Gotowe bibliografie tematyczne / CARDIAC ANOMALIES DETECTION

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Gotowa bibliografia na temat „CARDIAC ANOMALIES DETECTION”

Autor: Grafiati

Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "CARDIAC ANOMALIES DETECTION"

Bahtiyar, Mert Ozan, i Joshua A. Copel. "Improving Detection of Fetal Cardiac Anomalies". Journal of Ultrasound in Medicine 26, nr 12 (grudzień 2007): 1639–41. http://dx.doi.org/10.7863/jum.2007.26.12.1639.

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Triqui, Bouchra, i Abdelkader Benyettou. "Semi-Supervised Kohonen Map for Cardiac Anomalies Detection". International Review on Modelling and Simulations (IREMOS) 12, nr 3 (30.06.2019): 196. http://dx.doi.org/10.15866/iremos.v12i3.16953.

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Morgan, Jamie L., John J. Byrne, Donald D. McIntire, Diane M. Twickler i Jodi S. Dashe. "Detection of Fetal Cardiac Anomalies Using Standard Sonography [32S]". Obstetrics & Gynecology 133, nr 1 (maj 2019): 210S. http://dx.doi.org/10.1097/01.aog.0000559482.62257.15.

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Nicole A, Bailey, Aldawsari Khalifah A, Zeidenweber Carlo M i Khan* Danyal M. "Sudden Cardiac Death in a Neonate Due to Bilateral Absence of Coronary Artery Ostium". Journal of Cardiology and Cardiovascular Medicine 8, nr 2 (24.07.2023): 086–88. http://dx.doi.org/10.29328/journal.jccm.1001158.

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Introduction: Congenital heart disease is a leading cause of neonatal mortality linked to birth defects. Despite the widespread availability of prenatal screenings, detection rates remain low. Accurate early detection of these lesions is pivotal to reducing neonatal morbidity and mortality. Methods: In this case, we present a neonate who experienced sudden cardiac death due to a rare, undiagnosed congenital cardiac anomaly - the bilateral absence of coronary artery ostium. Discussion: This case highlights the importance of prenatal detection of congenital cardiac anomalies. While fetal echocardiography is frequently utilized, it only identifies CHD in 36-50% of cases. This is attributed to inadequate imaging procedures, varied operator skills, and regional discrepancies. Early detection of severe CHD is essential for specialized treatment, thereby mitigating neonatal health risks and improving survival rates. Conclusion: Prenatal detection of CHD, especially coronary anomalies, continues to pose significant challenges. There is a pressing need to establish and enforce standardized protocols for fetal echocardiography aimed at these anomalies. To enhance care and improve outcomes, a joint effort between academic institutions and community centers is encouraged. Learning Objectives: • Congenital coronary artery anomalies are a significant cause of sudden cardiac death in children. • The absence of a coronary artery ostium is known to be associated with other congenital heart diseases, particularly pulmonary atresia with an intact ventricular septum. However, isolated coronary disease has also been reported in this case. • Prenatal echocardiography is a valuable tool for diagnosing congenital heart disease. However, certain limitations may be encountered when diagnosing coronary artery anomalies.

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Tasha, Ilir, Rachel Brook, Heidi Frasure i Noam Lazebnik. "Prenatal Detection of Cardiac Anomalies in Fetuses with Single Umbilical Artery: Diagnostic Accuracy Comparison of Maternal-Fetal-Medicine and Pediatric Cardiologist". Journal of Pregnancy 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/265421.

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Aim. To determine agreement of cardiac anomalies between maternal fetal medicine (MFM) physicians and pediatric cardiologists (PC) in fetuses with single umbilical artery (SUA).Methods. A retrospective review of all fetuses with SUA between 1999 and 2008. Subjects were studied by MFM and PC, delivered at our institution, and had confirmation of SUA and cardiac anomaly by antenatal and neonatal PC follow-up. Subjects were divided into four groups: isolated SUA, SUA and isolated cardiac anomaly, SUA and multiple anomalies without heart anomalies, and SUA and multiple malformations including cardiac anomaly.Results. 39,942 cases were studied between 1999 and 2008. In 376 of 39,942 cases (0.94%), SUA was diagnosed. Only 182 (48.4%) met inclusion criteria. Cardiac anomalies were found in 21% (38/182). Agreement between MFM physicians and PC in all groups combined was 94% (171/182) (95% CI [89.2, 96.8]). MFM physicians overdiagnosed cardiac anomalies in 4.4% (8/182). MFM physicians and PC failed to antenatally diagnose cardiac anomaly in the same two cases.Conclusions. Good agreement was noted between MFM physicians and PC in our institution. Studies performed antenatally by MFM physicians and PC are less likely to uncover the entire spectrum of cardiac abnormalities and thus neonatal follow-up is suggested.

