Identification of cardiac wall motion abnormalities in diverse populations by deep learning of the electrocardiogram by Albert J. Rogers, Neal K. Bhatia, Sabyasachi Bandyopadhyay, James Tooley, Rayan Ansari, Vyom Thakkar, Justin Xu, Jessica Torres Soto, Jagteshwar S. Tung, Mahmood I. Alhusseini, Paul Clopton, Reza Sameni, Gari D. Clifford, J. Weston Hughes, Euan A. Ashley, Marco V. Perez, Matei Zaharia, Sanjiv M. Narayan

“…Abstract Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. …”
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Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods by Said Ziani, Yousef Farhaoui, Mohammed Moutaib

Subjects: “…fetal electrocardiogram…”
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The electrocardiogram in hypertensive patients. P wave dispersion: a new measure to be considered by Elibet Chávez González, Emilio González Rodríguez, Jesús Castro Hevia, María Del Carmen Llanes Camacho, Yosvany García Nóbrega, Raimundo Carmona Puerta, Ramiro Ramos Ramírez

“…The electrocardiogram is a complementary test with high specificity but low sensitivity. …”
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Compressed sensing for electrocardiogram acquisition in wireless body sensor network: A comparative analysis by Junxin Chen, Jiazhu Xing, Leo Yu Zhang, Lin Qi

“…For prolonging the monitoring duration of biosignals, compressed sensing is also exploited for simultaneous sampling and compression of electrocardiogram signals in the wireless body sensor network. …”
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Association between skin sympathetic nerve activity and electrocardiogram alterations after subarachnoid hemorrhage by Yoichiro Nakagawa, Takashi Kusayama, Sho Tamai, Yuta Nagamori, Kazutaka Takeuchi, Shuhei Iwaisako, Taishi Tsutsui, Tomoya Kamide, Kouichi Misaki, Soichiro Usui, Kenji Sakata, Mitsutoshi Nakada, Masayuki Takamura

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Development of rapid evaluation scale for pericardiectomy of tuberculous constrictive pericarditis by 12-lead electrocardiogram by Yanhong Ren, Yanshu Dong, Xinyue Fan, Hongrui Xu, Shuangyi Yin

Subjects: “…Electrocardiogram…”
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Progression of Wolff–Parkinson–White pattern 12-lead electrocardiogram changes in an asymptomatic athlete by Hwei Sian Shauna Sim, Syed Saqib Imran, Chin Sim Teoh

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Functional feature extraction and validation from twelve-lead electrocardiograms to identify atrial fibrillation by Wei Yang, Rajat Deo, Wensheng Guo

“…Abstract Background Deep learning methods on standard, 12-lead electrocardiograms (ECG) have resulted in the ability to identify individuals at high-risk for the development of atrial fibrillation. …”
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Develop an emotion recognition system using jointly connectivity between electroencephalogram and electrocardiogram signals by Javid Farhadi Sedehi, Nader Jafarnia Dabanloo, Keivan Maghooli, Ali Sheikhani

“…This study pioneers an innovative approach to improve the accuracy and dependability of emotion recognition (ER) systems by integrating electroencephalogram (EEG) with electrocardiogram (ECG) data. We propose a novel method of estimating effective connectivity (EC) to capture the dynamic interplay between the heart and brain during emotions of happiness, disgust, fear, and sadness. …”
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A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction by Ching-Heng Lin, Zhi-Yong Liu, Pao-Hsien Chu, Jung-Sheng Chen, Hsin-Hsu Wu, Ming-Shien Wen, Chang-Fu Kuo, Ting-Yu Chang

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Right Ventricular Compression Mimicking Brugada-Like Electrocardiogram in a Patient with Recurrent Pectus Excavatum by Jinhee Ahn, Jong-Il Choi, Jaemin Shim, Sung Ho Lee, Young-Hoon Kim

“…We report a case of a patient with recurrent PE after surgical correction presenting with palpitation and electrocardiogram (ECG) showing ST-segment elevation on the right precordial leads, which could be mistaken for a Brugada syndrome (BrS).…”
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Prognostic and diagnostic accuracy of intracoronary electrocardiogram recorded during percutaneous coronary intervention: a meta-analysis by Jun Fan, Weijie Li, Yizhi Pan, Jialin He, Jiankai Huang, Pingan Chen

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Heart-retina time analysis using electrocardiogram-coupled time-resolved dynamic optical coherence tomography by Philippe Valmaggia, Julia Wolleb, Florentin Bieder, Hendrik P. N. Scholl, Philippe C. Cattin, Peter M. Maloca

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Synthesized 18-Lead Electrocardiogram in Diagnosing Posterior Stemi-Equivalent Acute Coronary Syndrome in Patients with NSTEMI by Tomoki Horie, Rikuta Hamaya, Tomoyo Sugiyama, Hidenori Hirano, Masahiro Hoshino, Yoshihisa Kanaji, Tetsumin Lee, Taishi Yonetsu, Tetsuo Sasano, Tsunekazu Kakuta

“…To assess the clinical utility of synthesized V7–V9 ST-segment elevation (sV7-9 STE) in patients with 12-lead-electrocardiogram (ECG)-based non-STE myocardial infarction (NSTEMI) in diagnosing left circumflex artery (LCx) STEMI-equivalent acute coronary syndrome (ACS). …”
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Methods of program analysis of high-resolution electrocardiogram recorded using hardware-software complex based on nanosensors by M. L. Ivanov, D. K. Avdeeva, М. М. Yuzhakov, S. А. Rybalka, Guo Wenjia, I. V. Maksimov, M. V. Balakhonova

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UTILIZATION OF K-NEAREST NEIGHBOR ALGORITHM TO ANALYZE AND CLASSIFY HEART DISORDERS BASED ON ELECTROCARDIOGRAM RECORDING DATA by Sumiati, Hanif Nurmajid, Muhammad Ibrohim, Hendry Gunawan

“… This study develops a system to classify heart conditions based on electrocardiogram (ECG) medical records using the K-Nearest Neighbor (KNN) method. …”
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Electrocardiogram Parameters That Affect the Success Rate of Radiofrequency Ablation in Patients with Outflow Tract Ventricular Premature Complexes by Zhen Ye, Zhe Xu, Kezeng Gong, Xuehai Chen, Weiwei Wang, Jianhua Chen, Lianglong Chen, Feilong Zhang

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A Multi-Level Multiple Contrastive Learning Method for Single-Lead Electrocardiogram Atrial Fibrillation Detection by Yonggang Zou, Peng Wang, Lidong Du, Xianxiang Chen, Zhenfeng Li, Junxian Song, Zhen Fang

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A case report of atrial enlargement and transitory electrocardiographic changes during labour by Antonio VILLA, Donatella RODEGHER, Valeria BARBERA, Carlo TURRI

Subjects: “…atrial enlargement; electrocardiogram; pregnancy…”
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Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study by Soonil Kwon, SooMin Chung, So-Ryoung Lee, Kwangsoo Kim, Junmo Kim, Dahyeon Baek, Hyun-Lim Yang, Eue-Keun Choi, Seil Oh

“…A convolutional neural networks-based model customized to the study (AFibEFNet) and other deep-learning models were investigated. Electrocardiogram signals, ECG features, and clinical features (demographic information, comorbidities, blood cell counts, and blood test results) were collected for training. …”
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