Zhu Yongwei, Zhang Haihong, Jayachandran Maniyeri, Ng Andrew Keong, Biswas Jit, Chen Zhihao
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5203-6. doi: 10.1109/EMBC.2013.6610721.
Ballistocardiography (BCG) is a promising unobtrusive method for home e-healthcare systems, and has attracted increasing interest in recent years along with technological advances in related biomedical, electrical engineering and computer science fields. While existing systems have investigated the efficacy of BCG setups in bed, backrest, seat or scale positions, we propose to study BCG in headrest position that will allow new practical and portable applications. To this end, we designed and implemented a multi-modality sensing system including a high-sensitivity microbend fiber optic BCG sensor. In this preliminary study, we have collected multi-modality physiological data on 3 human subjects. We ran extensive analysis on BCG in correlation with ECG, and identified special characteristics of the signal in the new BCG setup. The result suggests that new appropriate computing techniques are necessary for accurately recovering the heart beat signal. Therefore, we developed a novel algorithm for heart beat detection. We evaluate the algorithm with the data and demonstrate that it can accurately compute heart rate intervals in the headrest BCG despite significant signal distortion.
心冲击图描记术(BCG)是一种用于家庭电子医疗保健系统的很有前景的非侵入性方法,近年来随着相关生物医学、电气工程和计算机科学领域的技术进步,它受到了越来越多的关注。虽然现有系统已经研究了BCG在床、靠背、座椅或秤等位置设置的功效,但我们建议研究头枕位置的BCG,这将带来新的实用和便携式应用。为此,我们设计并实现了一个多模态传感系统,其中包括一个高灵敏度微弯光纤BCG传感器。在这项初步研究中,我们收集了3名人类受试者的多模态生理数据。我们对BCG与心电图的相关性进行了广泛分析,并确定了新BCG设置中信号的特殊特征。结果表明,需要新的合适计算技术来准确恢复心跳信号。因此,我们开发了一种用于心跳检测的新算法。我们用这些数据对该算法进行了评估,并证明它能够在头枕BCG中准确计算心率间期,尽管信号存在明显失真。