Design and Validation of a Multimodal Wearable Device for Simultaneous Collection of Electrocardiogram, Electromyogram, and Electrodermal Activity.

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.

A Preliminary Study on Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning.

The electrodermal activity (EDA) signal is a sensitive and non-invasive surrogate measure of sympathetic function. Use of EDA has increased in popularity in recent years for such applications as emotion and stress recognition; assessment of pain, fatigue, and sleepiness; diagnosis of depression and epilepsy; and other uses. Recently, there have been several studies using ambulatory EDA recordings, which are often quite useful for analysis of many physiological conditions. Because ambulatory monitoring uses wearable devices, EDA signals are often affected by noise and motion artifacts. An automated noise and motion artifact detection algorithm is therefore of utmost importance for accurate analysis and evaluation of EDA signals. In this paper, we present machine learning-based algorithms for motion artifact detection in EDA signals. With ten subjects, we collected two simultaneous EDA signals from the right and left hands, while instructing the subjects to move only the right hand. Using these data, we proposed a cross-correlation-based approach for non-biased labeling of EDA data segments. A set of statistical, spectral and model-based features were calculated which were then subjected to a feature selection algorithm. Finally, we trained and validated several machine learning methods using a leave-one-subject-out approach. The classification accuracy of the developed model was 83.85% with a standard deviation of 4.91%, which was better than a recent standard method that we considered for comparison to our algorithm.

Validation of Spectral Indices of Electrodermal Activity with a Wearable Device.

Electrodermal activity (EDA) has been found to be a highly sensitive, accurate and non-invasive measure of the sympathetic nervous system's activity and has been used to extract biomarkers of various pathophysiological conditions including stress, fatigue, epilepsy, and chronic pain. Recently, various robust signal processing techniques have been developed to obtain more reliable and accurate indices that capture the meaningful characteristics of the EDA using data collected from laboratory-scale devices. However, EDA also has the potential to monitor such physiological conditions in active ambulatory settings, for which the developed tools must be deployed in wearable devices. In this paper, we studied the feasibility of obtaining the highly-sensitive spectral indices of EDA using a wearable device. EDA signals were collected from left hand fingers using a wearable device and a laboratory-scale reference device, while N=18 subjects underwent the Head up Tilt test and the Stroop test to stimulate orthostatic and cognitive stress, respectively. We computed two time-domain indices, the skin conductance level (SCL) and nonspecific skin conductance responses (NS.SCRs), and two spectral indices, the normalized sympathetic components of the EDA (EDASympn), and the time-varying EDA index of sympathetic control (TVSymp). The results showed similar performances for EDASympn and TVSymp indices across both devices. While spectral indices obtained from both devices performed similarly in response to orthostatic and cognitive stress, time-domain exhibited large variation when obtained by the wearable device. Further research is required to develop and refine such devices, as well as the indices used to analyze EDA results.Clinical Relevance- This study proves the feasibility of obtaining spectral indices of EDA using a wearable device, which can be used to develop wearable tools to detect pain, stress, fatigue, between others.