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Personne# Aviinaash Sankaranarayanan

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Andreas Peter Burg, Georgios Karakonstantis, Aviinaash Sankaranarayanan

In this paper, a low complexity system for spectral analysis of heart rate variability (HRV) is presented. The main idea of the proposed approach is the implementation of the Fast-Lomb periodogram that is a ubiquitous tool in spectral analysis, using a wavelet based Fast Fourier transform. Interestingly we show that the proposed approach enables the classification of processed data into more and less significant based on their contribution to output quality. Based on such a classification a percentage of less-significant data is being pruned leading to a significant reduction of algorithmic complexity with minimal quality degradation. Indeed, our results indicate that the proposed system can achieve up-to 43% reduction in number of computations with only 4.9% average error in the output quality compared to a conventional FFT based HRV system.

2012, , , ,

A method and device for reducing the computational complexity of a processing algorithm, of a discrete signal, in particular of the spectral estimation and analysis of bio-signals, with minimum or no quality loss, which comprises steps of (a) choosing a domain, such that transforming the signal to the chosen domain results to an approximately sparse representation, wherein at least part of the output data vector has zero or low magnitude elements; (b) converting the original signal in the domain chosen in step (a) through a mathematical transform consisting of arithmetic operations resulting in a vector of output data; (c) reformulating the processing algorithm of the original signal in the original domain into a modified algorithm consisting of equivalent arithmetic operations in the domain chosen in step (a) to yield the expected result with the expected quality quantified in terms of a suitable application metric; (d) combining the mathematical transform of step (b) and the equivalent mathematical operations introduced in step (c) for obtaining the expected result within the original domain with the expected quality; (e) selecting a threshold value based on the difference in the mean magnitude value of the elements of the output data vector of the transform said in step (b) and the preferred complexity reduction and degree of output quality loss that can be tolerated in the expected result within the target application; (f) pruning a number of elements the magnitude of which is less than the threshold value selected in step (e); and/or eliminating arithmetic operations associated with the pruned elements of step (f) either in the mathematical transform of step (b) and/or in the equivalent algorithm of step (c).

2015Mohamed Mostafa Sabry Aly, David Atienza Alonso, Andreas Peter Burg, Georgios Karakonstantis, Aviinaash Sankaranarayanan

Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case.