Atrial Fibrillation Automatic Diagnosis Based on ECG Signal Using Pretrained Deep Convolution Neural Network and SVM Multiclass Model

Almalchy, Mohammed Tali; Al-Gayar, Sarmad Monadel Sabree; Popescu, Nirvana

doi:10.1109/comm48946.2020.9141994

Cited by 6 publications

(1 citation statement)

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“…Specifically, tasks include arrhythmia diagnosis, atrial fibrillation diagnosis, epileptic seizures detection, etc. Pathak et al [166,183] attempted to use the pre-training method to develop an automatic arrhythmia diagnosis system on one dataset and fine-tuning it on another dataset to evaluate the effectiveness of the pre-training model for ECG data, in which the data used in the tasks were all labelled by cardiovascular experts and the dataset used for pre-training and the fine-tuning under the same tasks. The works [16,184,185] employed the same method to detect atrial fibrillation (AF), but they trained the model on a general ECG dataset and fine-tuned it on an AF dataset.…”

Section: Disease Diagnosismentioning

confidence: 99%

Pre-training in Medical Data: A Survey

et al. 2023

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Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data, such as clinical imaging data, bio-signal data, electronic health records (EHR), and multi-modality medical data. With the development of deep neural networks in the last decade, the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods’ performance in a data-limited scenario. In recent years, studies of pre-training in the medical domain have achieved significant progress. To summarize these technology advancements, this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data. In this survey, we summarize a large number of related publications and the existing benchmarking in the medical domain. Especially, the survey briefly describes how some pre-training methods are applied to or developed for medical data. From a data-driven perspective, we examine the extensive use of pre-training in many medical scenarios. Moreover, based on the summary of recent pre-training studies, we identify several challenges in this field to provide insights for future studies.

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