Conference Presentations by Mayank Kumar Gautam
IEEE- ICETECH 2016
—ECG is basically the graphical representation of the electrical activity of cardiac muscles duri... more —ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiological parameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG for the use in specially designed instruments like ECG monitors, Holter tape recorders and scanners, ambulatory ECG recorders and analyzers. In this paper the study of the concept of pattern recognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes of predefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generated waveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters such as spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.
IEEE ICETEESES-2016
ECG plays a vital role in the analysis of various heart diseases as the shape of the ECG waveform... more ECG plays a vital role in the analysis of various heart diseases as the shape of the ECG waveform consist of vital information about heart conditions such as its electrical conduction or muscle activity. Inspite of the conventional method the extraction of ECG features is of major significance and benefit for the diagnosis of numerous harmful or even critical cardiac diseases. The feature extraction plays a vital role in diagnosis of the various cardiac diseases. Each cycle of an ECG signal contains of the P-QRS-T waves. This scheme of feature extraction describes and provides the amplitudes and intervals in the ECG signal for further investigation. The amplitudes and intervals value of P-QRS-T segment shows the operation of heart. Recently, various techniques have been evolved for analysis of the ECG signal. This paper discusses three most widely used methods used to extract the different features of Electrocardiograph (ECG) signals namely Wavelet Transform (WT), Fast Fourier Transform (FFT), Independent Component Analysis (ICA). The study conveys the information that the Fast Fourier Transform method gives better performance in frequency domain for the ECG feature extraction. Accuracy of Wavelet Transform is 92.20%, of the Fast Fourier Transform is 92.47%, and of the Independent Component Analysis is 90.13%. It has been observed that FFT shows better performance regarding the ECG signal analysis. Moreover, provides efficient estimation of the PSD from noise corrupted signals. But the limitation of this method is the leakage decreases the ability of FFT to resolve two frequencies of close space. But by the use of a window function will reduce this leakage.
Papers by Mayank Kumar Gautam
ECG is basically the graphical representation of the electrical activity of cardiac muscles durin... more ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiological parameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of pattern recognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes of predefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generated waveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters such as spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease. 1. Introduction Electrocardiography gives information of the electrical activity of the cardiac muscles. Bio-signals which are usually non-stationary signals may occur randomly in the timescale. Hence, for the effective diagnosis, the ECG signal pattern and heart rate variability should be observed over several hours. Because of the volume of the data being enormous due to long time recording, the analysis of it is tedious and also time consuming. Therefore, automatic computer-based examination and classification of cardiac diseases can be very helpful in diagnostic [1]. The frequency range of an ECG signal lies in between 0.05–100 Hz and its magnitude lies in the range of 1–10 mV. It is been characterized by five peaks and valleys labeled as P, Q, R, S and T as shown in Fig 1. The performance of any automatic ECG analyzer depends majorly on the accurate and reliable detection of the QRS segmentation part, as well as T and P waves. The detection of the QRS segmentation part is the crucial task in automatic ECG signal analysis. Because, once the QRS segmentation part has been acknowledged a more comprehensive assessment of ECG signal can be performed that includes the heart rate, the ST segment etc. The normal beats have the P-R interval usually in the range of 0.12-0.2 seconds whereas the QRS interval lies in the range of 0.04-0.12 seconds. The division of ECG is basically in two phases as depolarization of the cardiac muscles and repolarisation of the cardiac muscles. The depolarization phases include the P wave i.e, atrial depolarization and QRS-wave i.e, ventricles depolarization. The repolarisation phases include the T-wave and U-wave i.e, ventricular repolarisation [2-6]. Malfunction in the signaling in the myocardium results in the heart to pump blood less effectively and deteriorates proper conduction process of the heart [4]. Hence, the early detection of arrhythmias is very helpful for living a durable and reliable life as well as improves early detection of arrhythmias. Generally, the standard ECG signals are categorized into three different groups and shown in Figure 1. a. Waves – deviations from the isoelectric line i.e, the baseline voltage. They are named successively: P, Q, R, S, T, U. b. Segments-isoelectric lines time duration between waves. c. Intervals-time duration which include segments and waves.
