43 research outputs found
Development of the Er-Kay Classification: A Novel Volume-Based Assessment of Cesarean Scar Defects and Their Association with Abnormal Uterine Bleeding
No full textObjective: This study aimed to determine the prevalence of isthmocele in patients who had undergone cesarean delivery and to investigate its association with abnormal uterine bleeding (AUB). Additionally, a novel volume-based classification system (Er-Kay Classification) was developed to provide a more precise assessment of cesarean scar defects and their correlation with clinical symptoms. Material and Methods: This retrospective, hospital-based cohort study was conducted at Ankara Etlik Zübeyde Hanım Women’s Health Training and Research Hospital between October 2017 and March 2018. A total of 1098 patients who had undergone cesarean delivery and attended follow-up visits were included. Patients were categorized based on the presence of isthmocele (study group: n = 134) and its absence (control group: n = 964). Isthmocele volume was calculated using the formula (Height × Width × Depth)/3, and patients were classified as Grade 1 (≤50 mm3) or Grade 2 (>50 mm3) based on the novel Er-Kay Classification. Clinical symptoms, including AUB (pre-, inter-, postmenstrual bleeding), dysmenorrhea, dyspareunia, and postcoital bleeding, were compared between groups. Statistical analyses were performed using SPSS 27.0 (NY, USA),with a significance level of p < 0.05. Results: The prevalence of isthmocele was 12.2% (134/1098). Patients with isthmocele had significantly shorter menstrual cycles compared to those without (26.64 ± 5.35 vs. 28.08 ± 4.97 days, p = 0.038). Postmenstrual bleeding (47.0% vs. 4.7%, p < 0.001), dysmenorrhea (38.8% vs. 18.3%, p < 0.001), and dyspareunia (39.6% vs. 14.7%, p < 0.001) were significantly more frequent in the isthmocele group. According to the Er-Kay Classification, intermenstrual bleeding was significantly higher in Grade 2 (23.1%) than in Grade 1 (4.3%) (p = 0.001). Similarly, postmenstrual bleeding was more common in Grade 2 (56.9%) than in Grade 1 (37.7%) (p = 0.026). No significant differences were found for premenstrual bleeding, dysmenorrhea, or dyspareunia between the Er-Kay Classification groups (p > 0.05). Conclusions: The findings indicate that isthmocele is significantly associated with AUB, dysmenorrhea, and dyspareunia. The Er-Kay Classification, based on isthmocele volume, provides a more precise assessment of symptom severity, particularly in intermenstrual and postmenstrual bleeding cases. These results suggest that volume-based evaluations should be incorporated into clinical practice for better patient management and diagnosis of cesarean scar defects
Classification of cardiac arrhythmias using Zhao-Atlas-Marks time-frequency distribution
No full textThe major function of heart is to pump blood to tissues and organs necessary for the body metabolism. It is therefore one of the organs that affects human life. However, adverse situations, such as paralysis and death are the major problems that can lead to a heart failure. Healthy heart is very important to live comfortably. To prevent adverse events, it is important to monitor and detect heart diseases early. The aim of proposed method is to determine and classify nine types of ECG arrhythmias, including normal beats. A large feature set was obtained from the MIT-BIH Arrhythmia database. Zhao Atlas-Mark time-frequency distribution was used to extract the feature set. Five classification algorithms have been tried. The Cubic Support Vector Machine algorithm yielded best performance results. The proposed method achieved accuracy, sensitivity, specificity, F-score, positive predictive, and negative predictive values of 96.39%, 94.22%, 92.02%, 93.91%, 93.90% and 96.72%, respectively. Considering the data size, performance values, and number of arrythmias, the proposed method provided superiority to other studies. Furthermore, running time is suitable for telemedicine systems
Time-frequency approach to ECG classification of myocardial infarction
No full textElectrocardiogram (ECG) analysis is one of the most important techniques to classify myocardial infarction. It is possible to diagnose that the patient may have a heart attack with ST segment elevation or depression in the ECG recordings taken before patient has a myocardial infarction. We propose a method to classify ST segment using time-frequency distribution based features from multi-lead ECG signals. In contrast to many studies in the literature, the proposed method is based on four-class classifcation method and is tested on a large dataset consisting of three different databases, namely MIT-BIH Arrhythmia database, European ST-T database and Long-Term ST database. Among the classification algorithms, the weighted k-NN algorithm achieved the best average performance with accuracy of 94.23%, sensitivity of 95.72% and specificity of 98.15% using Choi-Williams time-frequency distribution features. Meanwhile, the speed of the proposed algorithm is suitable for telemedicine systems. © 2020 Elsevier Ltd114E452 Türkiye Bilimsel ve Teknolojik Araştirma Kurumu 114E452 Türkiye Bilimsel ve Teknolojik Araştirma KurumuWe declare that the authors of the publication have research support from The Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant 114E452.This research is supported by The Scientific and Technological Research Council of Turkey ( TÜBİTAK ) under Grant 114E452 . Ethics committee approval was not required because no data was collected from human subjects and all data samples were collected the three databases available at internet sites
Classification of cardiac arrhythmias using Zhao-Atlas-Marks time-frequency distribution
