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Differential Diagnosis Of QRS Complex Tachycardia And Tachyarrhythmia In Noisy ECG Signals Through Fuzzy Neural Signal Processing Embedded System | 6076
ISSN: 2155-9872
Journal of Analytical & Bioanalytical Techniques
Open Access
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The current work reports of a Field Programmable Gate Array (FPGA) based
embedded system for detection of QRS complex and differential diagnosis
of tachycardia and tachyarrhythmia in a noisy ECG signal. The QRS complex is
the most striking waveform, caused by ventricular depolarization of the human
heart. Once the positions of the QRS complexes are found, the locations of other
components of ECG like P, T-waves and ST segment etc. are found relative to the
position of QRS, in order to analyze the complete cardiac period. In this sense,
QRS detection is prerogative to almost all automated ECG analysis algorithms. The
QRS complex has been detected after application of entropy measure of fuzziness
to build a detection function of ECG signal, which has been previously filtered to
remove power line interference and base line wander. Using the detected QRS
complexes, differential diagnosis of tachycardia and tachyarrhythmia has been
performed. The entire algorithm has been realized in hardware on an FPGA. Using
the standard CSE ECG database, the algorithm performed highly effectively. The
performance of the algorithm in respect of QRS detection with sensitivity (
Se
) of
99.74% and accuracy of 99.5% is achieved when tested using single channel ECG
with entropy criteria. Using the system, 200 patients have been diagnosed with an
accuracy of 98.5%.
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