Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms

Gliner, Vadim; Keidar, Noam; Makarov, V. V.; Avetisyan, Arutyun; Schuster, Assaf; Yaniv, Yael

doi:10.1038/s41598-020-73060-w

Cited by 25 publications

(15 citation statements)

References 27 publications

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“…In addition, to confirm the effectiveness of the proposed method, we compared the accuracy with one-dimensional CNN, which is used in conventional ECG waveform analysis. The same four ECG inductions (II, V3, V5, aVR) as in the proposed methods were input to independent 1DCNNs, and their features were combined and classified in a fully connected layer [35] . The 1DCNN consists of four convolutional layers and pooling layers.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms

Kondo

Teramoto

Watanabe

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Objective: The early detection of cardiac disease is important because the disease can lead to sudden death and poor prognosis. Electrocardiograms (ECG) are used to screen for cardiac diseases and are useful for the early detection and determination of treatment strategies. However, the ECG waveforms of cardiac care unit (CCU) patients with severe cardiac disease are often complicated by comorbidities and patient conditions, making it difficult to predict the severity of further cardiac disease. Therefore, this study predicts the short-term prognosis of CCU patients to detect further deterioration in CCU patients at an early stage. Methods: The ECG data (II, V3, V5, aVR induction) of CCU patients were converted to image data. The transformed ECG images were used to predict short-term prognosis with a two-dimensional convolutional neural network (CNN). Results: The prediction accuracy was 77.3%. Visualization by GradCAM showed that the CNN tended to focus on the shape and regularity of waveforms, such as heart failure and myocardial infarction. Conclusion: These results suggest that the proposed method may be useful for short-term prognosis prediction using the ECG waveforms of CCU patients.

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Section: Discussionmentioning

confidence: 99%

Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms

Kondo

Teramoto

Watanabe

et al. 2023

IEEE J. Transl. Eng. Health Med.

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“…A convolutional neural network (CNN) [56] is a deep neural network with one or more convolutional layers. CNNs have shown very successful results in different kinds of applications [57] [58] [59] [60] [61] [62]; most commonly, CNNs are used in image processing where input image data is given as a 2D grid of pixels.…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables

Södergård¹,

Laakko

2023

IEEE Access

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The ability to concentrate well is an important determinant of students' learning outcomes but remains poorly understood. In this work we investigated whether there exists a mapping between students' biosignals and perceived concentration levels. If we succeed in this mapping, a wearable can function as a Concentration Tracker, a novel feature that is missing from current wearables. For this, a wearable wristband was used to record students' heart rate, heart rate variability, skin temperature, skin conductivity and acceleration from body changes. Additionally, students self-assessed their concentration levels using a smartphone application. We improved the accuracy by utilizing a big amount of unlabelled biodata from outside the study sessions. Our best boosted regression tree model predicted students' concentration level with only 1.7% NMAE error. The predictions for a user not in the training set were much weaker; the best model, a convolutional neural network, achieved a prediction NMAE error of 30.7%. This implies that the users generated biosignals highly individually. Thus, models are not well transferable from one user to another without rooting them in user-specific data. Contrary to stress research, our results showed that skin conductivity had mostly a negative correlation with students' concentration levels. Also diverging from stress reactions, skin temperature had mainly a positive correlation. Conductivity and temperature were the two dominant predictors. Further, the results suggest that an element of deep, effortless concentration was present in the learning experience of the subjects. Altogether, our work demonstrates that a concentration tracking wearable for improving learning is technically achievable.

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“…To our knowledge, convolutional neural network (CNN) works well with ECG recordings from the data acquisition IoT devices. Appropriate ECG signal processing with the CNN learns features using patient needs with abnormalities in arrhythmia and heart failure [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing

Mohana

Yakkala

Vimalnath³

et al. 2022

Journal of Healthcare Engineering

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Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.

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Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms

Cited by 25 publications

References 27 publications

Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms

Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms

Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing

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