The influence of atrial flutter in automated detection of atrial arrhythmias - are we ready to go into clinical practice?"

OBJECTIVE: To investigate the impact of atrial flutter (Afl) in the atrial arrhythmias classification task. We additionally advocate the use of a subject-based split for future studies in the field in order to avoid within-subject correlation which may lead to over-optimistic inferences. Finally, we demonstrate the effectiveness of the classifiers outside of the initially studied circumstances, by performing an inter-dataset model evaluation of the classifiers in data from different sources. METHODS: ECG signals of two private and three public (two MIT-BIH and Chapman ecgdb) databases were preprocessed and divided into 10s segments which were then subject to feature extraction. The created datasets were divided into a training and test set in two ways, based on a random split and a patient split. Classification was performed using the XGBoost classifier, as well as two benchmark classification models using both data splits. The trained models were then used to make predictions on the test data of the remaining datasets. RESULTS: The XGBoost model yielded the best performance across all datasets compared to the remaining benchmark models, however variability in model performance was seen across datasets, with accuracy ranging from 70.6% to 89.4%, sensitivity ranging from 61.4% to 76.8%, and specificity ranging from 87.3% to 95.5%. When comparing the results between the patient and the random split, no significant difference was seen in the two private datasets and the Chapman dataset, where the number of samples per patient is low. Nonetheless, in the MIT-BIH dataset, where the average number of samples per patient is approximately 1300, a noticeable disparity was identified. The accuracy, sensitivity, and specificity of the random split in this dataset of 93.6%, 86.4%, and 95.9% respectively, were decreased to 88%, 61.4%, and 89.8% in the patient split, with the largest drop being in Afl sensitivity, from 71% to 5.4%. The inter-dataset scores were also significantly lower than their intra-dataset counterparts across all datasets. CONCLUSIONS: CAD systems have great potential in the assistance of physicians in reliable, precise and efficient detection of arrhythmias. However, although compelling research has been done in the field, yielding models with excellent performances on their datasets, we show that these results may be over-optimistic. In our study, we give insight into the difficulty of detection of Afl on several datasets and show the need for a higher representation of Afl in public datasets. Furthermore, we show the necessity of a more structured evaluation of model performance through the use of a patient-based split and inter-dataset testing scheme to avoid the problem of within-subject correlation which may lead to misleadingly high scores. Finally, we stress the need for the creation and use of datasets with a higher number of patients and a more balanced representation of classes if we are to progress in this mission.

ECG waveform dataset for predicting defibrillation outcome in out-of-hospital cardiac arrested patients.

The provided database of 260 ECG signals was collected from patients with out-of-hospital cardiac arrest while treated by the emergency medical services. Each ECG signal contains a 9 second waveform showing ventricular fibrillation, followed by 1 min of post-shock waveform. Patients' ECGs are made available in multiple formats. All ECGs recorded during the prehospital treatment are provided in PFD files, after being anonymized, printed in paper, and scanned. For each ECG, the dataset also includes the whole digitized waveform (9 s pre- and 1 min post-shock each) and numerous features in temporal and frequency domain extracted from the 9 s episode immediately prior to the first defibrillation shock. Based on the shock outcome, each ECG file has been annotated by three expert cardiologists, - using majority decision -, as successful (56 cases), unsuccessful (195 cases), or indeterminable (9 cases). The code for preprocessing, for feature extraction, and for limiting the investigation to different temporal intervals before the shock is also provided. These data could be reused to design algorithms to predict shock outcome based on ventricular fibrillation analysis, with the goal to optimize the defibrillation strategy (immediate defibrillation versus cardiopulmonary resuscitation and/or drug administration) for enhancing resuscitation.