Abstract: In this paper, an automatic detecting algorithm for
QRS complex detecting was applied for analyzing ECG recordings
and five criteria for dangerous arrhythmia diagnosing are applied for a
protocol type of automatic arrhythmia diagnosing system. The
automatic detecting algorithm applied in this paper detected the
distribution of QRS complexes in ECG recordings and related
information, such as heart rate and RR interval. In this investigation,
twenty sampled ECG recordings of patients with different pathologic
conditions were collected for off-line analysis. A combinative
application of four digital filters for bettering ECG signals and
promoting detecting rate for QRS complex was proposed as
pre-processing. Both of hardware filters and digital filters were
applied to eliminate different types of noises mixed with ECG
recordings. Then, an automatic detecting algorithm of QRS complex
was applied for verifying the distribution of QRS complex. Finally,
the quantitative clinic criteria for diagnosing arrhythmia were
programmed in a practical application for automatic arrhythmia
diagnosing as a post-processor. The results of diagnoses by automatic
dangerous arrhythmia diagnosing were compared with the results of
off-line diagnoses by experienced clinic physicians. The results of
comparison showed the application of automatic dangerous
arrhythmia diagnosis performed a matching rate of 95% compared
with an experienced physician-s diagnoses.
Abstract: This paper presents the work of signal discrimination
specifically for Electrocardiogram (ECG) waveform. ECG signal is
comprised of P, QRS, and T waves in each normal heart beat to
describe the pattern of heart rhythms corresponds to a specific
individual. Further medical diagnosis could be done to determine any
heart related disease using ECG information. The emphasis on QRS
Complex classification is further discussed to illustrate the
importance of it. Pan-Tompkins Algorithm, a widely known
technique has been adapted to realize the QRS Complex
classification process. There are eight steps involved namely
sampling, normalization, low pass filter, high pass filter (build a band
pass filter), derivation, squaring, averaging and lastly is the QRS
detection. The simulation results obtained is represented in a
Graphical User Interface (GUI) developed using MATLAB.