Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Lyon, Aurore; Mincholé, Ana; Martínez, Juan Pablo; Laguna, Pablo; Rodriguez, Blanca

doi:10.1098/rsif.2017.0821

Cited by 174 publications

(106 citation statements)

References 95 publications

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“…A contributing factor for this is the shortage of full digital 12-lead ECG databases, since most ECG are still registered only on paper, archived as images, or in PDF format [16]. Most available databases comprise a few hundreds of tracings and no systematic annotation of the full list of ECG diagnosis [17], limiting their usefulness as training datasets in a deep learning setting.…”

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confidence: 99%

Automatic diagnosis of the 12-lead ECG using a deep neural network

et al. 2020

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We present a model for predicting electrocardiogram (ECG) abnormalities in shortduration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning papers. Using the database of a large telehealth network, we built a novel dataset with more than 2 million ECG tracings, orders of magnitude larger than those used in previous studies. Moreover, our dataset is more realistic, as it consist of 12-lead ECGs recorded during standard in-clinics exams. Using this data, we trained a residual neural network with 9 convolutional layers to map 7 to 10 second ECG signals to 6 classes of ECG abnormalities. Future work should extend these results to cover a large range of ECG abnormalities, which could improve the accessibility of this diagnostic tool and avoid wrong diagnosis from medical doctors. IntroductionCardiovascular diseases are the leading cause of death worldwide [1] and the electrocardiogram (ECG) is a major diagnostic tool for this group of diseases. As ECGs transitioned from analogue to digital, automated computer analysis of standard 12-lead electrocardiograms gained importance in the process of medical diagnosis [2]. However, limited performance of classical algorithms [3,4] precludes its usage as a standalone diagnostic tool and relegates it to an ancillary role [5].End-to-end deep learning has recently achieved striking success in task such as image classification [6] and speech recognition [7], and there are great expectations about how this technology may improve health care and clinical practice [8][9][10]. So far, the most successful applications used a supervised learning setup to automate diagnosis from exams. Algorithms have achieved better performance than a human specialist on their routine workflow in diagnosing breast cancer [11] and detecting certain eye conditions from eye scans [12]. While efficient, training deep neural networks using supervised learning algorithms introduces the need for large quantities of labeled data which, for medical applications, introduce several challenges, including those related to confidentiality and security of personal health information [13].Standard, short-duration 12-lead ECG is the most commonly used complementary exam for the evaluation of the heart, being employed across all clinical settings: from the primary care centers to the intensive care units. While tracing cardiac monitors and long-term monitoring, as the Holter exam, provides information mostly about cardiac rhythm and repolarization, 12-lead ECG can provide a full evaluation of heart, including arrhythmias, conduction disturbances, acute coronary syndromes, cardiac chamber hypertrophy and enlargement and even the effects of drugs and electrolyte disturbances.Machine Learning for Health (ML4H) Workshop at NeurIPS 2018.

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confidence: 99%

Automatic diagnosis of the 12-lead ECG using a deep neural network

et al. 2020

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“…However, these kinds of solutions are not effective for high-dimensional, more complex, real-world noisy signals, and these systems can suffer from an unreliable accuracy [22]. DL has been suggested to likely achieve a more effective analysis of ECG signals because its proven significant and remarkable improvements in robustness to noise and variability in several pattern recognition applications help real-time classification of very complex signals, though this scenario also raises new challenges, like regarding data availability [53,179]. Apart from health monitoring and medical diagnosis, the use of ECG as a biometric trait for identification or authentication has gained momentum [180].…”

Section: Applications In Ecg Processingmentioning

confidence: 99%

“…Several ML approaches (Neural Networks among them) were analyzed [177] for diagnosing myocardial infarction, differentiating arrhythmias, and detecting hypertrophy by using ECG signals. The computational methods in use for ECG analysis have been described [179], with focus on ML and three-dimensional computer simulations, as well as on their accuracy, clinical implications, and contributions to medical advances. The relevance of DL is scrutinized therein by reviewing a few related papers.…”

Section: Recent Interest In Deep Learning For Ecgmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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“…Therefore, in this paper, various methodological approaches have been acquired and addressed that produces malleable and extensible analytical frameworks that can be adopted for a wider range of demands [9]. D. It is a well-known and highly acknowledgeable fact that cardiovascular disorders are responsible for over 31% of deaths worldwide as cited by World Health Organization (WHO) [10]. Therefore, it is necessary to identify the patients at a higher risk at earlier stages and the working professionals or physicians should have a better interpretation and understanding of the various mechanisms used in the ECG analysis so that the efficiency of the treatment and diagnosis is increased.…”

Section: Related Workmentioning

confidence: 99%

Deduction of Efficient Scanning Machines using Big Data Technology

Fiorenza. J*,

Saxena

2019

IJRTE

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Nowadays, the advancement in the field of information technology has witnessed stupendous growth in various industries, especially the medical imaging technologies in the healthcare industry. However, these advancements in the different technologies have not only made the data bigger but also a bit difficult to process and handle it. Though, these advancements may have resulted in huge amount of unnecessary data, it still cannot be considered as a major problem in today’s world as nowadays, the various advancements in technologies such as Big Data Analytics, Cloud Computing and several others, have made it really easy and effortless for storing huge amount of datasets and handling them. One of the boon that the advancement in technology has given to the world in the field of healthcare industry is the evolution of the scanning machines which can be used for the diagnosis of different diseases and to assemble the conclusions in the form of various medical reports for different scans such as ECG (Electrocardiogram), MRI(Magnetic Resonance Imaging) Brain scans, Ultrasounds, X-Rays, CT-Scanners and much more. But, the interesting part here is that though these scanning machines have their own advantages, one of the main disadvantages of them is that the efficiency of the results produced by them are yet to be known when comparing their performance’s to justify their enormous costs. Therefore, in the paper, the key challenges and various methodologies are being investigated in the healthcare industry with prime focus on comparing the scanning machines such as ECG, MRI, and Ultrasoundetc. by using Big Data Analytics. The various manufacturers of the scanning devices which are used by the hospitals or diagnostic centers have already fixed their price to such a high level that, even the hospitals have to spend lots of money to buy those machines and install them. Therefore, as a management side it becomes difficult to cope up with the performance related cost effectiveness of machines, which even shatters the trust of patients related to technical issues with a particular hospital. The prime aim is to focus on the precise implementation, performance efficiency and cost effectiveness of all the medical scans. The idea can also be implemented in improving theperformance along with the cost effectiveness of machines and devices other than the medical industry as well.

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Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Cited by 174 publications

References 95 publications

Automatic diagnosis of the 12-lead ECG using a deep neural network

Automatic diagnosis of the 12-lead ECG using a deep neural network

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deduction of Efficient Scanning Machines using Big Data Technology

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