Heart and respiratory rate detection on a bathroom scale based on the ballistocardiogram and the continuous wavelet transform

Abstract

Ballistocardiography is a non-invasive technique that yields information about the cardiovascular system that is not available in other external signals such as the electrocardiogram (ECG). In the last years, several research groups have obtained the ballistocardiogram (BCG) by using instrumentation methods simpler than those available in the 1950s and that did not progress because of their complexity as compared to ultrasound and other noninvasive techniques that are in common use nowadays. We describe a novel method for real-time robust heart- (HR) and respiratory- (RR) rate detection from a subject that stands on a common electronic bathroom scale. BCG signals from the scale are wirelessly sent to a PC where algorithms based on the continuous wavelet transform (CWT) extract the HR and the RR. HR results are compared to those obtained from the ECG. To better assess the RR results, subjects have been asked to synchronize their breathing rate to an on-screen bar-graph set at a constant rate of breaths per minute. This method to obtain the heart and respiratory rates is simple, compact, non-invasive and passive, and can be applied to any person able to stand on an electronic weighing scale, even if wearing shoes.

Continuous and unconstrained vital signs monitoring with ballistocardiogram sensors in headrest position

“…The BCG data is collected from five healthy subjects in a sitting position using an electromechanical film sensor. In [7] continuous wavelet transform (CWT) is introduced for HR and respiratory rate measurements, where in each case, the scale which matches the periodicity of the signal is selected. The proposed approach is applied to six healthy subjects standing on a bathroom scale equipped with multiple strain gages.…”

An algorithm for beat-to-beat heart rate detection from the BCG based on the continuous spline wavelet transform

“…Several algorithms have been proposed to detect individual heartbeats from the BCG, some based on the search of largest deflection [15] which was very sensitive to motion artifacts. Some others are based on the discrete wavelet transform (DWT) [7], for which is required additional signal processing for noise filtering; and the continuous wavelet transform (CWT) [16], that required two ranges of scales to obtain low and high HR. Other algorithms that involve a high computational load due to its complexity are based on unsupervised learning techniques [4], continuous local interval estimation with three estimators combined [17] and adaptive heartbeat shape modeling [18].…”

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

“…The main devices requiring mechanical contact are piezoelectric sensors, load cells, sternal accelerometers and electromechanical film sensors (EMFi). The robustness of BCG monitoring systems based on EMFi sensors has been evidenced in its large number of implementations [31,[34][35][36]. However, the adoption of this solution in medical facilities is still limited in present days.…”