Comparison of UHF-ECG with Other Noninvasive Electrophysiological Mapping Tools for Assessing Ventricular Dyssynchrony

This paper compares Ultra High Frequency ECG (UHF-ECG) with other techniques in the capacity to assess ventricular dyssynchrony. Ventricular dyssynchrony is important to identify patients that qualify for Cardiac Resynchronization Therapy (CRT) and to measure effects of CRT and other pacing therapies.Currently used tools are: duration of the QRS complex in the 12-lead ECG, vectorcardiographically determined QRSarea, ECG belt and ECG imaging. QRS duration is crude, QRSarea has been shown to predict CRT response in three large single center studies, ECG belt is a novel approach using 50-60 body surface electrodes and yields (variation in) activation times. ECG imaging requires cardiothoracic imaging and recordings using 150-250 electrodes and results in images of activation, which are converted into inter and intraventricular AT differences. UHF-ECG requires 12-14 lead ECG but provides two measures: (also) a measure of interventricular dyssynchrony (e-DYS) and a marker of width of the activation wavefront that reflects the contribution of rapid conduction. The latter is a unique feature that appears particularly useful in studies on different modes of physiological pacing

Entropy in scalp EEG can be used as a preimplantation marker for VNS efficacy

Abstract Vagus nerve stimulation (VNS) is a therapeutic option in drug-resistant epilepsy. VNS leads to ≥ 50% seizure reduction in 50 to 60% of patients, termed "responders". The remaining 40 to 50% of patients, "non-responders", exhibit seizure reduction < 50%. Our work aims to differentiate between these two patient groups in preimplantation EEG analysis by employing several Entropy methods. We identified 59 drug-resistant epilepsy patients treated with VNS. We established their response to VNS in terms of responders and non-responders. A preimplantation EEG with eyes open/closed, photic stimulation, and hyperventilation was found for each patient. The EEG was segmented into eight time intervals within four standard frequency bands. In all, 32 EEG segments were obtained. Seven Entropy methods were calculated for all segments. Subsequently, VNS responders and non-responders were compared using individual Entropy methods. VNS responders and non-responders differed significantly in all Entropy methods except Approximate Entropy. Spectral Entropy revealed the highest number of EEG segments differentiating between responders and non-responders. The most useful frequency band distinguishing responders and non-responders was the alpha frequency, and the most helpful time interval was hyperventilation and rest 4 (the end of EEG recording)

Reducing False Alarm Rates in Neonatal Intensive Care: A New Machine Learning Approach

In neonatal intensive care units (NICUs), 87.5% of alarms by the monitoring system are false alarms, often caused by the movements of the neonates. Such false alarms are not only stressful for the neonates as well as for their parents and caregivers, but may also lead to longer response times in real critical situations. The aim of this project was to reduce the rates of false alarms by employing machine learning algorithms (MLA), which intelligently analyze data stemming from standard physiological monitoring in combination with cerebral oximetry data (in-house built, OxyPrem). MATERIALS & METHODS Four popular MLAs were selected to categorize the alarms as false or real: (i) decision tree (DT), (ii) 5-nearest neighbors (5-NN), (iii) naïve Bayes (NB) and (iv) support vector machine (SVM). We acquired and processed monitoring data (median duration (SD): 54.6 (± 6.9) min) of 14 preterm infants (gestational age: 26 6/7 (± 2 5/7) weeks). A hybrid method of filter and wrapper feature selection generated the candidate subset for training these four MLAs. RESULTS A high specificity of >99% was achieved by all four approaches. DT showed the highest sensitivity (87%). The cerebral oximetry data improved the classification accuracy. DISCUSSION & CONCLUSION Despite a (as yet) low amount of data for training, the four MLAs achieved an excellent specificity and a promising sensitivity. Presently, the current sensitivity is insufficient since, in the NICU, it is crucial that no real alarms are missed. This will most likely be improved by including more subjects and data in the training of the MLAs, which makes pursuing this approach worthwhile

Bipolar anodal septal pacing with direct LBB capture preserves physiological ventricular activation better than unipolar left bundle branch pacing

BackgroundLeft bundle branch pacing (LBBP) produces delayed, unphysiological activation of the right ventricle. Using ultra-high-frequency electrocardiography (UHF-ECG), we explored how bipolar anodal septal pacing with direct LBB capture (aLBBP) affects the resultant ventricular depolarization pattern.MethodsIn patients with bradycardia, His bundle pacing (HBP), unipolar nonselective LBBP (nsLBBP), aLBBP, and right ventricular septal pacing (RVSP) were performed. Timing of local ventricular activation, in leads V1–V8, was displayed using UHF-ECG, and electrical dyssynchrony (e-DYS) was calculated as the difference between the first and last activation. Durations of local depolarizations were determined as the width of the UHF-QRS complex at 50% of its amplitude.ResultsaLBBP was feasible in 63 of 75 consecutive patients with successful nsLBBP. aLBBP significantly improved ventricular dyssynchrony (mean −9 ms; 95% CI (−12;−6) vs. −24 ms (−27;−21), ), p ConclusionAlthough aLBBP improved ventricular synchrony and depolarization duration of the septum and RV compared to unipolar nsLBBP, the resultant ventricular depolarization was still less physiological than during HBP.</p