Browsing by Subject "ECG"

Now showing 1 - 5 of 5
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    Brief Report: Reduced Heart Rate Variability in Children with Autism Spectrum Disorder
    (SpringerLink, 2020-11) Lory, Catharine; Kadlaskar, Girija; McNally Keehn, Rebecca; Francis, Alexander L.; Keehn, Brandon; Pediatrics, School of Medicine
    Dysregulation of the autonomic nervous system (ANS), which can be indexed by heart rate variability (HRV), has been posited to contribute to core features of autism spectrum disorder (ASD). However, the relationship between ASD and HRV remains uncertain. We assessed tonic and phasic HRV of 21 children with ASD and 21 age- and IQ-matched typically developing (TD) children and examined (1) group differences in HRV and (2) associations between HRV and ASD symptomatology. Children with ASD showed significantly lower tonic HRV, but similar phasic HRV compared to TD children. Additionally, reduced tonic HRV was associated with atypical attentional responsivity in ASD. Our findings suggest ANS dysregulation is present in ASD and may contribute to atypical attentional responses to sensory stimulation.
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    ECG conduction delays: A case of viral myocarditis
    (Elsevier, 2019-04) Dobben, Elizabeth; Lattimore, Sherene; Welch, Julie L.; Emergency Medicine, School of Medicine
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    The Effects of Progestin-Only Hormone Treatment on QT Interval in the Adolescent Female
    (Elsevier, 2021-01) Kean, Adam C.; Ayers, Mark D.; Farrell, Anne G.; Kean, Kelly A.; Brooks, Patricia W.; Shew, Marcia L.; Pediatrics, School of Medicine
    We describe the effect of exogenous progestin on the corrected QT interval (QTc) in adolescent females. In post-menarcheal females, <18 years old, we compared QTc in milliseconds (ms) on ECG evaluations in those taking exogenous progestin vs those who are not. There were 40 controls and 21 treated participants. The age range was 10–17 years. There were no differences between the groups with regard to race, height, weight, BMI, or blood pressure. In the controls, the mean QTc was 403 ±19 milliseconds (ms) vs. 397 ±15 ms in those treated (p = 0.22). Those on progestin therapy had a shorter QTc by the same magnitude difference (six ms) as the hormonal naïve group in the adult literature. We report no adverse effects of progestin associated with QTc prolongation and a trend suggesting a decreased QTc in a population of post-menarcheal adolescent females.
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    Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction
    (MDPI, 2023-06-19) Zhang, Qingxue; Zhou, Dian; Electrical and Computer Engineering, School of Engineering and Technology
    Background: Internet-of-things technologies are reshaping healthcare applications. We take a special interest in long-term, out-of-clinic, electrocardiogram (ECG)-based heart health management and propose a machine learning framework to extract crucial patterns from noisy mobile ECG signals. Methods: A three-stage hybrid machine learning framework is proposed for estimating heart-disease-related ECG QRS duration. First, raw heartbeats are recognized from the mobile ECG using a support vector machine (SVM). Then, the QRS boundaries are located using a novel pattern recognition approach, multiview dynamic time warping (MV-DTW). To enhance robustness with motion artifacts in the signal, the MV-DTW path distance is also used to quantize heartbeat-specific distortion conditions. Finally, a regression model is trained to transform the mobile ECG QRS duration into the commonly used standard chest ECG QRS durations. Results: With the proposed framework, the performance of ECG QRS duration estimation is very encouraging, and the correlation coefficient, mean error/standard deviation, mean absolute error, and root mean absolute error are 91.2%, 0.4 ± 2.6, 1.7, and 2.6 ms, respectively, compared with the traditional chest ECG-based measurements. Conclusions: Promising experimental results are demonstrated to indicate the effectiveness of the framework. This study will greatly advance machine-learning-enabled ECG data mining towards smart medical decision support.
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    Prognostic value of initial electrocardiography in predicting long-term all-cause mortality in COVID-19
    (Elsevier, 2022) Kassis, Nicholas; Kumar, Ashish; Gangidi, Shravani; Milinovich, Alex; Kalra, Ankur; Bhargava, Ajay; Menon, Venu; Wazni, Oussama M.; Rickard, John; Khot, Umesh N.; Medicine, School of Medicine
    Background: The electrocardiography (ECG) has short-term prognostic value in coronavirus disease 2019 (COVID-19), yet its ability to predict long-term mortality is unknown. This study aimed to elucidate the predictive role of initial ECG on long-term all-cause mortality in patients diagnosed with COVID-19. Methods: In this prospective cohort study, adults with COVID-19 who underwent ECG testing within a 17-hospital health system in Northeast Ohio and Florida between 03/2020-06/2020 were identified. An expert ECG reader analyzed all studies blinded to patient status. The associations of ECG characteristics with long-term all-cause mortality and intensive care unit (ICU) admission were assessed using Cox proportional hazards regression model and multivariable logistic regression models, respectively. Status of long-term mortality was adjudicated on 01/07/2022. Results: Of 837 patients (median age 65 years, 51% female, 44% Black), 683 (81.6%) were hospitalized, 281 (33.6%) required ICU admission, 67 (8.0%) died in-hospital, and 206 (24.6%) died at final follow-up after a median (IQR) of 21 (9-103) days after ECG. Overall, 179 (20.7%) patients presented with sinus tachycardia, 12 (1.4%) with atrial flutter, and 45 (5.4%) with atrial fibrillation (AF). After multivariable adjustment, sinus tachycardia (E-value for HR=3.09, lower CI=2.2) and AF (E-value for HR=3.13, lower CI=2.03) each independently predicted all-cause mortality. At final follow-up, patients with AF had 64.5% probability of death compared with 20.5% for those with normal sinus rhythm (P<.0001). Conclusions: Sinus tachycardia and AF on initial ECG strongly predict long-term all-cause mortality in COVID-19. The ECG can serve as a powerful long-term prognostic tool in COVID-19.