Arrhythmia Detection Using Convolutional Neural Models

Our main goal was studying the effectiveness of transfer learning using 2D CNNs. For this task, we generated spectrograms from ECG segments that were fed to a CNN to automatically extract features. These features are classified by a MLP into arrhythmic or normal rhythm segments, achieving 90% accuracy.Nuestra meta principal consistió en estudiar la efectividad de la transferencia de aprendizaje en el uso de CNNs 2D. Para ello, generamos espectrogramas, a partir de segmentos de electrocardiogramas, que sirvieron como entrada de una CNN para extraer automáticamente sus características. Estas características son clasificadas por un MLP para discernir entre segmentos arrítmicos o normales, obteniendo una precisión del 90%

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Accelerated filtering and in situ verification for energy-optimized genome read mapping

Whole genome sequencing (WGS) includes sequencing and assembly pipelines to extract biological genomes for new advances in healthcare, agriculture and environmental research. It produces small random sections of the genome, called reads, and then re-assembled by mapping those reads to a reference genome. This process called read mapping produces a large volume of data, which are disparately processed by compute- and memory-intensive filtering and verification algorithms. As such, the problem of energy-frugal read mapping has remained an open challenge. In this paper, we propose an accelerated read mapping methodology with combined filtering and verification, implemented on an FPGA platform. Core to our methodology is an algorithm based on q-gram lemma for filtration with Myers bit-vector for verification in tandem. Through in situ verification, the proposed implementation optimizes resource utilization between filtration and verification and introduces parallel pipelines in computation and storage processes. Our experimental analysis shows that this methodology gives up to 8.7× energy efficiency when implemented on the Zynq Ultrascale+ FPGA platform, compared with the state-of-the-art software and hardware approaches

<smarttagtype namespaceuri="urn:schemas-microsoft-com:office:smarttags" name="City"><smarttagtype namespaceuri="urn:schemas-microsoft-com:office:smarttags" name="place"> Effect of ethanolic extract of root of <i style="">Pongamia pinnata</i> (L) pierre on oxidative stress, behavioral and histopathological alterations induced by cerebral ischemia–reperfusion and long-term hypoperfusion in rats </smarttagtype></smarttagtype>

868-876 Possible effect of an ethanolic root extract of Pongamia pinnata (L) Pierre (P. pinnata) on oxidant-antioxidant status and histopathological changes in acute ischemia-reperfusion injury in the rat forebrain have been investigated. Further, its effect was also assessed on long-term cerebral hypoperfusion-induced changes in anxiety, cognitive and histopathological parameters. Cerebral post-ischemic reperfusion is known to be associated with generation of free radicals. In the present study, bilateral common carotid artery occlusion (BCCAO) for 30 min followed by 45 min reperfusion produced increases in lipid peroxidation, superoxide dismutase (SOD) activity and a fall in the total tissue sulfhydryl (T-SH) levels. The ethanolic extract of roots of P. pinnata (50 mg kg-1, po for 5 days) attenuated the ischemia-reperfusion-induced increase in lipid peroxidation, SOD activity and a fall in T-SH levels. The extract also ameliorated histopathological changes and inflammatory cell infiltration in the frontoparietal region of the rat brain. The extract (50 mg kg-1, po for 15 days) was also found to alleviate the long-term hypoperfusion-induced anxiety and listlessness (open field paradigm). There was an improvement of learning and memory deficits (Morris’ water maze testing). It also attenuated reactive changes in forebrain histology like gliosis, lymphocytic infiltration, astrocytosis and cellular edema. Results suggest protective role of P. pinnata in ischemia-reperfusion injury and cerebrovascular insufficiency states. </smarttagtype