Multichannel ECG recording from waist using textile sensors

Abstract

Background: The development of wearable health monitoring systems is garnering tremendous interest in research, technology and commercial applications. Their ability of providing unique capabilities in continuous, real-time, and non-invasive tracking of the physiological markers of users can provide insights into the performance and health of individuals. Electrocardiogram (ECG) signals are of particular interest, as cardiovascular disease is the leading cause of death globally. Monitoring heart health and its conditions such as ventricular disturbances and arrhythmias can be achieved through evaluating various features of ECG such as R-peaks, QRS complex, T-wave, and P-wave. Despite recent advances in biosensors for wearable applications, most of the currently available solutions rely solely on a single system attached to the body, limiting the ability to obtain reliable and multi-location biosignals. However, in engineering systems, sensor fusion, which is the optimal integration and processing of data from multiple sensors, has been a common theme and should be considered for wearables. In recent years, due to an increase in the availability and variety of different types of sensors, the possibility of achieving sensor fusion in wearable systems has become more attainable. Sensor fusion in multi-sensing systems results in significant enhancements of information inferences compared to those from systems with a sole sensor. One step towards the development of sensor fusion for wearable health monitoring systems is the accessibility to multiple reliable electrophysiological signals, which can be recorded continuously. Results: In this paper, we develop a textile-based multichannel ECG band that has the ability to measure ECG from multiple locations on the waist. As a proof of concept, we demonstrate that ECG signals can be reliably obtained from different locations on the waist where the shape of the QRS complex is nearly comparable with recordings from the chest using traditional gel electrodes. In addition, we develop a probabilistic approach-based on prediction and update strategies-to detect R-peaks from noisy textile data in different statuses, including sitting, standing, and jogging. In this approach, an optimal search method is utilized to detect R-peaks based on the history of the intervals between previously detected R-peaks. We show that the performance of our probabilistic approach in R-peak detection is significantly better than that based on Pan-Tompkins and optimal-threshold methods.

Textile-Based Sensors for Biosignal Detection and Monitoring

“…Generally, the ECG signal consists of a prominent QRS-complex, a preceding smaller P-wave and followed by a T-wave (Figure 1) [18]. While the detection of diverse illnesses of the heart necessitates investigating the full signal, including the heights of Pand T-waves, as well as the time distances between them and the QRS-complex, for other applications, it is sufficient to detect the pulse, i.e., the times at which the R-peak as the most prominent peak occurs.…”

“…For this case, Alizadeh Meghrazi et al suggested a multi-sensor system based on knitted conductive yarns which they applied on the waist, as opposed to the more common positioning at the chest. They developed a probabilistic approach to find R-peaks in noisy data, stemming from these textile electrodes, even if the probands were jogging, and suggested inserting textile electrodes in underwear, combined with this evaluation process, to measure the pulse constantly [18]. [18], originally published under a CC-BY license.…”

Signal Quality Assessment of a Novel ECG Electrode for Motion Artifact Reduction

“…Nonstandard single-lead placement has often been considered in several studies assessing signal quality, e.g., two electrodes placed on each side of the sternum rather close to the precordial leads and [ 25 , 27 ], one electrode placed at position A and the other electrode higher up than position I [ 23 ], two electrodes placed on the forearm at a distance of 8 cm [ 26 ], and different locations on the waist of two electrodes (textile sensors) [ 43 ]. In a few studies, the assessment has been based on standard lead systems, i.e., the standard 12-lead ECG [ 31 ] and the EASI lead system except lead AI [ 32 ]; it is unclear why this particular lead was disregarded.…”

“…Had the subjects worn a garment, the results would likely have been different due to electrostatic and mechanical noise induced by the garment. Studies assessing the signal quality of textile ECG electrodes usually involve subjects with a garment, e.g., a T-shirt, hosting the electrodes [ 25 , 31 , 43 ].…”

Smart Wearables for Cardiac Monitoring—Real-World Use beyond Atrial Fibrillation

“…More recently, other forms of wearables with ECG monitoring capabilities have been designed. An example is the smart textile-based garments that have the ability to measure ECG from multiple locations [ 20 , 21 , 22 ]. These novel technologies may further expand this already rapidly developing field.…”