (PA) 在生物医学中提供了诊断潜力.
- 对PA信号进行定性结果的自动分析仍然具有挑战性.
- 精确分类血液生理状态对于疾病诊断至关重要.
研究的目的:
- 为光声传感器数据开发一个动态建模方案.
- 根据生理状态对全人血液样本进行分类.
- 评估拟议方法的性能与传统技术相比.
主要方法:
- 一个状态空间模型使用子空间方法对35个全人血液样本进行了估计.
- 血液样本使用模型参数和线性差异分析进行分类.
- 性能与时间域,频率域特征以及自回归移动平均模型进行了比较.
主要成果:
- 拟议的分析成功预测了五种血液类别:健康 (女性和男性),微细胞贫血,宏细胞贫血和白血病.
- 动态建模方案显著超过了传统的信号处理方法.
- 该方法在根据血液学状况对血液样本进行分类时表现出高准确度.
结论:
- 动态建模方案为分析光声数据提供了强大的自动化方法.
- 这种方法对检测血液病的护理点设备有希望.
- 这些发现支持使用光声信号分析用于先进的医学诊断.
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