Torfinn Berset

Motion artifact reduction in ambulatory ECG monitoring: an integrated system approach

Recent advances in low-power micro-electronics are revolutionizing ECG monitoring. Wearable patches now allow comfortable monitoring over several days. Achieving reliable and high integrity recording however remains a challenge, especially under daily-life activities. In this paper we present a system approach to motion artifact reduction in ambulatory recordings. A custom ultra-low-power ECG analog front-end read-out for simultaneous measurement of ECG and electrode-tissue impedance, from the same electrode, is reported. Integrating this front-end, we describe a wireless patch for the monitoring of 3-lead ECG, electrode electrical artifact and 3D-acceleration. Beyond ECG monitoring, this wireless patch provides the additional necessary data to filter out motion artifact. Two algorithm methods are tested. The first method applies ICA for de-noising multi-lead ECG recordings. The second method is an adaptive filter that uses skin/electrode impedance as the measurement of noise. Algorithms, circuits and system provide a platform for reliable ECG monitoring on-the-move.

Clinical validation of a low-power and wearable ECG patch for long term full-disclosure monitoring

BACKGROUND Detection of intermittent atrial fibrillation (AF) is done using a 24-h Holter. Holter recordings are powerful but lack the comfort and have limited recording times resulting in under diagnosing of intermittent AF. OBJECTIVE Within this work we evaluated and compared a novel miniaturized three-channel ECG monitoring patch versus a 24-h Holter system. METHODS Both patients with a chronic AF rhythm (n=5) as well as patients with an AF rhythm that underwent electrical reconversion (n = 5) were equipped with both a 24-h Holter and ECG patch. RESULTS Alignment of raw data of both ECG systems allowed cross-correlation analysis. Overall good correlations of up to 85% were obtained. RR-interval analysis of both systems resulted in very high correlations of 99% and higher. AF analysis showed correct identification of AF on both ECG systems. CONCLUSIONS The performance of our ECG patch matches that of the 24-h Holter and could provide a suitable tool for long-term monitoring applications.

An optimized DSP implementation of adaptive filtering and ICA for motion artifact reduction in ambulatory ECG monitoring

Noise from motion artifacts is currently one of the main challenges in the field of ambulatory ECG recording. To address this problem, we propose the use of two different approaches. First, an adaptive filter with electrode-skin impedance as a reference signal is described. Secondly, a multi-channel ECG algorithm based on Independent Component Analysis is introduced. Both algorithms have been designed and further optimized for real-time work embedded in a dedicated Digital Signal Processor. We show that both algorithms improve the performance of a beat detection algorithm when applied in high noise conditions. In addition, an efficient way of choosing this methods is suggested with the aim of reduce the overall total system power consumption.

Robust heart rhythm calculation and respiration rate estimation in ambulatory ECG monitoring

Heart rhythm and respiration rate are two vital signs that are of interest for ambulatory monitoring. However, noise due to activity in ambulatory monitoring complicates the ECG interpretation. This paper describes a set of algorithms to robustly monitor a subjects heart rhythm and respiration rate in an ambulatory environment. To ensure robustness against motion artifacts, we have developed an algorithm for motion artifact reduction based on independent component analysis with a multi-lead ECG system. From the robust heart-rate calculations, we use an ECG derived respiration (EDR) algorithm based on heart-rate variability (HRV) to estimate the respiration rate.