Multi-label classification of reduced-lead ECGs using an interpretable deep convolutional neural network.

We propose a model that can perform multi-label classification on 26 cardiac abnormalities from reduced lead Electrocardiograms (ECGs) and interpret the model.PhysioNet/computing in cardiology (CinC) challenge 2021 datasets are used to train the model. All recordings shorter than 20 s are preprocessed by normalizing, resampling, and zero-padding.

WaveCSP: a robust motor imagery classifier for consumer EEG devices.

There is an increasing demand for reliable motor imagery (MI) classification algorithms for applications in consumer level brain-computer interfacing (BCI). For the practical use, such algorithms must be robust to both device limitations and subject variability, which make MI classification a challenging task. This study proposes methods to study the effect of limitations including a limited number of electrodes, limited spatial distribution of electrodes, lower signal quality, subject variabilities and BCI literacy, on the performance of MI classification.

Detecting atrial fibrillation from short single lead ECGs using statistical and morphological features.

Objective: Point of care ECG devices can improve the early detection of atrial fibrillation (AF). The efficiency of such devices depends on the capability of automatic AF detection against normal sinus rhythm and other arrhythmias from a short single lead ECG record in the presence of noise and artifacts. The objective of this study was to develop an algorithm that classifies a short single lead ECG record into 'Normal', 'AF', 'Other' and 'Noisy' classes, and identify the challenges in developing such algorithms and potential mitigation steps.