Browsing by Author "Shaik, B.S."

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A Method for QRS Delineation Based on STFT Using Adaptive Threshold
(Elsevier, 2015) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Chandrasheker, T.; Narasimhadhan, A.V.
Electrocardiogram (ECG) is the electrical manifestation of the contractile activity of the heart. In this work, it is proposed to utilize an adaptive threshold technique on spectrogram computed using Short Time Fourier Transform (STFT) for QRS complex detection in electrocardiogram (ECG) signal. The algorithm consists of preprocessing the raw ECG signal to remove the power-line interference, computing the STFT, applying adaptive thresholding technique and followed by identifying QRS peaks. Sensitivity, Specificity and Detection error rate are calculated on MIT-BIH database using the proposed method, which yields a competitive results when compared with the state of art in QRS detection. Â© 2015 The Authors.
A novel approach for QRS delineation in ECG signal based on chirplet transform
(Institute of Electrical and Electronics Engineers Inc., 2016) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Narasimhadhan, A.V.
ECG analysis is used significantly in diagnosis, and biometrics. QRS complex detection is an important step in any application involving ECG signal. In this work, a novel approach for QRS complex detection based on chirplet transform is proposed. The QRS detection algorithm proposed in this work mainly consists of four steps. A preprocessing step to remove power line interference, computation of chirplet transform, an adaptive threshold technique for detecting possible QRS complex peaks, and followed by a decision making step. The performance of proposed algorithm for QRS complex detection is evaluated on MIT-BIH database and compared with the results of different algorithms in the state of art. The performance of the algorithm is comparable with the state of art of QRS complex detection. Â© 2015 IEEE.
A Method for QRS Delineation Based on STFT Using Adaptive Threshold
(2015) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Chandrasheker, T.; Narasimhadhan, A.V.
Electrocardiogram (ECG) is the electrical manifestation of the contractile activity of the heart. In this work, it is proposed to utilize an adaptive threshold technique on spectrogram computed using Short Time Fourier Transform (STFT) for QRS complex detection in electrocardiogram (ECG) signal. The algorithm consists of preprocessing the raw ECG signal to remove the power-line interference, computing the STFT, applying adaptive thresholding technique and followed by identifying QRS peaks. Sensitivity, Specificity and Detection error rate are calculated on MIT-BIH database using the proposed method, which yields a competitive results when compared with the state of art in QRS detection. � 2015 The Authors.
A novel approach for QRS delineation in ECG signal based on chirplet transform
(2016) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Narasimhadhan, A.V.
ECG analysis is used significantly in diagnosis, and biometrics. QRS complex detection is an important step in any application involving ECG signal. In this work, a novel approach for QRS complex detection based on chirplet transform is proposed. The QRS detection algorithm proposed in this work mainly consists of four steps. A preprocessing step to remove power line interference, computation of chirplet transform, an adaptive threshold technique for detecting possible QRS complex peaks, and followed by a decision making step. The performance of proposed algorithm for QRS complex detection is evaluated on MIT-BIH database and compared with the results of different algorithms in the state of art. The performance of the algorithm is comparable with the state of art of QRS complex detection. � 2015 IEEE.
R peak delineation in ECG signal based on polynomial chirplet transform using adaptive threshold
(2016) Naganjaneyulu, G.V.S.S.K.R.; Shaik, B.S.; Narasimhadhan, A.V.
R peak delineation is fundamental step in any application implicating electrocardiogram (ECG) signal. ECG is non stationary and non linear. Hence, linear transforms like short time fourier transform, wavelet transform and chirplet transform may be inadequate to represent ECG signal and consequently for R peak delineation. Polynomial chirplet transform (PCT) models the frequency into a higher order polynomial to enhance the representation of non stationary signals whose frequency vary non linearly with time. In this paper, PCT based R peak delineation method using adaptive threshold is proposed. The performance of the proposed algorithm is evaluated on ECG ID data base taken from physionet data bank. This work also presents a comparative study of QRS detection methods employing the uni scale family of time frequency analysis methods, short time fourier transform, chirplet transform, stockwell transform, wigner ville distribution, and pseudo wigner ville distribution out of which stockwell transform, pseudo wigner ville distribution along with adaptive threshold are applied to QRS detection for the first time. The results show that the proposed method outperforms the competitors in terms of sensitivity, specificity and detection error rate. � 2016 IEEE.
R peak delineation in ECG signal based on polynomial chirplet transform using adaptive threshold
(Institute of Electrical and Electronics Engineers Inc., 2016) Naganjaneyulu, G.V.S.S.K.R.; Shaik, B.S.; Narasimhadhan, A.V.
R peak delineation is fundamental step in any application implicating electrocardiogram (ECG) signal. ECG is non stationary and non linear. Hence, linear transforms like short time fourier transform, wavelet transform and chirplet transform may be inadequate to represent ECG signal and consequently for R peak delineation. Polynomial chirplet transform (PCT) models the frequency into a higher order polynomial to enhance the representation of non stationary signals whose frequency vary non linearly with time. In this paper, PCT based R peak delineation method using adaptive threshold is proposed. The performance of the proposed algorithm is evaluated on ECG ID data base taken from physionet data bank. This work also presents a comparative study of QRS detection methods employing the uni scale family of time frequency analysis methods, short time fourier transform, chirplet transform, stockwell transform, wigner ville distribution, and pseudo wigner ville distribution out of which stockwell transform, pseudo wigner ville distribution along with adaptive threshold are applied to QRS detection for the first time. The results show that the proposed method outperforms the competitors in terms of sensitivity, specificity and detection error rate. Â© 2016 IEEE.