Accurate Heartbeat Detection on Ballistocardiogram Accelerometric Traces

Abstract

This paper presents an automated procedure for acquisition and analysis of BallistoCardioGraphy (BCG) traces. A tri-axial accelerometer and a microcontroller unit are used to record heart-induced recoil forces generated from a lying subject. The problem of BCG J-peak annotation is split into two sub-tasks: candidates extraction, based on a detection signal, and actual annotation, guided by subject-specific search windows. Such procedure is derived from an automatic calibration, which is carried out with no need of concurrent ElectroCardioGram (ECG) or user intervention. The algorithm also implements post-annotation checks for refinement of annotation, which effectively reduces the number of missed J-peaks. The impact of each algorithm phase is analyzed, assessing statistical significance of each step; finally, performance is optimized in a data-driven fashion. Results show that the proposed methodology is able to achieve high sensitivity and precision (the median score is 98.9% and 98.1%, respectively) in J-peak detection. The quality of J-peaks timing annotation is further demonstrated by a very low discrepancy between BCG and ECG HR estimates. Over all population, the standard deviation of such error was found to be approximately 6.56 ms, whereas the Mean Absolute Error just 4.7 ms (i.e. ≈ 1.18; Ts, where Ts = 4 ms is the sampling period). Such scores, indeed, improve over recent related literature.

J peaks detection for ballistocardiography based on autocorrelation and dual-threshold

“…Table 2 lists the accuracy of the heart rate detection results and the J peaks annotation results of the algorithm in this paper compared with the literature Mora's [11] algorithm. Compared with Mora [11] , our method performs well on each individual. This to some extent demonstrates the superiority of the algorithm.…”

Evaluation of a contactless neonatal physiological monitor in Nairobi, Kenya

“…In comparison with ECG, heartbeats are less pronounced and more difficult to detect in BCG signals due to a lower signal-to-noise ratio 14 ; yet, BCG signals have been shown to accurately estimate HR in adults. 15 We selected capnography as the RR reference method. 16 Agreement between RR measurements was impacted by withinneonate RR variability, which is particularly pronounced in neonates due to immature control of respiration, and normal and abnormal physiological responses such as periodic breathing and periods of apnoea.…”

IoT Smart Flooring Supporting Active and Healthy Lifestyles

“…IoT is widely exploited for health-oriented applications [ 2 , 3 , 4 , 5 ], including highly specific solutions, supporting the needs of people with disabilities [ 6 , 7 ], or techniques for non-intrusive, continuous monitoring of relevant physiological parameters (e.g., blood pressure [ 8 ], heart rate [ 9 ]). Wearable sensors are widely used, with the non-wearable approach being sought for further reducing the burden of the user [ 10 , 11 ]. The adoption of Artificial Intelligence techniques allows for indirect health assessment through activity recognition and behavioural analysis [ 12 , 13 , 14 , 15 , 16 ].…”