Adaptive filter is a primary method to filter
Electrocardiogram (ECG), because it does not need the signal
statistical characteristics. In this paper, an adaptive filtering
technique for denoising the ECG based on Genetic Algorithm
(GA) tuned Sign-Data Least Mean Square (SD-LMS) algorithm
is proposed. This technique minimizes the mean-squared error
between the primary input, which is a noisy ECG, and a
reference input which can be either noise that is correlated in
some way with the noise in the primary input or a signal that is
correlated only with ECG in the primary input. Noise is used as
the reference signal in this work. The algorithm was applied to
the records from the MIT -BIH Arrhythmia database for
removing the baseline wander and 60Hz power line interference.
The proposed algorithm gave an average signal to noise ratio
improvement of 10.75 dB for baseline wander and 24.26 dB for
power line interference which is better than the previous
reported works
Description:
2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
Mythili, P; Dileep, Lukose; Vijayakumari, C K; Rekha, James K(IEEE, June 4, 2013)
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Abstract:
This paper presents a new approach to the design of
combinational digital circuits with multiplexers using
Evolutionary techniques. Genetic Algorithm (GA) is used as the
optimization tool. Several circuits are synthesized with this
method and compared with two design techniques such as
standard implementation of logic functions using multiplexers
and implementation using Shannon’s decomposition technique
using GA. With the proposed method complexity of the circuit
and the associated delay can be reduced significantly
Description:
International Conference on Microelectronics, Communication and Renewable Energy (ICMiCR-2013)
Mythili, P; Pelumi, Osoba E; Eric, Michielssen(IEEE, November 17, 2010)
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Abstract:
A Multi-Objective Antenna Placement Genetic Algorithm (MO-APGA) has been proposed for the synthesis of matched antenna arrays on complex platforms. The total number of antennas required, their position on the platform, location of loads, loading circuit parameters, decoupling and matching network topology, matching network parameters and feed network parameters are optimized simultaneously. The optimization goal was to provide a given minimum gain, specific gain discrimination between the main and back lobes and broadband performance. This algorithm is developed based on the non-dominated sorting genetic algorithm (NSGA-II) and Minimum Spanning Tree (MST) technique for producing diverse solutions when the number of objectives is increased beyond two. The proposed method is validated through the design of a wideband airborne SAR
Description:
Communication Systems (ICCS), 2010 IEEE International Conference on,PP 109-112
Mythili, P; Baby, Paul; Shanavaz, K T(IEEE, January 3, 2012)
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Abstract:
In this paper an attempt has been made to determine
the number of Premature Ventricular Contraction (PVC) cycles
accurately from a given Electrocardiogram (ECG) using a
wavelet constructed from multiple Gaussian functions. It is
difficult to assess the ECGs of patients who are continuously
monitored over a long period of time. Hence the proposed
method of classification will be helpful to doctors to determine
the severity of PVC in a patient. Principal Component Analysis
(PCA) and a simple classifier have been used in addition to the
specially developed wavelet transform. The proposed wavelet has
been designed using multiple Gaussian functions which when
summed up looks similar to that of a normal ECG. The number
of Gaussians used depends on the number of peaks present in a
normal ECG. The developed wavelet satisfied all the properties
of a traditional continuous wavelet. The new wavelet was
optimized using genetic algorithm (GA). ECG records from
Massachusetts Institute of Technology-Beth Israel Hospital
(MIT-BIH) database have been used for validation. Out of the
8694 ECG cycles used for evaluation, the classification algorithm
responded with an accuracy of 97.77%. In order to compare the
performance of the new wavelet, classification was also
performed using the standard wavelets like morlet, meyer,
bior3.9, db5, db3, sym3 and haar. The new wavelet outperforms
the rest
Description:
Power, Signals, Controls and Computation (EPSCICON), 2012 International Conference on,pp 1-5