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Bak, G. S., B. L. Shaffer, E. Madriago, A. Allen, B. Kelly, A. B. Caughey i L. Pereira. "Detection of fetal cardiac anomalies: cost‐effectiveness of increased number of cardiac views". Ultrasound in Obstetrics & Gynecology 55, nr 6 (czerwiec 2020): 758–67. http://dx.doi.org/10.1002/uog.21977.

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Nannavecchia, Antonella, Francesco Girardi, Pio Raffaele Fina, Michele Scalera i Giovanni Dimauro. "Personal Heart Health Monitoring Based on 1D Convolutional Neural Network". Journal of Imaging 7, nr 2 (5.02.2021): 26. http://dx.doi.org/10.3390/jimaging7020026.

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The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.

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Marimon, Xavier, Sara Traserra, Marcel Jiménez, Andrés Ospina i Raúl Benítez. "Detection of Abnormal Cardiac Response Patterns in Cardiac Tissue Using Deep Learning". Mathematics 10, nr 15 (5.08.2022): 2786. http://dx.doi.org/10.3390/math10152786.

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This study reports a method for the detection of mechanical signaling anomalies in cardiac tissue through the use of deep learning and the design of two anomaly detectors. In contrast to anomaly classifiers, anomaly detectors allow accurate identification of the time position of the anomaly. The first detector used a recurrent neural network (RNN) of long short-term memory (LSTM) type, while the second used an autoencoder. Mechanical contraction data present several challanges, including high presence of noise due to the biological variability in the contraction response, noise introduced by the data acquisition chain and a wide variety of anomalies. Therefore, we present a robust deep-learning-based anomaly detection framework that addresses these main issues, which are difficult to address with standard unsupervised learning techniques. For the time series recording, an experimental model was designed in which signals of cardiac mechanical contraction (right and left atria) of a CD-1 mouse could be acquired in an automatic organ bath, reproducing the physiological conditions. In order to train the anomaly detection models and validate their performance, a database of synthetic signals was designed (n = 800 signals), including a wide range of anomalous events observed in the experimental recordings. The detector based on the LSTM neural network was the most accurate. The performance of this detector was assessed by means of experimental mechanical recordings of cardiac tissue of the right and left atria.

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van Dooren, Marieke F., Natascha N. T. Goemaere, Annelies de Klein, Dick Tibboel i Ronald R. de Krijger. "Postmortem Findings and Clinicopathological Correlation in Congenital Diaphragmatic Hernia". Pediatric and Developmental Pathology 7, nr 5 (wrzesień 2004): 459–67. http://dx.doi.org/10.1007/s10024-004-1118-2.

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Congenital diaphragmatic hernia (CDH) is a severe life-threatening disease, with an incidence of 3 per 10,000 births, that can occur as an isolated defect or in combination with other congenital anomalies. We reviewed the clinical and autopsy reports of 39 subjects with CDH that were autopsied between 1988 and 2001 to determine whether autopsy had an additional value in the detection of malformations in patients with CDH. We compared the clinical data (including echographic results in some patients) concerning congenital anomalies with the autopsy results. Before autopsy, 6 structural cardiac defects, 3 anomalies of the urogenital system, and 3 anomalies of the digestive tract were observed in 10 patients (clinical and echographic results). However, with postmortem examination, only 4 structural cardiac defects were confirmed, 2 cases showed another cardiac anomaly, and 7 new cardiac defects were found. In the urogenital system, 1 anomaly was confirmed, 1 was not confirmed, and 1 showed another malformation. In addition, in 7 patients new urogenital malformations were found after autopsy. In the digestive tract, all 3 malformations were confirmed, but we found 3 new malformations after postmortem examination. All clinically established dysmorphic features and anomalies of the skeletal system and central nervous system were confirmed by autopsy, and no additional malformations were found. We concluded that postmortem examination has an important additional role in the detection of structural cardiac defects and malformations of the urogenital system and digestive tract in children with CDH.

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Menashe, M., R. Arbel, D. Raveh, R. Achiron i S. Yagel. "Poor prenatal detection rate of cardiac anomalies in Noonan syndrome". Ultrasound in Obstetrics and Gynecology 19, nr 1 (styczeń 2002): 51–55. http://dx.doi.org/10.1046/j.0960-7692.2001.00485.x.

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Rozprawy doktorskie na temat "CARDIAC ANOMALIES DETECTION"

Sridhar, Shravan. "Comparison of 64‐Slice EKG‐Gated Computed Tomographic Angiography, Transthoracic Echocardiography, and Transesophageal Echocardiography for Detection and Complete Characterization of Anomalous Coronary Arteries in Infants with Comorbid Congenital Cardiac Malformations". Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/603665.

