Atrial Fibrillation Detection Using Deep Features and Convolutional Networks

Atrial fibrillation is a cardiac arrhythmia that affects an estimated 33.5 million people globally and is the potential cause of 1 in 3 strokes in people over the age of 60. Detection and diagnosis of atrial fibrillation (AFIB) is done noninvasively in the clinical environment through the evaluation of electrocardiograms (ECGs). Early research into automated methods for the detection of AFIB in ECG signals focused on traditional bio-medical signal analysis to extract important features for use in statistical classification models. Artificial intelligence models have more recently been used that employ convolutional and/or recurrent network architectures. In this work, significant time and frequency domain characteristics of the ECG signal are extracted by applying the short-time Fourier trans-form and then visually representing the information in a spectrogram. Two different classification approaches were investigated that utilized deep features in the spectrograms construct-ed from ECG segments. The first approach used a pretrained DenseNet model to extract features that were then classified using Support Vector Machines, and the second approach used the spectrograms as direct input into a convolutional network. Both approaches were evaluated against the MIT-BIH AFIB dataset, where the convolutional network approach achieved a classification accuracy of 93.16%. While these results do not surpass established automated atrial fibrillation detection methods, they are promising and warrant further investigation given they did not require any noise prefiltering, hand-crafted features, nor a reliance on beat detection.

The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentation

Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined. There have been a lot of promising recent works utilizing deep network architectures, such as FCNs, U-Nets, and ResNets, for developing automated skin lesion segmentation. This paper investigates various pre- and post-processing techniques for improving the performance of U-Nets as measured by the Jaccard Index. The dataset provided as part of the "2017 ISBI Challenges on Skin Lesion Analysis Towards Melanoma Detection" was used for this evaluation and the performance of the finalist competitors was the standard for comparison. The pre-processing techniques employed in the proposed system included contrast enhancement, artifact removal, and vignette correction. More advanced image transformations, such as local binary patterns and wavelet decomposition, were also employed to augment the raw grayscale images used as network input features. While the performance of the proposed system fell short of the winners of the challenge, it was determined that using wavelet decomposition as an early transformation step improved the overall performance of the system over pre- and post-processing steps alone.