Unidad de Investigación y Desarrollo Tecnológico (UIDT), Hospital General de México "Dr. Eduardo Liceaga", Dr. Balmis 148, 06720, Cuauhtémoc, Doctores, Ciudad de México, México.
Instituto de Ingeniería, Universidad Nacional Autónoma de México, Av. Universidad 3000, 04510, Ciudad Universitaria, Coyoacán, Ciudad de México, México.
Med Biol Eng Comput. 2024 Feb;62(2):389-403. doi: 10.1007/s11517-023-02939-3. Epub 2023 Oct 25.
The photoacoustic effect is an attractive tool for diagnosis in several biomedical applications. Analyzing photoacoustic signals, however, is challenging to provide qualitative results in an automated way. In this work, we introduce a dynamic modeling scheme of photoacoustic sensor data to classify blood samples according to their physiological status. Thirty-five whole human blood samples were studied with a state-space model estimated by a subspace method. Furthermore, the samples are classified using the model parameters and the linear discriminant analysis algorithm. The classification performance is compared with time- and frequency-domain features and an autoregressive-moving-average model. As a result, the proposed analysis can predict five blood classes: healthy women and men, microcytic and macrocytic anemia, and leukemia. Our findings indicate that the proposed method outperforms conventional signal processing techniques to analyze photoacoustic data for medical diagnosis. Hence, the method is a promising tool in point-of-care devices to detect hematological diseases in clinical scenarios.
光声效应是几种生物医学应用中诊断的一种有吸引力的工具。然而，分析光声信号对于提供定性结果具有挑战性，难以实现自动化。在这项工作中，我们引入了一种光声传感器数据的动态建模方案，以根据生理状态对血液样本进行分类。使用子空间方法估计的状态空间模型研究了 35 个全人类血液样本。此外，使用模型参数和线性判别分析算法对样本进行分类。将分类性能与时间和频域特征以及自回归移动平均模型进行了比较。结果表明，所提出的分析可以预测五类血液：健康的女性和男性、小细胞性和大细胞性贫血以及白血病。我们的研究结果表明，与传统的信号处理技术相比，所提出的方法更适合分析光声数据以进行医学诊断。因此，该方法有望成为临床场景中用于检测血液疾病的即时检测设备中的一种有前途的工具。
