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.