School of Electronics Engineering (SENSE), VIT University, Vellore, Tamil Nadu, India.
Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia.
Comput Methods Programs Biomed. 2017 Jul;145:67-72. doi: 10.1016/j.cmpb.2017.04.013. Epub 2017 Apr 14.
The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial.
This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system.
The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset.
The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069.
The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS.
在多种医学情况下，监测呼吸频率至关重要，包括睡眠呼吸暂停，因为与对照组相比，睡眠呼吸暂停患者的呼吸频率不规则。因此，通过检测不同的呼吸阶段来监测呼吸频率至关重要。
本研究旨在使用基于呼吸相位检测的新开发的自适应神经模糊推理系统（ANFIS）对肺声信号进行呼吸周期分段，并随后评估该系统的性能。
对每个分段的归一化平均功率谱密度进行模糊化，并制定了一组模糊规则。开发了 ANFIS 来检测呼吸相位，然后执行呼吸循环分段。为了评估所提出方法的性能，计算并分析了均方根误差（RMSE）和相关系数值，并使用 KIMS 医院和 RALE 标准数据集收集的数据对所提出的方法进行了验证。
为了评估其性能而进行的神经模糊模型相关系数分析表明，相关强度为 r = 0.9925，并且发现神经模糊模型的 RMSE 等于 0.0069。
与模糊推理系统（FIS）相比，所提出的神经模糊模型在检测呼吸阶段和分段呼吸周期方面表现更好，并且需要的规则比 FIS 少。
