Salma Aryanejad , Fatemeh Taheri Bojd , Atiye Riasi, Tayyebeh Chahkandi, Forod Salehi,
Volume 80, Issue 5 (8-2022)
Abstract
Background: Obesity and overweight are one of the components of metabolic syndrome and the cause of cardiovascular disease and sudden cardiac death. Obesity is associated with a wide range of electrocardiogram (ECG) abnormalities.
Methods: This case-control study was performed on 50 children and adolescents aged 9 to 18 years in Birjand from May to October 2020. In the control group, 25 people with normal weight and in the case group, 25 people with obesity or overweight were included in the study. Individuals with a body mass index of 85-95 percent were defined as overweight, ones with a body mass index above the 95th percentile were defined as obese, and individuals with a body mass index below the 85th percentile were defined as normal. After clinical examination, height, weight and electrocardiogram indices were measured and compared by using statistical tests by SPSS (Version 19) software.
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Results: There were 15 boys in the control group and 17 boys in the case group. The mean age of the control and case groups was 11.28±2.13 and 10.96±1.97 years, respectively. The mean distance between the peak to the end of the T wave in the case group was 323.72±120.15 and in the control group was 79.20±13.06. The mean difference between the shortest and longest distance of TP-e in case group was 48±23.04 and in control group was 18.44±5.58, respectively. There was a statistically significant difference between the two indices (P<0.001). But in other variables, no statistically significant difference was observed between the two groups.
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Conclusion: The results of the present study showed that obesity can have adverse effects on the ECG of children compared to normal-weight individuals. These changes are associated with an increased risk of arrhythmias. Given that these changes can be corrected with weight control, it is recommended to warn families and educate them to prevent and control overweight and obesity.
Reza Atef Yekta , Hoda Kavosi , Pouria Esavand, Monir Sadat Hakemi , Abdolvahhab Baradaran, Zahra Tamartash,
Volume 81, Issue 8 (11-2023)
Abstract
Background: Systemic Sclerosis (SSc) is an autoimmune disease with multi-organ involvement mostly due to fibrosis and ectopic or excessive collagen fibers production in organs. Myocardial fibrosis is the main finding of cardiac involvement in patients with systemic sclerosis. In recent studies, the presence of Fragmented QRS complexes (FQRS) has been shown in the surface electrocardiogram in relation to fibrosis.
Methods: The present study is a case-control study during March 2019 to February 2020 that was conducted in 148 patients with scleroderma referred to the Rheumatology clinic in Shariati Hospital and 101 non-ischemic individuals in the control group matched by age and sex with the patient group. All the medical records were reviewed and those who were low risk according to 10-year atherosclerotic cardiovascular disease (ASCVD) risk assessment were selected as case groups. Data of ECG were evaluated for availability of FQRS or conductive abnormalities and calculating PR, QRS, QT, QTc and Tp-e intervals.
Results: Of the 141 patients with systemic sclerosis, 127(85.81%) were female and 21(14.19%) were male. In the control group, 81 women (80.2%) were present. 61(41.2%) of patients with scleroderma and 8(7.9%) of the control group in this study had FQRS changes in their electrocardiogram. In this study, QRS, QTc and Tp-e intervals were significantly higher in patients with systemic sclerosis compared to those in the control group. The frequency of FQRS, LAHB and LPHB in patients with systemic sclerosis was significantly more than control group. The relationship between PR, QRS, QTc, Tp-e intervals with age, length of disease onset and the severity of skin involvement was assessed. There was a significant correlation between PR-interval and age. Furthermore, there were a correlation between QRS interval and Rodnan skin score, Pulmonary Artery Pressure and Finger to Palm. It is also a meaningful correlation between QTc interval and Rodnan score.
Conclusion: The FQRS finding in electrocardiogram in patients with systemic sclerosis, which has no obvious cardiac symptoms, may indicate myocardial fibrosis and predict future cardiac disorders. |
Hossein Akhavan, Fatemeh Rezaei,
Volume 83, Issue 3 (6-2025)
Abstract
Background: An Electrocardiogram is a non-invasive method for receiving heart signals. Despite advances in imaging methods, the electrocardiogram still plays an important role remains a vital tool in the diagnosis of heart diseases. Analysis of electrocardiogram signals plays an important role in the early detection of heart diseases such as arrhythmias and heart attacks. Today, with the advancement of science and technology, computer methods have received more and more attention from doctors. In this study, machine learning methods were used to classify normal and abnormal heartbeats.
Methods: The data under study were extracted from a dataset called Heartbeat published on the Kaggle website. This dataset includes samples of audio ECG signals that are divided into healthy and unhealthy categories. First, the data were preprocessed and normalized to prepare them for input into the model. Then, temporal and frequency features were extracted from the signals. Next, a hybrid model consisting of one-dimensional convolutional layers was designed and trained. Also, by using the early stopping method, overfitting was prevented and the stability of the model was improved.
Results: In this study, it was shown that by using deep learning, especially using CNN and 1D Conv, an accuracy of 0.99% and a loss of 0.0350 for test data in detecting normal and abnormal heartbeats can be achieved. This model has the ability to analyze complex structures and temporal dynamics of ECG signals and is able to detect patterns related to cardiac disorders.
Conclusion: Today, the electrocardiogram has received more attention than ever before. Appropriate selection of the model, data standardization, and a qualitative range of data are among the factors of high accuracy in this study. This study can be an effective step in the development of intelligent systems for diagnosing cardiac disorders and can be used in medical applications, especially in the field of continuous patient monitoring.
