Peak Space Motion Artifact Cancellation Applied to Textile Electrode Waist Electrocardiograms Recording During Outdoors Walking and Jogging

Background: Obtaining reliable rate heart estimates from waist based electrocardiograms (ECGs) poses a very challenging problem due to the presence of extreme motion artifacts. The literature reveals few, if any, attempts to apply motion artifact cancellation methods to waist based ECGs. This paper describes a new methodology for ameliorating the effects of motion artifacts in ECGs by specifically targeting ECG peaks for elimination that are determined to be correlated with accelerometer peaks. This peak space cancellation was applied to real world waist based ECGs. Algorithm Summary: The methodology includes successive applications of a previously described pattern-based heart beat detection scheme (Temporal Pattern Search, or TEPS) that can also detect patterns in other types of peak sequences. In the first application, TEPS is applied to accelerometer signals recorded contemporaneously with ECG signals to identify high-quality accelerometer peak sequences (SA) indicative of quasi-periodic motion likely to impair identification of peaks in a corresponding ECG signal. The process then performs ECG peak detection and locates the closest in time ECG peak to each peak in an SA. The differences in time between ECG and SA peaks are clustered. If the number of elements in a cluster of peaks in an SA exceeds a threshold, the ECG peaks in that cluster are removed from further processing. After this peak removal process, further QRS detection proceeds according to TEPS. Experiment: The above procedure was applied to data from real world experiments involving four sessions of walking and jogging on a dirt road for approximately 20-25 minutes. A compression shirt with textile electrodes served as the ground truth recording. A textile electrode based chest strap was worn around the waist to generate a single channel signal upon which to test peak space cancellation/TEPS. Results: Both walking and jogging heart rates were generally well tracked. In the four recordings, the percentage of 5 second segments within 10 beats/minute of reference was 96%, 99%, 92% and 96%. The percentage of segments within 5 beats/minute of reference was 86%, 90%, 82% and 78%. There was very good agreement between the RR intervals associated with the reference and waist recordings. For acceptable quality segments, the root mean square sum of successive RR interval differences (RMSSD) was calculated for both the reference and waist recordings. Next, the difference between waist and reference RMSSDs was calculated (∆RMSSD). The mean ∆RMSSD (over acceptable segments) was 4.6 m, 5.2 ms, 5.2 ms and 6.6 ms for the four recordings. Conclusion: Given that only one waist ECG channel was available, and that the strap used for the waist recording was not tailored for that purpose, the proposed methodology shows promise for waist based sinus rhythm QRS detection.

Verification and comparison of MIT-BIH arrhythmia database based on number of beats

<span lang="EN-US">The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database.</span>

Signal Quality Assessment of a Novel ECG Electrode for Motion Artifact Reduction

Background: The presence of noise is problematic in the analysis and interpretation of the ECG, especially in ambulatory monitoring. Restricting the analysis to high-quality signal segments only comes with the risk of excluding significant arrhythmia episodes. Therefore, the development of novel electrode technology, robust to noise, continues to be warranted. Methods: The signal quality of a novel wet ECG electrode (Piotrode) is assessed and compared to a commercially available, commonly used electrode (Ambu). The assessment involves indices of QRS detection and atrial fibrillation detection performance, as well as signal quality indices (ensemble standard deviation and time–frequency repeatability), computed from ECGs recorded simultaneously from 20 healthy subjects performing everyday activities. Results: The QRS detection performance using the Piotrode was considerably better than when using the Ambu, especially for running but also for lighter activities. The two signal quality indices demonstrated similar trends: the gap in quality became increasingly larger as the subjects became increasingly more active. Conclusions: The novel wet ECG electrode produces signals with less motion artifacts, thereby offering the potential to reduce the review burden, and accordingly the cost, associated with ambulatory monitoring.

QRS Detection Based on Medical Knowledge and Cascades of Moving Average Filters

Heartbeat detection is the first step in automatic analysis of the electrocardiogram (ECG). For mobile and wearable devices, the detection process should be both accurate and computationally efficient. In this paper, we present a QRS detection algorithm based on moving average filters, which affords a simple yet robust signal processing technique. The decision logic considers the rhythmic and morphological features of the QRS complex. QRS enhancing is performed with channel-specific moving average cascades selected from a pool of derivative systems we designed. We measured the effectiveness of our algorithm on well-known benchmark databases, reporting F1 scores, sensitivity on abnormal beats and processing time. We also evaluated the performances of other available detectors for a direct comparison with the same criteria. The algorithm we propose achieved satisfying performances on par with or higher than the other QRS detectors. Despite the performances we report are not the highest that have been published so far, our approach to QRS detection enhances computational efficiency while maintaining high accuracy.