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We propose a novel pipeline for the real-time detection of myocardial infarction from a single heartbeat of a 12-lead electrocardiograms. We do so by merging a real-time R-spike detection algorithm with a deep learning Long-Short Term Memory (LSTM) network-based classifier. A comparative assessment of the classification performance of the resulting system is performed and provided. The proposed algorithm achieves an inter-patient classification accuracy of 95.76% (with a 95% Confidence Interval (CI) of ±2.4%), a recall of 96.67% (±2.4% 95% CI), specificity of 93.64% (±5.7% 95% CI), and the average J-Score is 90.31% (±6.2% 95% CI). These state-of-the-art myocardial infarction detection metrics are extremely promising and could pave the wave for the early detection of myocardial infarctions. This high accuracy is achieved with a processing time of 40 milliseconds, which is most appropriate for online classification as the time between fast heartbeats is around 300 milliseconds.

This study introduces a new multimodal deep regression method to predict cognitive test score in a 5-year longitudinal study on Alzheimer’s disease (AD). The proposed model takes advantage of multimodal data that includes cerebrospinal fluid (CSF) levels of tau and beta-amyloid, structural measures from magnetic resonance imaging (MRI), functional and metabolic measures from positron emission tomography (PET), and cognitive scores from neuropsychological tests (Cog), all with the aim of achieving highly accurate predictions of future Mini-Mental State Examination (MMSE) test scores up to five years after baseline biomarker collection. A novel data augmentation technique is leveraged to increase the numbers of training samples without relying on synthetic data. With the proposed method, the best and most encompassing regressor is shown to achieve better than state-of-the-art correlations of 85.07%(SD=1.59) for 6 months in the future, 87.39% (SD =1.48) for 12 months, 84.78% (SD=2.66) for 18 months, 85.13% (SD=2.19) for 24 months, 81.15% (SD=5.48) for 30 months, 81.17% (SD=4.44) for 36 months, 79.25% (SD=5.85) for 42 months, 78.98% (SD=5.79) for 48 months, 78.93%(SD=5.76) for 54 months, and 74.96% (SD=7.54) for 60 months.

This paper introduces a new image smoothing filter based on a feed-forward convolutional neural network (CNN) in presence of impulse noise. This smoothing filter integrates a very deep architecture, a regularization method, and a batch normalization process. This fully integrated approach yields an effectively denoised and smoothed image yielding a high similarity measure with the original noise free image. Specific structural metrics are used to assess the denoising process and how effective was the removal of the impulse noise. This CNN model can also deal with other noise levels not seen during the training phase. The proposed CNN model is constructed through a 20-layer network using 400 images from the Berkeley Segmentation Dataset (BSD) in the training phase. Results are obtained using the standard testing set of 8 natural images not seen in the training phase. The merits of this proposed method are weighed in terms of high similarity measure and structural metrics that conform to the original image and compare favorably to the different results obtained using state-of-art denoising filters.

Predicting future cognitive status from current and past scores on objective cognitive tests and imaging measures would be useful in diagnosing Alzheimer's disease (AD) and to assess the progression of the disease. We used stochastic gradient boosting of decision trees on over 1,141 individuals whose clinical and imaging studies were available from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. The proposed method outperformed all the algorithms tested in all five cognitive scores (MMSE, CDRS, RAVLT, ADAS11 and ADAS13), outranking all other state-of-the-art algorithms in terms of both Pearson's correlation coefficient and root mean square error. All correlation measures between predicted and actual cognitive scores were higher than 0.9. Given the large number of subjects included in this study, all correlations were statistically significant. For the subset of MCI patients, we compared the proposed method with state of the art algorithms. Here, the proposed method outperformed all the algorithms tested in all five cognitive scores.

Electrocardiograms (EKGs) are the most common diagnosis tools used for the detection and diagnosis of cardiovascular diseases and abnormalities. In this paper, we proposed a method that uses independent component analysis (ICA) for the real-time detection of the most distinct component of the cardiac electrical signal, the R-peak. This approach will open the door to real-time analysis and decomposition of the complete cardiac signal and the online diagnosis of cardiac abnormalities. The potential benefits of such real-time implementation are far reaching, from the online diagnosis of diseases and abnormalities to its use in tracking heart functioning during the testing and development of cutting edge research and treatments, such as transcranial magnetic stimulation (TMS).