No full textThe major function of heart is to pump blood to tissues and organs necessary for the body metabolism. It is therefore one of the organs that affects human life. However, adverse situations, such as paralysis and death are the major problems that can lead to a heart failure. Healthy heart is very important to live comfortably. To prevent adverse events, it is important to monitor and detect heart diseases early. The aim of proposed method is to determine and classify nine types of ECG arrhythmias, including normal beats. A large feature set was obtained from the MIT-BIH Arrhythmia database. Zhao Atlas-Mark time-frequency distribution was used to extract the feature set. Five classification algorithms have been tried. The Cubic Support Vector Machine algorithm yielded best performance results. The proposed method achieved accuracy, sensitivity, specificity, F-score, positive predictive, and negative predictive values of 96.39%, 94.22%, 92.02%, 93.91%, 93.90% and 96.72%, respectively. Considering the data size, performance values, and number of arrythmias, the proposed method provided superiority to other studies. Furthermore, running time is suitable for telemedicine systems
Time-frequency approach to ECG classification of myocardial infarction
No full textAbstract Electrocardiogram (ECG) analysis is one of the most important techniques to classify myocardial infarction. It is possible to diagnose that the patient may have a heart attack with ST segment elevation or depression in the ECG recordings taken before patient has a myocardial infarction. We propose a method to classify ST segment using time-frequency distribution based features from multi-lead ECG signals. In contrast to many studies in the literature, the proposed method is based on four-class classifcation method and is tested on a large dataset consisting of three different databases, namely MIT-BIH Arrhythmia database, European ST-T database and Long-Term ST database. Among the classification algorithms, the weighted k-NN algorithm achieved the best average performance with accuracy of 94.23%, sensitivity of 95.72% and specificity of 98.15% using Choi-Williams time-frequency distribution features. Meanwhile, the speed of the proposed algorithm is suitable for telemedicine systems
Wigner-Ville distribution based ECG arrhythmia detection for telemedicine applications
No full textWhile the world's population is growing, average life expectancy is increasing. As a result, the growing elderly population is profoundly affecting the delivery of healthcare for everyone and in particular for those with chronic diseases. The remote monitoring of chronic patients may be achieved by a telemedicine system utilizing today's information and mobile communication technologies. In this study, an ECG arrhythmia detection algorithm based on Wigner-Ville distribution is developed. The performance of the method is tested on data obtained from the PhysioNet database
Detection of ECG arrhythmia using large Choi Williams time-frequency feature fet
No full textMedical Technologies National Congress (TIPTEKNO) -- OCT 12-14, 2017 -- TRABZON, TURKEYEarly detection and monitoring of heart diseases increase human quality of life and this can prevent negative consequences. It is even more important because it can prevent sudden deaths. in today's technology, these operations can be done with telemedicine systems. in this work, appropriate methods have been proposed for telemedicine systems. the proposed system is of two classes and is based on detection of arrhythmia from healthy and diseased ECG signals. MIT-BIH Arrhythmia database was used in the study. A total of 103026 R-R interval were used in this database. in this study, the Choi-Williams transformation is used as an feature extraction method. the performance results are given as accuracy, specificity and positive predictive accuracy, respectively 94.67%, 94.97%, 92.57%, 97.36%, 97.23%IEEE Turkey Sec
Using Wigner-Ville distribution in ECG arrhythmia detection for telemedicine applications
No full textWhile the world's population is growing, average life expectancy is increasing. As a result, the growing elderly population is profoundly affecting the delivery of healthcare for everyone and in particular for those with chronic diseases. The remote monitoring of chronic patients may be achieved by a telemedicine system utilizing today's information and mobile communication technologies. In this study, an ECG arrhythmia detection algorithm based on Wigner-Ville distribution is proposed. The performance of the method is tested on a large dataset obtained from the PhysioNet database. Compared to other studies, the proposed method yields better accuracy, sensitivity and specificity results. Furthermore the computation time is suitable for telemedicine applications
The comparison of the effects of clinical Pilates exercises with and without childbirth training on pregnancy and birth results
No full textAims Childbirth training programs together with exercise during pregnancy have drawn attention in many countries. The aim of this study was to investigate the effects on pregnancy and delivery outcomes of clinical Pilates exercises given with or without childbirth training. Methods A total of 64 pregnant women were randomly separated into three subgroups as Group 1, who received childbirth training with clinical Pilates exercises (n = 21), Group 2, who received only childbirth training (n = 21) and Group 3 as a control group (n = 22). The clinical Pilates exercise training was applied 2 days a week for 8 weeks, and childbirth training was applied one day a week for 4 weeks. Demographic data, weight gain throughout the pregnancy and duration of labour were recorded. Pain intensity during labor was evaluated with a Visual Analogue Scale. Anxiety was evaluated with the State-Trait Anxiety Inventory. Birth outcomes were recorded as gestational age at birth, birth weight and APGAR scores. Results Pre-training, the groups were homogenous in terms of demographic characteristics and general anxiety (P > .05). After the training, the Pilates group had better general anxiety values, gained less weight and felt less pain during labor than the other groups (P .05 for all). The APGAR scores of the infants of the Pilates group were better than those of the other groups (P < .05). Conclusions The study results showed that childbirth training applied with clinical Pilates exercise had a positive effect on pregnant women and their birth outcomes
