scholarly journals Movement-induced heart rate changes in epileptic and non-epileptic seizures

2009 ◽  
Vol 67 (3b) ◽  
pp. 789-791 ◽  
Author(s):  
Gisele R. de Oliveira ◽  
Francisco de A.A. Gondim ◽  
Edward R. Hogan ◽  
Francisco H. Rola
Keyword(s):  
Heart Rate ◽  
Motor Activity ◽  
Rate Increase ◽  
Epileptic Seizures ◽  
Baseline Heart Rate ◽  
Heart Rate Increase ◽  
Event Duration ◽  
Rate Analysis ◽  
Heart Rate Analysis ◽  
Video Eeg

Heart rate changes are common in epileptic and non-epileptic seizures. Previous studies have not adequately assessed the contribution of motor activity on these changes nor have evaluated them during prolonged monitoring. We retrospectively evaluated 143 seizures and auras from 76 patients admitted for video EEG monitoring. The events were classified according to the degree of ictal motor activity (severe, moderate and mild/absent) in: severe epileptic (SE, N=17), severe non-epileptic (SNE, N=6), moderate epileptic (ME, N=28), moderate non-epileptic (MNE, N=11), mild epileptic (mE, N=35), mild non-epileptic (mNE, N=33) and mild aura (aura, N=13). Heart rate increased in the ictal period in severe epileptic, severe non-epileptic, moderate epileptic and mild epileptic events (p<0.05). Heart rate returned to baseline levels during the post ictal phase in severe non-epileptic seizures but not in severe epileptic patients. Aura events had a higher baseline heart rate. A cut-off of 20% heart rate increase may distinguish moderate epileptic and mild epileptic events lasting more than 30 seconds. In epileptic seizures with mild/absent motor activity, the magnitude of heart rate increase is proportional to the event duration. Heart rate analysis in seizures with different degrees of movement during the ictal phase can help to distinguish epileptic from non-epileptic events.

scholarly journals Heart rate analysis differentiates dialeptic complex partial temporal lobe seizures from auras and non-epileptic seizures

2007 ◽  
Vol 65 (3a) ◽  
pp. 565-568 ◽  
Author(s):  
Gisele R. de Oliveira ◽  
Francisco de A.A. Gondim ◽  
R. Edward Hogan ◽  
Francisco H. Rola
Keyword(s):  
Heart Rate ◽  
Temporal Lobe ◽  
Epileptic Seizures ◽  
Eeg Monitoring ◽  
Rate Analysis ◽  
Temporal Lobe Seizures ◽  
Heart Rate Analysis ◽  
Video Eeg

The distinction of non-epileptic from epileptic events is difficult even for experienced neurologists. We retrospectively evaluated 59 dialeptic events from 27 patients admitted for video EEG monitoring to check whether heart rate (HR) analysis could help in differentiating dialeptic complex partial temporal lobe seizures (TLS) from dialeptic simple partial TLS, and non-epileptic dialeptic events. Baseline HR was increased in the simple partial TLS in comparison to complex partial TLS and non-epileptic groups (p<0.05). HR increase accompanied each individual dialeptic complex partial TLS (100% of the events, p<0.05) bur HR returned to baseline in the post-ictal phase. Ictal HR was not altered in the non-epileptic or simple partial TLS groups. Our findings suggest that ictal centrally mediated tachycardia is characteristic of dialeptic TLS (both tachycardia and bradycardia have been reported during TLS). This finding may be used as a criterion to distinguish dialeptic complex partial TLS from simple partial and non-epileptic dialeptic events.

scholarly journals Fetal Heart Rate Analysis for Automatic Detection of Perinatal Hypoxia Using Normalized Compression Distance and Machine Learning

2017 ◽  
Vol 8 ◽  
Author(s):  
Óscar Barquero-Pérez ◽  
Ricardo Santiago-Mozos ◽  
José M. Lillo-Castellano ◽  
Beatriz García-Viruete ◽  
Rebeca Goya-Esteban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Automatic Detection ◽  
Perinatal Hypoxia ◽  
Rate Analysis ◽  
Normalized Compression Distance ◽  
Heart Rate Analysis

scholarly journals Effect of muscle mass and intensity of isometric contraction on heart rate

