Electrocardiogram Derived Respiratory Rate Using a Wearable Armband

Abstract

A method for deriving respiratory rate from an armband, which records three-channel electrocardiogram (ECG) using three pairs of dry (no hydrogel) electrodes, is presented. The armband device is especially convenient for long-term (monthsyears) monitoring because it does not use obstructive leads nor hydrogels/adhesives, which cause skin irritation even after few days. An ECG-derived respiration (EDR) based on respirationrelated modulation of QRS slopes and R-wave angle approach was used. Moreover, we modified the EDR algorithm to lower the computational cost. Respiratory rates were estimated with the armband-ECG and the reference plethysmography-based respiration signals from 15 subjects who underwent breathing experiment consisting of five stages of controlled breathing (at 0.1, 0.2, 0.3, 0.4, and 0.5 Hz) and one stage of spontaneous breathing. The respiratory rates from the armband obtained a relative error with respect to the reference (respiratory rate estimated from the plethysmography-based respiration signal) that was not higher than 2.26% in median nor interquartile range (IQR) for all stages of fixed and spontaneous breathing, and not higher than 3.57% in median nor IQR in the case when the low computational cost algorithm was applied. These results demonstrate that respiration-related modulation of the ECG morphology are also present in the armband ECG device. Furthermore, these results suggest that respiration-related modulation can be exploited by the EDR method based on QRS slopes and R-wave angles to obtain respiratory rate, which may have a wide range of applications including monitoring patients with chronic respiratory diseases, epileptic seizures detection, stress assessment, and sleep studies, among others.

An ECG-based System for Respiratory Rate Estimation Tested on a Wearable Armband During Daily Life

“…A band-pass filter with cut-off frequencies 3 Hz and 25 Hz, for attenuating the electromyogram (EMG) component [1]. An additional ECG channel was computed as the first principal component obtained from a principal component analysis, as in [3]. An example of the preprocessed ECG channels can be observed in Fig.…”

“…An EDR algorithm based on QRS slopes and R-wave angle was used. This algorithm offered accurate estimations of respiratory rate from the armband-ECG signals during restricted-movement conditions in [3]. First, the points of maximum variation between Q and R, and between R and S were detected.…”

“…Estimation of BR was performed by using a timefrequency algorithm that can take one or more EDR signals as inputs [3]. First, a power spectrum density (PSD) is computed from the kth segment of 42 s from the jth EDR signal used as input (S j (k, f )).…”

“…The ECG monitoring allows, among other things, to measure some cardiac vital signs such as heart rate and its variability, and to estimate respi-ratory information such as breathing rate (BR) through the so-called ECG-derived respiration (EDR) techniques [2]. Heart rate and BR monitoring by using a wearable device would have a wide range of applications, including sleep studies, epileptic seizure detection, stress level assessment, and monitoring chronic respiratory patients [3].…”

“…The armband has previously been evaluated for heart rate monitoring during daily life, obtaining accurate heart rate during 75.25% (in median) of the non-bed time, and during 98.49% (in median) of the bed time [1]. Furthermore, the armband has previously been evaluated also for estimating BR during lab-controlled restricted-movement conditions, showing a relative error not higher than 1.40% in median (intersubject median of intra-subject median) and 3.06% in IQR (inter-subject median of intra-subject IQR) [3]. However, the armband has never been evaluated for BR estimation during daily life.…”