Clairmonte Nathan, Skoric James, D'Mello Yannick, Hakim Siddiqui, Aboulezz Ezz, Lortie Michel, Plant David
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:221-224. doi: 10.1109/EMBC44109.2020.9176119.
Non-invasive health monitoring has the potential to improve the delivery and efficiency of medical treatment.
This study was aimed at developing a neural network to classify the lung volume state of a subject (i.e. high lung volume (HLV) or low lung volume (LLV), where the subject had fully inhaled or exhaled, respectively) by analyzing cardiac cycles extracted from vibrational cardiography (VCG) signals.
A total of 15619 cardiac cycles were recorded from 50 subjects, of which 9989 cycles were recorded in the HLV state and the remaining 5630 cycles were recorded in the LLV state. A 1D convolutional neural network (CNN) was employed to classify the lung volume state of these cardiac cycles.
The CNN model was evaluated using a train/test split of 80/20 on the data. The developed model was able to correctly classify the lung volume state of 99.4% of the testing data.
VCG cardiac cycles can be classified based on lung volume state using a CNN.
These results provide evidence of a correlation between VCG and respiration volume, which could inform further analysis into VCG-based cardio-respiratory monitoring.
非侵入式健康监测有潜力改善医疗服务的提供和效率。
本研究旨在开发一种神经网络，通过分析从振动心动图（VCG）信号中提取的心动周期，对受试者的肺容积状态进行分类（即高肺容积（HLV）或低肺容积（LLV），分别对应受试者完全吸气或呼气时的状态）。
从50名受试者中记录了总共15619个心动周期，其中9989个周期是在高肺容积状态下记录的，其余5630个周期是在低肺容积状态下记录的。采用一维卷积神经网络（CNN）对这些心动周期的肺容积状态进行分类。
使用数据的80/20训练/测试分割对CNN模型进行评估。所开发的模型能够正确分类99.4%的测试数据的肺容积状态。
可以使用CNN根据肺容积状态对VCG心动周期进行分类。
这些结果提供了VCG与呼吸量之间相关性的证据，这可为基于VCG的心肺监测的进一步分析提供参考。
