Predisposition to arrhythmia and autonomic dysfunction in Nhlh1-deficient mice.

Nhlh1 is a basic helix-loop-helix transcription factor whose expression is restricted to the nervous system and which may play a role in neuronal differentiation. To directly study Nhlh1 function, we generated null mice. Homozygous mutant mice were predisposed to premature, adult-onset, unexpected death. Electrocardiograms revealed decreased total heart rate variability, stress-induced arrhythmia, and impaired baroreceptor sensitivity. This predisposition to arrhythmia is a likely cause of the observed death in the mutant mice. Heterozygosity for the closely related transcription factor Nhlh2 increased the severity of the Nhlh1-null phenotype. No signs of primary cardiac structural or conduction abnormalities could be detected upon necropsy of the null mice. The pattern of altered heart rhythm observed in basal and experimental conditions (stress and pharmacologically induced) suggests that a deficient parasympathetic tone may contribute to the arrhythmia in the Nhlh1-null mouse. The expression of Nhlh1 in the developing brain stem and in the vagal nuclei in the wild-type mouse further supports this hypothesis. The Nhlh1 mutant mouse may thus provide a model to investigate the contribution of the autonomic nervous system to arrhythmogenesis.

Inexact-aware architecture design for ultra-low power bio-signal analysis

This study introduces an inexact, but ultra-low power, computing architecture devoted to the embedded analysis of bio-signals. The platform operates at extremely low voltage supply levels to minimise energy consumption. In this scenario, the reliability of static RAM (SRAM) memories cannot be guaranteed when using conventional 6-transistor implementations. While error correction codes and dedicated SRAM implementations can ensure correct operations in this near-threshold regime, they incur in significant area and energy overheads, and should therefore be employed judiciously. Herein, the authors propose a novel scheme to design inexact computing architectures that selectively protects memory regions based on their significance, i.e. their impact on the end-to-end quality of service, as dictated by the bio-signal application characteristics. The authors illustrate their scheme on an industrial benchmark application performing the power spectrum analysis of electrocardiograms. Experimental evidence showcases that a significance-based memory protection approach leads to a small degradation in the output quality with respect to an exact implementation, while resulting in substantial energy gains, both in the memory and the processing subsystem.