Dispersión espacial de los tiempos de activación y repolarización asociada a diferentes modos de estimulación cardiaca

En pacientes con indicación de marcapasos permanente se aplican distintos tipos de estimulación ventricular. Los denominados fisiológicos estimulan el sistema de conducción cardiaca induciendo una activación fisiológica eficiente. Entre estos se encuentran la estimulación selectiva del haz de His (HBP selectiva, sHBP, y HBP no selectiva, nsHBP, por sus siglas en inglés) y las estimulaciones selectiva y no selectiva de la rama izquierda (sLBBP y nsLBBP) Otras regiones cardiacas que también suelen estimularse mediante el marcapasos son el septo del ventrículo izquierdo (LVSP) o del ventrículo derecho (RVSP) y el ápex del ventrículo derecho (RVAP). En este trabajo se analizaron 695 electrocardiogramas de muy alta frecuencia (UHF-ECG) obtenidos de 176 pacientes con complejo QRS estrecho y con indicación de marcapasos. Se caracterizaron los tiempos de activación (TA) y de repolarización (TR) y se agruparon en tres regiones según las derivaciones en las que se evaluaron (R1: derivaciones V1-V2; R2: V3-V4; R3: V5-V6). Globalmente en la población, las estimulaciones sHBP, nsLBBP y LVSP proporcionaron los valores de AT y RT más similares a los obtenidos durante ritmo espontáneo. Los valores absolutos de las medias para las diferencias R1-R2 y R3-R2 en TA resultaron menores a 3, 16 y 10 ms para sHBP, nsLBBP y LVSP, respectivamente, con respecto al ritmo espontáneo. Para TR estas diferencias fueron menores a 11, 34 y 24 ms para sHBP y nsLBBP y LVSP. En conclusión, las estimulaciones HBP, LBBP y LVSP inducen los tiempos de activación y repolarización ventricular más similares a los hallados en ritmo espontáneo en pacientes con conducción fisiológica (QRS estrecho).Este trabajo ha sido realizado con el apoyo de los proyectos PID2019-105674RB-I00, PID2019-104881RB-I00, TED2021-130459B-I00 y la ayuda BES-2017-080587 (Ministerio de Ciencia e Innovación), el proyecto LMP94_21 y el grupo de referencia BSICoS T39-23R (Gobierno de Aragón cofinanciado por el FEDER 2014-2020 “Construyendo Europa desde Aragón”) y el proyecto ERC G.A. 638284 (European Research Council). Los cálculos computacionales se han realizado en la ICTS NANBIOSIS (HPC Unit at University of Zaragoza)

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