Browsing by Author "Vernekar, S."

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A novel approach for classification of normal/abnormal phonocardiogram recordings using temporal signal analysis and machine learning
(IEEE Computer Society help@computer.org, 2016) Vernekar, S.; Nair, S.; Vijayasenan, D.; Ranjan, R.
This paper discusses a novel approach used for classification of phonocardiogram (PCG) excerpts into normal and abnormal classes as a part of Physionet 2016 challenge [10]. The dataset used for the competition comprises of cardiac abnormalities such as mitral valve prolapse (MVP), benign murmurs, aortic diseases, coronary artery disease, miscellaneous pathological conditions etc. [3], We present the approach used for classification from a general machine learning application standpoint, giving details on feature extraction, type of classifiers used comparing their performances individually and in combination. We propose a technique which leverages previous research on feature extraction with a novel approach to modeling temporal dynamics of the signal using Markov chain analysis [7,9]. These newly introduced Markov features along with other statistical and frequency domain features, trained over an ensemble of artificial neural networks and gradient boosting trees, with bagging, gave us an accuracy of 82% on the validation dataset provided in the competition and was consistent with the test data with the best result of 78%. Â© 2016 CCAL.
A novel approach for Robust Detection of Heart Beats in Multimodal Data using neural networks and boosted trees
(IEEE Computer Society help@computer.org, 2016) Vernekar, S.; Vijayasenan, D.; Ranjan, R.
This work describes a novel approach designed for Physionet 2014 Challenge, Robust Detection of Heart Beats in Multimodal Data [5]. The objective here is to detect the location of R peaks from QRS complex of an electrocardiogram (ECG) excerpt. Robust detection of heart beats in a noisy ECG signal is an extremely difficult task. To overcome the challenge in such situations, besides ECG, blood pressure (BP) signal is also recorded at the same time; hence the idea here is that, if a segment of one of the signals is noisy, the peaks in that segment can be better estimated by peaks found in the corresponding segment of other signal, if good. The approach uses Machine Learning (ML) methods to identify locations of R-peaks in a given segment of ECG or BP signal. Peaks from both ECG and BP signal are found separately using a novel feature representation and subsequent ML approaches that renders R peaks in the signal, easier to be detected, by a simple windowing technique. Individually detected peaks, from both ECG and BP are further analyzed in chunks of equal short time periods, and the best result of the two is chosen in final peak prediction based on variance comparison techniques. The performance of system on the training dataset [11] provided in the competition is 99.95%. The performance on test datasets which are hidden for phase I, phase II and phase III of the competition respectively are 93.27%, 90.28% and 89.74%. The submission resulted in 1st place in all three phases of the competition. Â© 2016 CCAL.
A novel approach for classification of normal/abnormal phonocardiogram recordings using temporal signal analysis and machine learning
(2016) Vernekar, S.; Nair, S.; Vijaysenan, D.; Ranjan, R.
This paper discusses a novel approach used for classification of phonocardiogram (PCG) excerpts into normal and abnormal classes as a part of Physionet 2016 challenge [10]. The dataset used for the competition comprises of cardiac abnormalities such as mitral valve prolapse (MVP), benign murmurs, aortic diseases, coronary artery disease, miscellaneous pathological conditions etc. [3], We present the approach used for classification from a general machine learning application standpoint, giving details on feature extraction, type of classifiers used comparing their performances individually and in combination. We propose a technique which leverages previous research on feature extraction with a novel approach to modeling temporal dynamics of the signal using Markov chain analysis [7,9]. These newly introduced Markov features along with other statistical and frequency domain features, trained over an ensemble of artificial neural networks and gradient boosting trees, with bagging, gave us an accuracy of 82% on the validation dataset provided in the competition and was consistent with the test data with the best result of 78%. � 2016 CCAL.
A novel approach for Robust Detection of Heart Beats in Multimodal Data using neural networks and boosted trees
(2016) Vernekar, S.; Vijaysenan, D.; Ranjan, R.
This work describes a novel approach designed for Physionet 2014 Challenge, Robust Detection of Heart Beats in Multimodal Data [5]. The objective here is to detect the location of R peaks from QRS complex of an electrocardiogram (ECG) excerpt. Robust detection of heart beats in a noisy ECG signal is an extremely difficult task. To overcome the challenge in such situations, besides ECG, blood pressure (BP) signal is also recorded at the same time; hence the idea here is that, if a segment of one of the signals is noisy, the peaks in that segment can be better estimated by peaks found in the corresponding segment of other signal, if good. The approach uses Machine Learning (ML) methods to identify locations of R-peaks in a given segment of ECG or BP signal. Peaks from both ECG and BP signal are found separately using a novel feature representation and subsequent ML approaches that renders R peaks in the signal, easier to be detected, by a simple windowing technique. Individually detected peaks, from both ECG and BP are further analyzed in chunks of equal short time periods, and the best result of the two is chosen in final peak prediction based on variance comparison techniques. The performance of system on the training dataset [11] provided in the competition is 99.95%. The performance on test datasets which are hidden for phase I, phase II and phase III of the competition respectively are 93.27%, 90.28% and 89.74%. The submission resulted in 1st place in all three phases of the competition. � 2016 CCAL.