LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network

An electrocardiogram (ECG) monitors the electrical activity generated by the heart and is used to detect fatal cardiovascular diseases (CVDs). Conventionally, to capture the precise electrical activity, clinical experts use multiple-lead ECGs (typically 12 leads). But in recent times, large-size deep learning models have been used to detect these diseases. However, such models require heavy compute resources like huge memory and long inference time. To alleviate these shortcomings, we propose a low-parameter model, named Low Resource Heart-Network (LRH-Net), which uses fewer leads to detect ECG anomalies in a resource-constrained environment. A multi-level knowledge distillation process is used on top of that to get better generalization performance on our proposed model. The multi-level knowledge distillation process distills the knowledge to LRH-Net trained on a reduced number of leads from higher parameter (teacher) models trained on multiple leads to reduce the performance gap. The proposed model is evaluated on the PhysioNet-2020 challenge dataset with constrained input. The parameters of the LRH-Net are 106x less than our teacher model for detecting CVDs. The performance of the LRH-Net was scaled up to 3.2% and the inference time scaled down by 75% compared to the teacher model. In contrast to the compute- and parameter-intensive deep learning techniques, the proposed methodology uses a subset of ECG leads using the low resource LRH-Net, making it eminently suitable for deployment on edge devices

The complete mitochondrial genome of the Atlantic spiny lumpsucker Eumicrotremus spinosus (Fabricius, 1776)

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CHARACTERISATION OF TETRA AMELIA SYNDROME BY SNP BASED ON COMPUTATIONAL GENOTYPING ANALYSIS

  Objective: Tetra Amelia syndrome is a congenital disorder, which is mainly caused by the presence of mutation in the WNT3 region. Being an embryonic developmental disorder, earlier warning of its onset can be predicted by subjecting there single nucleotide polymorphisms (SNPs) to further studies.Methods: For the identification of the list of mutant WNT3 proteins with the specified (G83X) position, we predicted it through reverse genetics method. This region was further used to determine the SNPs involved in them using the Chi-square test. Finally, we have validated the existence of these SNPs in the WNT3 gene by multifactor dimensionality reduction analysis.Results: In Tetra Amelia syndrome, we determined that among the six frames of WNT3 gene, only the 2nd frame has more identity with WNT3 protein (98%). The mutant amino acid residue was found only at the 83rd position (G83X). Sequence analysis techniques helped to determine 16 SNPs: rs147030694, rs9908452, rs1062339, rs193268056, rs190245126, rs185051815, rs71375364, rs188212517, rs185848450, rs151309556, rs148810526, rs142400306, rs118086135, rs77768380, rs75398055, and rs34608985. These SNPs where validated further and this lead to 3 SNP s, which can be used to genotype Tetra Amelia syndrome.Conclusion: The present studies of genotyping Tetra Amelia syndrome can help determine congenital disease at earlier stages itself. In future, larger dataset is needed and as well similar methodology can be used on late onset diseases (like Parkinson's) can also be predicted by subjecting there SNPs genotype.Keywords: Tetra Amelia syndrome, WNT3, Hap map, Multifactor dimensionality reduction, Single, Nucleotide polymorphism