2000 ◽  
Vol 88 (2) ◽  
pp. 487-492 ◽  
Author(s):  
José M. Gálvez ◽  
Juan P. Alonso ◽  
Luis A. Sangrador ◽  
Gonzalo Navarro
Keyword(s):  
Heart Rate ◽  
Muscle Mass ◽  
Rate Increase ◽  
Isometric Exercise ◽  
Heart Rate Increase ◽  
Maximal Force ◽  
Wheel Turn ◽  
Obese Subjects ◽  
Energetic Expenditure ◽  
Handgrip Contractions

The purpose of this study was to determine the effect of muscle mass and the level of force on the contraction-induced rise in heart rate. We conducted an experimental study in a sample of 28 healthy men between 20 and 30 yr of age (power: 95%, α: 5%). Smokers, obese subjects, and those who performed regular physical activity over a certain amount of energetic expenditure were excluded from the study. The participants exerted two types of isometric contractions: handgrip and turning a 40-cm-diameter wheel. Both were sustained to exhaustion at 20 and 50% of maximal force. Twenty-five subjects finished the experiment. Heart rate increased a mean of 15.1 beats/min [95% confidence interval (CI): 5.5–24.6] from 20 to 50% handgrip contractions, and 20.7 beats/min (95% CI: 11.9–29.5) from 20 to 50% wheel-turn contractions. Heart rate also increased a mean of 13.3 beats/min (95% CI: 10.4–16.1) from handgrip to wheel-turn contractions at 20% maximal force, and 18.9 beats/min (95% CI: 9.8–28.0) from handgrip to wheel-turn contractions at 50% maximal force. We conclude that the magnitude of the heart rate increase during isometric exercise is related to the intensity of the contraction and the mass of the contracted muscle.

scholarly journals Monitoring athletic training status using the maximal rate of heart rate increase

2016 ◽  
Vol 19 (7) ◽  
pp. 590-595 ◽  
Author(s):  
Clint R. Bellenger ◽  
Rebecca L. Thomson ◽  
Peter R.C. Howe ◽  
Laura Karavirta ◽  
Jonathan D. Buckley
Keyword(s):  
Heart Rate ◽  
Athletic Training ◽  
Rate Increase ◽  
Training Status ◽  
Maximal Rate ◽  
Heart Rate Increase

scholarly journals Analysis of Heart Rate Dynamics Before and During Meditation

2021 ◽  
Vol 17 (05) ◽  
pp. 100
Author(s):  
Mohammad Karimi Moridani ◽  
Tina Habikazemi ◽  
Nahid Khoramabadi
Keyword(s):  
Heart Rate ◽  
Vital Signs ◽  
Normal Subjects ◽  
Support Vector ◽  
Nonlinear Characteristics ◽  
Rate Analysis ◽  
High Tension ◽  
Heart Rate Dynamics ◽  
Heart Rate Analysis ◽  
Sensitivity Specificity

<p>Heart rate is one of the most important vital signs. People usually face high tension in routine life, and if we found an effective method to control the heart rate, it would be very desirable. One of the goals of this paper is to examine changes in heart rate before and during meditation. Another goal is that what impact could have meditation on the human heartbeat.</p><p>To heart rate analysis before and during meditation, available heart rate signals have been used for the Physionet database that contains 10 normal subjects and 8 subjects that meditation practice has been done on them. In this paper, first is paid to extract linear and nonlinear characteristics of heart rate and then is paid to the best combination of features to identify two intervals before and during meditation using MLP and SVM classifiers with the help of sensitivity, specificity and accuracy measurements.</p><p>The achieved results in this paper showed that choosing the best combination of a feature to make a meaningful difference between two intervals before and during meditation includes two-time features (Mean HR, SDNN), a frequency feature ( ), and three nonlinear characteristics   ( ). Also, using the support vector machine had better results than the MLP neural network. The sensitivity, specificity, and accuracy of the mean and standard deviation obtained respectively like 92.73  0.23, 89.05 0.67, 89.97 0.23 by using MLP and respectively like 95.96 0.09, 93.80 0.16, and 94.90 0.14 by using SVM.</p>As a result, using meditation can reduce the stress and anxiety of patients by effects on heart rate, and the treatment process speeds up and have an important role in improving the performance of the system.

Sign in / Sign up

Export Citation Format

Share Document