Preliminary Results from a Proof of Concept Study for Fall Detection via ECG Morphology

Abstract

Abstract-Falls are a major problem in later life. Early fall detection systems are increased over the years as undetected falls can have severe consequences for the fallers. Fall detection systems are based mainly on posture detection using accelerometers and gyroscopes. Alternatively, this study aims to understand if it is possible to detect posture changes using only electrocardiogram (ECG) morphology, which is significantly associated with moving from one position to another. This paper presents preliminary results of a feasibility study aiming to investigate at what extend it was possible to detect lying and standing position, using wearable devices to observe ECG morphology alterations. According to the literature, 29 ECG features were extracted. 11 healthy subjects (aged 19-36 years) were monitoring while lying down and standing up. ECG and accelerometer signals were recorded continually using a chest wearable monitoring device, the BioHarness M3 (ZephyrTech, NZ).Variations in the ECG features while the subjects lay down and stood up were analysed with the parametric statistical paired T-test. The results of the current study suggested that 4 ECG features were effective in detecting changes while lying down or standing up. Linear Discriminant Analysis (LDA) was used to generate a classifier based on these ECG features to detect automatically the changes while lying down or standing up with total classification accuracy, sensitivity and specificity rates of 77.3%, 81.8%, and 72.7% respectively. The results obtained from the current study support a preliminary proof of concept and pave the way to more complex studies aiming to detecting real falls using ECG variations.

Estimation of ECG Parameters via Deep Learning based ECG Segmentation Tool for Non-invasive Detection of Glycaemic Events

“…After determination of fiducial points, the next step is the determination of ECG parameters [7,10] . We have five fiducial points namely P, Q, R, S and T. We can have a total of 9 intervals among them.…”

“…The impact of glycaemic events can be reflected through ECG signal via changes in ECG parameters [3,5,6] such as beat intervals, power spectrum etc. [3,6,7]. These alterations are identified as prolongation of QT-interval, flattening of T-wave, shortening of P-R interval, ST-segment depression for hypoglycaemic events, reduction of R-R variability, variability in QT interval and increase of P-R interval for hyperglycaemic events [8,9].…”

“…These alterations are identified as prolongation of QT-interval, flattening of T-wave, shortening of P-R interval, ST-segment depression for hypoglycaemic events, reduction of R-R variability, variability in QT interval and increase of P-R interval for hyperglycaemic events [8,9]. The estimation of these parameters rely on accurate segmentation of ECG beats as time domain features require accurate identification of the fiducial points in a ECG beat [7,10] . Consequently, the accurate identification of fiducial points requires accurate segmentation of the ECG waves present in the ECG beat.…”