Development and Validation of an Algorithm for the Digitization of ECG Paper Images

Randazzo, Vincenzo; Puleo, Edoardo; Paviglianiti, Annunziata; Vallan, Alberto; Pasero, Eros

doi:10.3390/s22197138

Cited by 11 publications

(2 citation statements)

References 37 publications

(41 reference statements)

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“…A basic multi-layer perceptron was also fed with ECG signals extracted from the two-heartbeat image crops, which are detailed in the next sections. A signal extraction tool [45] was used, yielding 500-sample numerical signals for precordial leads (V1 to V3, since leads V4 to V6 were too noisy to be digitized on several images). This process was remarkably complicated and time-consuming.…”

Section: Shallow Neural Network: Signalsmentioning

confidence: 99%

Artificial Intelligence ECG Analysis in Patients with Short QT Syndrome to Predict Life-Threatening Arrhythmic Events

Pasero,

Gaita,

Randazzo

et al. 2023

Sensors

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Short QT syndrome (SQTS) is an inherited cardiac ion-channel disease related to an increased risk of sudden cardiac death (SCD) in young and otherwise healthy individuals. SCD is often the first clinical presentation in patients with SQTS. However, arrhythmia risk stratification is presently unsatisfactory in asymptomatic patients. In this context, artificial intelligence-based electrocardiogram (ECG) analysis has never been applied to refine risk stratification in patients with SQTS. The purpose of this study was to analyze ECGs from SQTS patients with the aid of different AI algorithms to evaluate their ability to discriminate between subjects with and without documented life-threatening arrhythmic events. The study group included 104 SQTS patients, 37 of whom had a documented major arrhythmic event at presentation and/or during follow-up. Thirteen ECG features were measured independently by three expert cardiologists; then, the dataset was randomly divided into three subsets (training, validation, and testing). Five shallow neural networks were trained, validated, and tested to predict subject-specific class (non-event/event) using different subsets of ECG features. Additionally, several deep learning and machine learning algorithms, such as Vision Transformer, Swin Transformer, MobileNetV3, EfficientNetV2, ConvNextTiny, Capsule Networks, and logistic regression were trained, validated, and tested directly on the scanned ECG images, without any manual feature extraction. Furthermore, a shallow neural network, a 1-D transformer classifier, and a 1-D CNN were trained, validated, and tested on ECG signals extracted from the aforementioned scanned images. Classification metrics were evaluated by means of sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve. Results prove that artificial intelligence can help clinicians in better stratifying risk of arrhythmia in patients with SQTS. In particular, shallow neural networks’ processing features showed the best performance in identifying patients that will not suffer from a potentially lethal event. This could pave the way for refined ECG-based risk stratification in this group of patients, potentially helping in saving the lives of young and otherwise healthy individuals.

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Section: Shallow Neural Network: Signalsmentioning

confidence: 99%

Artificial Intelligence ECG Analysis in Patients with Short QT Syndrome to Predict Life-Threatening Arrhythmic Events

Pasero,

Gaita,

Randazzo

et al. 2023

Sensors

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“…Analogue methods of the recording of changes in the vector of electromagnetic forces have been replaced with digital recording over time, and the interpretation of changes in the ECG curve, initially based on the assessment of P, Q, R, S and T waves by means of the human eye, has been replaced with automatic analysis [4,5]. Collecting thousands of digital ECG records in databases and confronting them with diagnostic methods based on cardiac imaging as well as with clinical data allows us to look at the ECG curve as an inexhaustible source of information about patients [6][7][8]. This happened thanks to artificial intelligence (AI), a tool which saw much more in the ECG than the human eye [9,10].…”

Section: Introductionmentioning

confidence: 99%

What Else Can AI See in a Digital ECG?

Rechciński

2023

JPM

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The electrocardiogram (ECG), considered by some diagnosticians of cardiovascular diseases to be a slightly anachronistic tool, has acquired a completely new face and importance thanks to its three modern features: the digital form of recording, its very frequent use, and the possibility of processing thousands of records by artificial intelligence (AI). In this review of the literature on this subject from the first 3 months of 2023, the use of many types of software for extracting new information from the ECG is described. These include, among others, natural language processing, backpropagation neural network and convolutional neural network. AI tools of this type allow physicians to achieve high accuracy not only in ECG-based predictions of the patient’s age or sex but also of the abnormal structure of heart valves, abnormal electrical activity of the atria, distorted immune response after transplantation, good response to resynchronization therapy and an increased risk of sudden cardiac death. The attractiveness of the presented results lies in the simplicity of the examination by the staff, relatively low costs and even the possibility of performing the examination remotely. The twelve studies presented here are just a fraction of the novelties that the current year will bring.

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