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Register a new userEstimation of atrial fibrillation from lead-I ECGs: Comparison with cardiologists and machine learning model (CurAlive), a clinical validation study
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Electrocardiogram recognition of cardiac arrhythmias is critical for cardiac abnormality diagnosis. Because of their strong prediction characteristics, artificial neural networks are the preferred method in medical diagnosis systems. This study presents a method to detect atrial fibrillation with lead-I ECGs using artificial intelligence. The aim of the study is to compare the accuracy of the diagnoses estimated by cardiologists and artificial intelligence over lead-I ECGs using 12-lead ECGs as references. To evaluate the performance of the proposed model, dataset were collected from China Physiological Signal Challenge 2018. In the study, diagnoses were examined in three groups as normal sinus rhythm, atrial fibrillation and OTHER. All rhythm and beat types except NSR and AFIB were labeled as OTHER super-class. OTHER contains First-degree atrioventricular blocks, Conduction disturbances, Left bundle branch block, Right bundle branch block, Premature atrial contraction, Premature ventricular contraction, ST-segment depression and ST-segment elevated type ECGs. CurAlive A.I. model which is using DenseNet as a CNN architecture and continuous wavelet transform as feature extraction method, showed a great performance on classifying ECGs from only lead-I compared to cardiologists. The AI model reached the weighted average precision, recall, F1-score and total accuracy 94.1%, 93.6%, 93.7% and 93.6% respectively, and the average of each of the three cardiologists has reached weighted average precision, recall, F1-score and total accuracy 82.2%, 54.6%, 57.5% and 54.6% respectively. This study showed that the proposed CNN model CurAlive, can be used to accurately diagnose AFIB, NSR, and OTHER rhythm using lead-I ECGs to accelerate the early detection of AFIB as a cardiologist assistant. It is also able to identify patients into different risk groups as part of remote patient monitoring systems.
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Atrial Fibrillation (AF) is a common cardiac arrhythmia affecting a large number of people around the world. If left undetected, it will develop into chronic disability or even early mortality. However, patients who have this problem can barely feel its presence, especially in its early stage. A non-invasive, automatic, and effective detection method is therefore needed to help early detection so that medical intervention can be implemented in time to prevent its progression. Electrocardiogram (ECG), which records the electrical activities of the heart, has been widely used for detecting the presence of AF. However, due to the subtle patterns of AF, the performance of detection models have largely depended on complicated data pre-processing and expertly engineered features. In our work, we developed DenseECG, an end-to-end model based on 5 layers 1D densely connected convolutional neural network. We trained our model using the publicly available dataset from 2017 PhysioNet Computing in Cardiology(CinC) Challenge containing 8528 single-lead ECG recordings of short-term heart rhythms (9-61s). Our trained model was able to outperform the other state-of-the-art AF detection models on this dataset without complicated data pre-processing and expert-supervised feature engineering.
The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a single channel short ECG segment (9-60 seconds). For this purpose, signal quality index (SQI) along with dense convolutional neural networks was used. Two convolutional neural network (CNN) models (main model that accepts 15 seconds ECG and secondary model that processes 9 seconds shorter ECG) were trained using the training data set. If the recording is determined to be of low quality by SQI, it is immediately classified as noisy. Otherwise, it is transformed to a time-frequency representation and classified with the CNN as NSR, AF, O, or noise. At the final step, a feature-based post-processing algorithm classifies the rhythm as either NSR or O in case the CNN models discrimination between the two is indeterminate. The best result achieved at the official phase of the PhysioNet/CinC challenge on the blind test set was 0.80 (F1 for NSR, AF, and O were 0.90, 0.80, and 0.70, respectively).
Training machine learning algorithms from a small and imbalanced dataset is often a daunting challenge in medical research. However, it has been shown that the synthetic data generated by data augmentation techniques can enlarge the dataset and contribute to alleviating the imbalance situation. In this study, we propose a novel generative adversarial network (GAN) architecture-Welch-GAN and focused on examining how its influence on classifier performance is related to signal quality and class imbalance within the context of photoplethysmography (PPG)-based atrial fibrillation (AF) detection. Pulse oximetry data were collected from 126 adult patients and augmented using the permutation technique to build a large training set for training an AF detection model based on a one-dimensional residual neural network. To test the model, PPG data were collected from 13 stroke patients and utilized. Four data augmentation methods, including both traditional and GANs, are leveraged as baseline in this study. Three different experiments are designed to investigate each data augmentation methods from the aspect of performance gain, robustness to motion artifact and training sample size, respectively. Compared to the un-augmented data, by training the same AF classification algorithm using augmented data, the AF detection accuracy was significantly improved from 80.36% to over 90% with no compromise on sensitivity nor on negative predicted value. Within each data augmentation techniques, Welch-GAN has shown around 3% superiority in terms of AF detection accuracy compared to the baseline methods, which suggests the state-of-the-art of our proposed Welch-GAN.
Atrial Fibrillation (AF) is an abnormal heart rhythm which can trigger cardiac arrest and sudden death. Nevertheless, its interpretation is mostly done by medical experts due to high error rates of computerized interpretation. One study found that only about 66% of AF were correctly recognized from noisy ECGs. This is in part due to insufficient training data, class skewness, as well as semantical ambiguities caused by noisy segments in an ECG record. In this paper, we propose a K-margin-based Residual-Convolution-Recurrent neural network (K-margin-based RCR-net) for AF detection from noisy ECGs. In detail, a skewness-driven dynamic augmentation method is employed to handle the problems of data inadequacy and class imbalance. A novel RCR-net is proposed to automatically extract both long-term rhythm-level and local heartbeat-level characters. Finally, we present a K-margin-based diagnosis model to automatically focus on the most important parts of an ECG record and handle noise by naturally exploiting expected consistency among the segments associated for each record. The experimental results demonstrate that the proposed method with 0.8125 F1NAOP score outperforms all state-of-the-art deep learning methods for AF detection task by 6.8%.
Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM). However, clinical implementation is limited by lack of parameters standardization. We aimed to compare nine machine learning classifiers, with different optimization parameters, to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients, based on radiomic features from conventional and advanced MR. 156 adult patients with pathologic diagnosis of GBM were included. Three tumoral regions were analyzed: contrast-enhancing tumor, necrosis and non-enhancing tumor, selected by manual segmentation. Radiomic features were extracted with a custom version of Pyradiomics, and selected through Boruta algorithm. A Grid Search algorithm was applied when computing 4 times K-fold cross validation (K=10) to get the highest mean and lowest spread of accuracy. Once optimal parameters were identified, model performances were assessed in terms of Area Under The Curve-Receiver Operating Characteristics (AUC-ROC). Metaheuristic and ensemble classifiers showed the best performance across tasks. xGB obtained maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,6%). Best performing features shed light on possible correlations between MR and tumor histology.
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