ECG plays a vital role in the analysis of various heart diseases as the shape of the ECG waveform... more ECG plays a vital role in the analysis of various heart diseases as the shape of the ECG waveform consist of vital information about heart conditions such as its electrical conduction or muscle activity. Inspite of the conventional method the extraction of ECG features is of major significance and benefit for the diagnosis of numerous harmful or even critical cardiac diseases. The feature extraction plays a vital role in diagnosis of the various cardiac diseases. Each cycle of an ECG signal contains of the P-QRS-T waves. This scheme of feature extraction describes and provides the amplitudes and intervals in the ECG signal for further investigation. The amplitudes and intervals value of P-QRS-T segment shows the operation of heart. Recently, various techniques have been evolved for analysis of the ECG signal. This paper discusses three most widely used methods used to extract the different features of Electrocardiograph (ECG) signals namely Wavelet Transform (WT), Fast Fourier Transform (FFT), Independent Component Analysis (ICA). The study conveys the information that the Fast Fourier Transform method gives better performance in frequency domain for the ECG feature extraction. Accuracy of Wavelet Transform is 92.20%, of the Fast Fourier Transform is 92.47%, and of the Independent Component Analysis is 90.13%. It has been observed that FFT shows better performance regarding the ECG signal analysis. Moreover, provides efficient estimation of the PSD from noise corrupted signals. But the limitation of this method is the leakage decreases the ability of FFT to resolve two frequencies of close space. But by the use of a window function will reduce this leakage.
—The current paper shows the modeling, controller design and simulation of Permanent Magnet Synch... more —The current paper shows the modeling, controller design and simulation of Permanent Magnet Synchronous Motor (PMSM) drive. As the paper deals with use of neural network methods to implement control of PMSM hence two control methods are used-For the inner loop current control the Hysteresis current controller is used and for the purpose of outer loop speed control the PI controller is used. The conventional Proportional-Integral (PI) controller is used in industries because of its robustness but when the dynamics of system changes with time or with certain operating conditions, the PID controller can't be used. The solution that alters this situation is to use Artificial Neural Network (ANN) which provides the field-oriented control scheme of the PMSM drive. The simulation results show the validity of ANN based PMSM drive to ensure robustness towards load and parameter variations. Keywords— Artificial Neural Network (ANN), Permanent Synchronous Motor (PMSM), Vector Control, PI Controller, Hysteresis Current Controller.
I-MANAGER JOURNAL OF DIGITAL SIGNAL PROCESSING, Apr 28, 2016
ECG is basically the graphical representation of the electrical activity of cardiac muscles durin... more ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG for the use in specially designed instruments like ECG monitors, Holter tape recorders and scanners, ambulatory ECG recorders and analyzers. In this paper the study of the concept of pattern recognition of ECG is done. The ECG signal generated waveform gives almost all information about activity of the heart. The feature extraction of ECG is by Wavelet transform. This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.
I-MANAGER JOURNAL OF DIGITAL SIGNAL PROCESSING
The real wellspring of human misfortune in Cardiovascular Diseases (CVD) is Cardiac issues that a... more The real wellspring of human misfortune in Cardiovascular Diseases (CVD) is Cardiac issues that are expanding step-bystep
in the world. Incredible exertion is done to analyze the cardiovascular disease, where numerous individuals are
utilized to the diverse sort of portable Electrocardiogram (ECG) using remote observing method. ECG Feature Extraction
act as a critical part in diagnosing generally of the heart sicknesses. Presently a complete inspection has been done for
highlighting the extraction of ECG sign dissecting, and extricating and finally characterizing have been arranged amid
the long-prior time, and here the authors have presented delicate processing procedures. To perceive the current
circumstance of the heart, Electrocardiography is a fundamental device however it is a period expending procedure to
break down a persistent ECG signal as it might hold a huge number of relentless heart pulsates. Right now a simple sign
can be converted in to a computerized one which helps in precisely diagnosing the sign. Point of this paper is to show an
identification of some warmth arrhythmias utilizing the emerging neuro-wavelet approach.
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Conference Presentations by Mayank Kumar Gautam
Papers by Mayank Kumar Gautam
in the world. Incredible exertion is done to analyze the cardiovascular disease, where numerous individuals are
utilized to the diverse sort of portable Electrocardiogram (ECG) using remote observing method. ECG Feature Extraction
act as a critical part in diagnosing generally of the heart sicknesses. Presently a complete inspection has been done for
highlighting the extraction of ECG sign dissecting, and extricating and finally characterizing have been arranged amid
the long-prior time, and here the authors have presented delicate processing procedures. To perceive the current
circumstance of the heart, Electrocardiography is a fundamental device however it is a period expending procedure to
break down a persistent ECG signal as it might hold a huge number of relentless heart pulsates. Right now a simple sign
can be converted in to a computerized one which helps in precisely diagnosing the sign. Point of this paper is to show an
identification of some warmth arrhythmias utilizing the emerging neuro-wavelet approach.