Online ISSN: 2515-8260

Keywords : Electrocardiogram


A study to compare and evaluate variation in electrocardiogram, heart rate variability and hypertension during different phases of menstrual cycle to determine the effect of ovarian hormones on cardiovascular function

Parul Singh

European Journal of Molecular & Clinical Medicine, 2022, Volume 9, Issue 3, Pages 10072-10077

Purpose: During menstrual cycle the fluctuating level of endogenous sex hormones have
an impact on cardiac autonomic function and may also affect blood volume along with
electrocardiographic pattern. The main purpose of the study was to compare and
evaluate variation in electrocardiogram, heart rate variability and hypertension during
different phases of menstrual cycle to determine the effect of ovarian hormones on
cardiovascular function.
Methods: This was a cross sectional prospective study conducted in 145 healthy female
student who had regular menstrual cycle of 30 ± 3 days and aged between 18 to 24 years
after taking informed consent and institutional ethical clearance. In different phases of
menstrual cycle [Menstrual Phase (day 1-5), Follicular Phase (day 5–14) and Luteal
phase(day 15-28)] blood pressure, lead II electrocardiogram recordings were captured
and by using PHYSIOPAC after connecting the limb leads of ECG in supine resting
position with eyes closed HRV was assessed for 15min.
Results: On electro gram parameters a significant QT interval and RR interval were
observed. Longer QT interval during follicular Phase and shorter QT interval during
Luteal phase were recoded where as shorter RR interval observed during Menstrual
Phase which were longer during luteal phase. During the three phases no significant
variation in both systolic and diastolic blood pressure were noticed. An increase resting
heart rate were noted during menstrual phase which was lower during luteal phase. A
non-significant increase in LF nudomain and LF/HF ratio were noted during the luteal
phase as compared to other phases. Compared to luteal phase, during the follicular
Phase, in LF nudomain and LF/HF ratio a non-significant increase were observed.
Conclusion: In healthy young women with regular menstrual cycle sympathovagal
balance were greatly influenced by endogenous sex hormones. Cardiacautonomic
dysfunction and be resulted due to any type of hormonal imbalance which effect
sympathovagal balance. The study also concluded even within range of fluctuations,
ventricular action potentials were were greatly influenced by estrogen as QT and Q Tc
intervals shows changed in healthy young adults.

AUTOMATED CLASSIFICATION OF ECG SIGNAL USING CONVOLUTIONAL GATED RECURRENT NEURAL NETWORK FOR CARDIAC DISEASE DETECTION

M. Mohamed Suhail; Dr.T. Abdul Razak

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 2722-2739

Early detection of unusual heart conditions is huge to recognize heart disappointment and maintain a strategic distance from unexpected death. The human with similar heart conditions nearly has practically identical using electrocardiogram (ECG) signals. By reviewing the ECG signals' models, one can anticipate heart disease.Since the standard techniques for heart disease disclosure depend after securing morphological features of the ECG signals, which are repetitious and tedious, the customized recognizable proof of cardiovascular disease is progressively perfect. Subsequently, the programmed identification of heart diseases a satisfactory strategy is required, which could arrange the ECG signals with dark features as appeared by the similitudes among them and the ECG signals with known characteristics. If this classifier can discover the similitudes, the likelihood of cardiovascular disease disclosure is broadened. This count can change into a significant procedure in research facilities. During this examination work, and another classification technique is brought into the Convolutional Gated Recurrent Neural Network classification methodology. All the more precisely, orders ECG signals that rely upon a powerful model of the ECG signal classification. With this proposed method, a convolutional gated recurrent neural network was constructed, and its simulation results show that this classification can partition the ECG with 97% accuracy.