Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA.
Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Sensors (Basel). 2022 Nov 16;22(22):8851. doi: 10.3390/s22228851.
Bio-signals are being increasingly used for the assessment of pathophysiological conditions including pain, stress, fatigue, and anxiety. For some approaches, a single signal is not sufficient to provide a comprehensive diagnosis; however, there is a growing consensus that multimodal approaches allow higher sensitivity and specificity. For instance, in visceral pain subjects, the autonomic activation can be inferred using electrodermal activity (EDA) and heart rate variability derived from the electrocardiogram (ECG), but including the muscle activation detected from the surface electromyogram (sEMG) can better differentiate the disease that causes the pain. There is no wearable device commercially capable of collecting these three signals simultaneously. This paper presents the validation of a novel multimodal low profile wearable data acquisition device for the simultaneous collection of EDA, ECG, and sEMG signals. The device was validated by comparing its performance to laboratory-scale reference devices. N = 20 healthy subjects were recruited to participate in a four-stage study that exposed them to an array of cognitive, orthostatic, and muscular stimuli, ensuring the device is sensitive to a range of stressors. Time and frequency domain analyses for all three signals showed significant similarities between our device and the reference devices. Correlation of sEMG metrics ranged from 0.81 to 0.95 and EDA/ECG metrics showed few instances of significant difference in trends between our device and the references. With only minor observed differences, we demonstrated the ability of our device to collect EDA, sEMG, and ECG signals. This device will enable future practical and impactful advances in the field of chronic pain and stress measurement and can confidently be implemented in related studies.
生物信号越来越多地用于评估包括疼痛、压力、疲劳和焦虑在内的病理生理状况。对于某些方法,单一信号不足以提供全面的诊断;然而,越来越多的共识认为,多模态方法可以提高灵敏度和特异性。例如,在内脏疼痛患者中,可以使用皮肤电活动(EDA)和心电图(ECG)衍生的心率变异性来推断自主神经激活,但包括从表面肌电图(sEMG)检测到的肌肉激活可以更好地区分引起疼痛的疾病。目前还没有商业上能够同时采集这三种信号的可穿戴设备。本文介绍了一种新型的多模态低轮廓可穿戴数据采集设备,用于同时采集 EDA、ECG 和 sEMG 信号。该设备通过与实验室规模的参考设备进行性能比较来验证。招募了 20 名健康受试者参加一个四阶段的研究,使他们接触到一系列认知、直立和肌肉刺激,以确保设备对各种应激源敏感。对所有三种信号的时域和频域分析表明,我们的设备与参考设备之间存在显著相似性。sEMG 指标的相关性范围为 0.81 到 0.95,EDA/ECG 指标的趋势相关性很少有显著差异。我们观察到只有微小的差异,证明了我们的设备能够采集 EDA、sEMG 和 ECG 信号。该设备将为慢性疼痛和应激测量领域的未来实际和有影响力的进展提供支持,并可以在相关研究中自信地实施。