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A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine.
Background and Objective: Computed tomographic angiography (CTA) offers several benefits over echocardiography in the detection of CAAs (coronary artery anomalies). These include higher spatial resolution, operator independency, non‐invasiveness, and the availability of reconstructive techniques to track the entire arterial course.1,4,9 Accordingly, standard clinical practice (per ACC/AHA guidelines for adults with CAAs) for adults with suspected CAAs includes use of CTA as a first‐line imaging modality.6 Currently, there is no evidence favoring either CTA, transthoracic echocardiography (TTE), or transesophageal echocardiography (TEE) for initial imaging of infants with suspected CAAs. Therefore, the aims of this retrospective study include investigating the efficacy of CTA, TTE, and TEE in the detection and complete characterization of CAAs. Methods: Imaging and surgical data for 27 patients who presented for evaluation of congenital heart disease between 2006 and 2011 were evaluated. Patients had a mean age of 2.2 ± 0.7 months at initial evaluation and had undergone EKG‐gated 64‐slice cardiac CTA with 3D reconstruction in addition to multiple TTE and TEE studies. Performance metrics (including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) of each modality in CAA detection were computed. Concordance between each modality and surgical/conventional angiographic diagnosis in the characterization of anatomy along the origin, course, and termination of anomalous coronary arteries was evaluated. The rate of limitations of each modality in the imaging and interpretation of coronary anatomy was also reported. Results: Using surgical/angiographic diagnosis as the gold standard, CTA produced a sensitivity, specificity, and accuracy of 80%, 50%, and 74%, respectively. TTE produced a sensitivity, specificity, and accuracy of 20%, 50%, and 26%, respectively. TEE produced a sensitivity, specificity, and accuracy of 27%, 100%, and 42%, respectively. CTA outperformed TTE and TEE at characterizing anatomy at the origin and course of an anomalous coronary artery. At characterizing anatomy at the termination of an anomalous coronary artery, CTA outperformed TEE but did not significantly outperform TTE. CTA had a higher rate of documented limitations to imaging/interpretation compared to TTE and TEE but a lower rate when compared to conventional angiography. Conclusion and Impact: CTA is a rapid, non‐invasive, operator‐independent imaging modality that offers high resolution, 3‐dimensional imaging of CAAs in infants. The results of this study indicate that CTA is the most sensitive and accurate modality for detection of CAAs in infants and is optimal for characterizing anatomy along the entire length of an anomalous coronary artery. As such, CTA may be the optimal modality for first‐line coronary artery imaging in infants with suspected anomalous coronary artery anatomy who have a high pretest probability for having a CAA.

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PATEL, VIJAY KUMAR. "CARDIAC ANOMALIES DETECTION FROM ECG". Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16147.

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Cardiac diseases are one of the leading cause of death across the world. Identification of many heart diseases involving irregular heartbeat relies upon the reading of an ECG. An ECG is the electrical signal of the heart that is taken using electrodes put on the skin. Ideally 12-lead ECG on the patients and on the chest. The magnitude of the heart is then measured from the twelve different angles and is recorded over a period of time. In this project we propose efficient technique to automatically extract the ECG features. In this work we are able to identify at least 9 diseases (ischemia peaked, ischemia2, ischemia3, ischemia4, stemi1, stemi2, stemi3, stemi4 and stemi5) from data collected by ECG in raw form or in biometric form. The efficiency of the results is as high as 70% for regular (non-noisy) data. All the ECG databases used here are from the Pysionet’s [1] online databases.

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PATEL, VIJAY KUMAR. "CARDIAC ANOMALIES DETECTION FROM ECG". Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16325.

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Części książek na temat "CARDIAC ANOMALIES DETECTION"

Cekić, Miloš. "Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review". W Artificial Intelligence. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.112582.

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Anomaly detection in medical data is often of critical importance, from diagnosing and potentially localizing disease processes such as epilepsy to detecting and preventing fatal events such as cardiac arrhythmias. Generative adversarial networks (GANs) have since their inception shown promise in various applications and have been shown to be effective in cybersecurity, data denoising, and data augmentation, and have more recently found a potentially important place in the detection of anomalies in medical time series. This chapter provides a selective review of this novel use of GANs, in the process highlighting the nature of anomalies in time series, special challenges related to medical time series, and some general issues in approaching time series anomaly detection with deep learning. We cover the most frequently applied GAN models and briefly detail the current landscape of applying GANs to anomaly detection in two commonly used medical time series, electrocardiography (ECG) and electroencephalography (EEG).

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B. J., Sowmya, Pradeep Kumar D., Hanumantharaju R., Gautam Mundada, Anita Kanavalli i Shreenath K. N. "Development of an Efficient Monitoring System Using Fog Computing and Machine Learning Algorithms on Healthcare 4.0". W Advances in Systems Analysis, Software Engineering, and High Performance Computing, 78–98. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8161-2.ch005.

