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Human equation; Frequency (Hz) = 1/Time period

Human heartbeats 70 times per minute approximately.
Three important electrical potentials of the heart are P Wave, QRS Wave, and T
Wave. The frequency of heartbeat can be calculated and measured with hertz as
shown in the equation; Frequency (Hz) = 1/Time period (Seconds(s)    henceF=1/T  
1/500ms=1/0.5s=2Hz.2Hz is 120 beats /minute. 500 ms is the time period
between peaks of an ECG of a normal healthy person. In this paper, modified
techniques are employed with the standard algorithm and presented to the classifier
to predict the accurate result. Raw ECG signal is given as input and the signal
is smoothed by pre-processing after pre-processing the signals are passed to
feature selection, here feature selection method is compared with the modified
techniques and standard algorithms. The selected feature is presented to the
classifier for accurate classification result.

 

 

 

 

 

 

 

                                 

 

Figure2.
Proposed Methodology

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DATABASE AND METHODOLOGY 

The data are collected from MIT BIH Arrhythmia
database which contains 48   records
recorded for 30 minutes with a sampling frequency hertz of 360 Hz. 1975,
Massachusetts Institute of Technology’s laboratory Beth Israel Hospital at
Boston’s was supported to produce the MIT-BIH Arrhythmia Database. To evaluate
the Arrhythmia disease, Massachusetts Institute of Technology provided the
MIT-BIH Arrhythmia Database, which was the first available set of standard data
set for the Arrhythmia research. Since 1980 onwards this database was available
in 500 sites across the world, which paves the way to do research in finding
and diagnosing Cardiac Arrhythmia.  Among
that, 60% of datasets were collected from inpatients and 40 % of datasets were
collected from outpatients. The methodology used in our paper is divided into
the following aspects: Preprocessing, Feature selection, Training the
classifier and evaluation of the result. In feature selection, three modified
techniques are used to select the most relevant features. The modified PSO, BFO
and BAT algorithms are employed to the feature selection and the selected
relevant feature is presented to the modified SVM and ELM classifier and the
result is presented.

?

PRE-PROCESSING

The foremost objective of pre-processing is to
transform the noise signal into a reliable format for further analysis and
remove the noise present in the signal to make the data more close to relevant
data. Noise immunity is a major asset of the electronic device. The important
and the preliminary step in this ECG recording is to remove the noise
associated with the data.If the signal or input data is pre-processed the noisy
interactions of power line interface, baseline drifting, muscle contraction,
generated by the equipment will be eliminated and produced the noiseless signal
to feature selection. In this work, wavelet function is used to denoise the
signal. The input records are taken from MIT BIH database and the signals are
loaded into the Mat lab wavelet function toolbox and denoised the signal as
shown in the figure.Human heartbeats 70 times per minute approximately.
Three important electrical potentials of the heart are P Wave, QRS Wave, and T
Wave. The frequency of heartbeat can be calculated and measured with hertz as
shown in the equation; Frequency (Hz) = 1/Time period (Seconds(s)    henceF=1/T  
1/500ms=1/0.5s=2Hz.2Hz is 120 beats /minute. 500 ms is the time period
between peaks of an ECG of a normal healthy person. In this paper, modified
techniques are employed with the standard algorithm and presented to the classifier
to predict the accurate result. Raw ECG signal is given as input and the signal
is smoothed by pre-processing after pre-processing the signals are passed to
feature selection, here feature selection method is compared with the modified
techniques and standard algorithms. The selected feature is presented to the
classifier for accurate classification result.

 

 

 

 

 

 

 

                                 

 

Figure2.
Proposed Methodology

 

DATABASE AND METHODOLOGY 

The data are collected from MIT BIH Arrhythmia
database which contains 48   records
recorded for 30 minutes with a sampling frequency hertz of 360 Hz. 1975,
Massachusetts Institute of Technology’s laboratory Beth Israel Hospital at
Boston’s was supported to produce the MIT-BIH Arrhythmia Database. To evaluate
the Arrhythmia disease, Massachusetts Institute of Technology provided the
MIT-BIH Arrhythmia Database, which was the first available set of standard data
set for the Arrhythmia research. Since 1980 onwards this database was available
in 500 sites across the world, which paves the way to do research in finding
and diagnosing Cardiac Arrhythmia.  Among
that, 60% of datasets were collected from inpatients and 40 % of datasets were
collected from outpatients. The methodology used in our paper is divided into
the following aspects: Preprocessing, Feature selection, Training the
classifier and evaluation of the result. In feature selection, three modified
techniques are used to select the most relevant features. The modified PSO, BFO
and BAT algorithms are employed to the feature selection and the selected
relevant feature is presented to the modified SVM and ELM classifier and the
result is presented.

?

PRE-PROCESSING

The foremost objective of pre-processing is to
transform the noise signal into a reliable format for further analysis and
remove the noise present in the signal to make the data more close to relevant
data. Noise immunity is a major asset of the electronic device. The important
and the preliminary step in this ECG recording is to remove the noise
associated with the data.If the signal or input data is pre-processed the noisy
interactions of power line interface, baseline drifting, muscle contraction,
generated by the equipment will be eliminated and produced the noiseless signal
to feature selection. In this work, wavelet function is used to denoise the
signal. The input records are taken from MIT BIH database and the signals are
loaded into the Mat lab wavelet function toolbox and denoised the signal as
shown in the figure.

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