Biology subjects

Hopenfeld, B. R.

Publications and source records attributed to Hopenfeld, B. R..

3 recordsLinked to original sources

bioRxiv2022

Fetal QRS Detection by Multiple Channel Temporal Pattern Search Applied to the NInFEA Database

BackgroundIn some cases, the fetal electrocardiogram (ECG) has a relatively low signal to noise ratio. Much of the literature pertaining to fetal ECG processing focuses on methods for separating the fetal ECG from the maternal ECG rather than detecting the fetal QRS in high noise conditions. This paper describes the application of a previously described pattern search methodology (TEPS) for detecting the fetal QRS in noisy ECGs. Algorithm SummarySignals are processed in non-overlapping 5 s segments. The maternal QRS peaks are detected in a segment from a reliable lead. Next, for each of a potentially large number of leads, after removing the maternal QRS peaks from the set of searchable peaks, single channel TEPS is applied to search for provisional fetal peak sequences. The provisional sequences are scored according to previously described single channel TEPS quality measures: temporal regularity, peak pair amplitude ratios, and number of skips. The provisional sequences across all leads are aggregated according to their average RR intervals and scores. The most likely average RR interval for the segment is estimated from this aggregation by forming a score weighted average of the RR intervals. Sequences with RR intervals close to the chosen RR interval are selected to form a set of high quality sequences. To account for the possibility of peak time offsets between different channels, these sequences are time aligned by time lagged cross correlation. An optimal sequence is formed from these time aligned sequences, with optimality criteria based on peak timing coherence, temporal regularity, and skips. DataThe above procedure was applied to 60 ECG signals (avg. duration approximately 31 s) from the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research ("NInFEA"). The NInFEA dataset also includes ultrasound recordings from which RR intervals may be extracted. ResultsOut of 59 records, RR intervals were well tracked in 57 records. (One of the 60 NInFEA records was excluded due to the inability to obtain a sinus rhythm peak sequence from the ultrasound recording). For these 57 records, 84% and 95% of the ECG RR intervals were within 5 ms and 10 ms, respectively, of the ultrasound derived RR intervals. The mean and median average absolute value RR interval difference (between ECG and ultrasound) over 5 second segments were 3.2 ms and 2.4 ms respectively, with 93% of segments having a mean average absolute value RR interval difference less than 7ms. ConclusionThe TEPS methodology shows promise for ECG based fetal sinus rhythm monitoring.

bioengineering↗

bioRxiv2022

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

BackgroundObtaining 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 is applied to real world waist based ECGs. Algorithm SummaryThe methodology includes successive applications of a previously described pattern-based heart beat detection scheme (Temporal Pattern Search, or "TEPS"). 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. ExperimentThe 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. ResultsBoth walking and jogging heart rates were generally well tracked. In the four recordings, the percentage of 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 ({Delta}RMSSD). The mean {Delta}RMSSD (over acceptable segments) was 4.6 m, 5.2 ms, 5.2 ms and 6.6 ms for the four recordings. 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.

bioengineering↗

bioRxiv2021

Multiple Channel Electrocardiogram QRS Detection by Temporal Pattern Search

A highly constrained temporal pattern search ("TEPS") based multiple channel heartbeat detector is described. TEPS generates sequences of peaks and statistically scores them according to: 1) peak time coherence across channels; 2) peak prominence; 3) temporal regularity; and 4) number of skipped beats. TEPS was tested on 31 records of three channel capacitive electrode data from the UnoViS automobile database. TEPS showed a sensitivity (SE) of 91.3% and a false discovery rate (FDR) of 3.0% compared to an SE and FDR of 75.3% and 65.0% respectively for a conventional single channel detector (OSEA) applied separately to the three channels. The peak matching window was 30ms. The percentage of 5 second segments with average heart rates within 5 beats/minute of reference was also measured. In 6 of the 31 records, TEPS percentage was at least 30% greater than OSEAs. TEPS was also applied to synthetic data comprising a known signal corrupted with calibrated amounts of noise. At a fixed SE of 85%, increasing the number of channels from one to two resulted in an improvement of approximately 5dB in noise resistance, while increasing the number of channels from two to four resulted in an improvement of approximately 3dB in noise resistance. The quantification of noise resistance as a function of the number of channels could help guide the development of wearable electrocardiogram monitors.

bioengineering↗