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 1 st place in all three phases of the competition.
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%. IntroductionThis work describes a novel approach designed for Physionet 2016 Challenge Classification of Normal/Abnormal Heart Sound Recordings. The objective here is to classify Phonocardiogram (PCG) recordings into normal and abnormal categories. A comprehensive detail on the database, explaining how the PCG signals were collected and the type of abnormalities found are discussed in the paper mentioned in the reference section [3].Usually, statistical features such as means, standard deviations of systole, diastole intervals, and signal complexity features are used as features for classification and these are enough to give decent results. But these features fail to completely capture the temporal information of the signal. This could be very important since it represents how each heart beat changes over time.To capture the temporal dynamics, we take PCG signal beat by beat and assign each beat a symbol/category based on different thresholds set on features (ratios of systole intervals to RR interval, diastole intervals to RR interval, beat energy, the power of frequency component above 200 Hz). Thus, a sequence of symbols for the entire signal is obtained. We then extract features out of this sequence for classification. One of the ways we employed is to create a Markov chain with symbols being the states of the matrix and the resulting transition probabilities are used as features. These features along with marginal probabilities of states and rest of the acoustic features like sample entropy, instantaneous frequency analysis etc. are used to train an ensemble/bag of 4 class boosted tree classifiers [8, 13] and 4 artificial neural networks [12]. PreprocessingThe training data consists of PCG signals of varying length, anywhere between 5s to just over 120s all sampled at 2000 Hz. For training, all the signals were re-sampled to 1000 Hz and features were extracted. Since PCG record...
Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution). Therefore, detecting outof-distribution (OOD) samples is very important to avoid classification errors. In the context of OOD detection for image classification, one of the recent approaches proposes training a classifier called "confident-classifier" by minimizing the standard cross-entropy loss on in-distribution samples and minimizing the KL divergence between the predictive distribution of OOD samples in the low-density regions of in-distribution and the uniform distribution (maximizing the entropy of the outputs). Thus, the samples could be detected as OOD if they have low confidence or high entropy. In this paper, we analyze this setting both theoretically and experimentally. We conclude that the resulting confident-classifier still yields arbitrarily high confidence for OOD samples far away from the in-distribution. We instead suggest training a classifier by adding an explicit "reject" class for OOD samples.
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