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Disruptive innovations in data management and analytics have led to the development of patient-centric Healthcare 4.0 from the hospital-centric Healthcare 3.0. This work presents an IoT-based monitoring systems for patients with cardiovascular abnormalities. IoT-enabled wearable ECG sensor module transmits the readings in real-time to the fog nodes/mobile app for continuous analysis. Deep learning/machine learning model automatically detect and makes prediction on the rhythmic anomalies in the data. The application alerts and notifies the physician and the patient of the rhythmic variations. Real-time detection aids in the early diagnosis of the impending heart condition in the patient and helps physicians clinically to make quick therapeutic decisions. The system is evaluated on the MIT-BIH arrhythmia dataset of ECG data and achieves an overall accuracy of 95.12% in classifying cardiac arrhythmia.

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"Fetal medicine and surgery". W Paediatric Surgery, redaktorzy Mark Davenport i Paolo De Coppi, 73–102. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198798699.003.0003.

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This chapter begins by discussing fetal screening (screening for and diagnosis of chromosomal defects) and the use of magnetic resonance imaging in detecting problems such as brain abnormalities or congenital diaphragmatic hernia. Fetal diagnosis of cardiac anomalies, lung anomalies, congenital diaphragmatic hernia, abdominal wall defects, gastrointestinal anomalies, abdominal masses, obstructive uropathy, hydrocephalus, and spina bifida are discussed. There are probably two relevant surgical conditions where actual fetal intervention has a potential role. These are FETO fetoscopic endoluminal tracheal occlusion (FETO) for severe diaphragmatic hernia and in utero closure of spina bifida.

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Prieto, Claudia, René M. Botnar, Hajime Sakuma, Masaki Ishida i Marcus R. Makowski. "Coronary imaging". W The EACVI Textbook of Cardiovascular Magnetic Resonance, redaktorzy Massimo Lombardi, Sven Plein, Steffen Petersen, Chiara Bucciarelli-Ducci, Emanuela R. Valsangiacomo Buechel, Cristina Basso i Victor Ferrari, 164–76. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198779735.003.0019.

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Due to its high soft tissue contrast, high spatial resolution, and lack of ionizing radiation, cardiovascular magnetic resonance (CMR) is a promising imaging modality for non-invasive imaging of the coronary arteries. However, because of the high spatial resolution and large coverage required for visualization of the coronary arteries, scan times are relatively long. This can result in imaging artefacts from cardiac and respiratory motion. Usually, coronary CMR is therefore performed with respiratory and cardiac compensation methods. CMR has shown promising results for the detection of coronary stenosis, when compared against invasive and computed tomography coronary angiography, but in clinical practice, CMR is more often used to define the course of anomalous coronary arteries and for the detection and tracking of coronary artery aneurysms. CMR also allows imaging of the coronary vessel wall and coronary plaque imaging, as well as the detection of coronary thrombus. These emerging methods may have a future role in risk stratification of patients with known or suspected coronary artery disease.

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Beke, Artúr, i Aténé Simonyi. "The Risk of Chromosomal Abnormalities in Cases of Minor and Major Fetal Anomalies in the Second Trimester". W Chromosomal Abnormalities. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.88271.

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Currently, noninvasive intrauterine screening for most chromosome abnormalities is available, but ultrasound examinations also play an important role during pregnancy, by drawing the attention to the suspect of a possible abnormality. Fetal ultrasound disorders can be classified into two major groups: (1) Major abnormalities are actually diagnosed malformations that are often associated with certain chromosome abnormalities but may be associated with other disorders (multiplex malformation) and may occur as isolated disorders (e.g., cardiac disorders, duodenal atresia, omphalocele, cystic hygroma (CH)). (2) Minor anomalies (“soft markers”) are not abnormal in themselves but are mild abnormalities that may occur in normal pregnancy but also increase the risk of certain chromosome aberrations. The minor anomalies in the second trimester include thickened nuchal fold (NF), mild ventriculomegaly, pyelectasis, hyperechogenic bowels, hyperechogenic papillary muscle, and shorter long bones. Plexus choroid cyst which is classified as a minor marker does not increase the risk of Down syndrome but increases the risk of trisomy 18 (Edwards syndrome). We want to emphasize the importance of screening of minor and major ultrasound abnormalities in detecting chromosomal abnormalities in the second trimester.

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Streszczenia konferencji na temat "CARDIAC ANOMALIES DETECTION"

Ben Salah, Ihsen, Kais Ouni i Ridha Ben Salah. "Cardiac anomalies detection by cepstral analysis of ICG signal". W 2016 International Symposium on Signal, Image, Video and Communications (ISIVC). IEEE, 2016. http://dx.doi.org/10.1109/isivc.2016.7893966.

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