The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN). Given the cost-sensitive nature of misclassification, our CNN architecture is trained using a modified loss function that directly optimizes the trade-off between sensitivity and specificity. We evaluated our algorithm at the 2016 PhysioNet Computing in Cardiology challenge where the objective was to accurately classify normal and abnormal heart sounds from single, short, potentially noisy recordings. Our entry to the challenge achieved a final specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved the greatest specificity score out of all challenge entries and, using just a single CNN, our algorithm differed in overall score by only 0.02 compared to the top place finisher, which used an ensemble approach.
While there have been many previous efforts applied to automated heart sound analysis, gauging the success of historical approaches has been somewhat difficult, due to differences in dataset quality, number of recordings available for training and testing algorithms, recorded signal lengths and the environment in which data was collected (e.g. clinical vs. non-clinical settings). Moreover, some existing works have not performed appropriate train-test data splits and have reported results on training or validation data, which is highly likely to produce optimistic results due to overfitting @cite_27 . In this work, we report results from the 2016 PhysioNet Computing in Cardiology Challenge, which evaluated entries on a large test-set that was not made publicly available. To reduce overfitting, no recordings from the same subject were included in both the training and the test set and a variety of both and PCG recordings, which exhibited very poor signal quality, were included to encourage the development of accurate and robust algorithms.
@cite_27
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In this paper, we studied and validated a simple heart beat classifier based on ECG feature models selected with the focus on an improved generalization capability. We considered features from the RR series, as well as features computed from the ECG samples and different scales of the wavelet transform, at both avail able leads. The classification performance and generalization were studied using publicly available databases: the MIT-BIH Arrhythmia, the MIT-BIH Supraventricular Arrhythmia, and the St. Pe tersburg Institute of Cardiological Technics (INCART) databases. The Association for the Advancement of Medical Instrumentation recommendations for class labeling and results presentation were followed. A floating feature selection algorithm was used to obtain the best performing and generalizing models in the training and validation sets for different search configurations. The best model found comprehends eight features, was trained in a partition of the MIT-BIH Arrhythmia, and was evaluated in a completely disjoint partition of the same database. The results obtained were: global accuracy of 93 ; for normal beats, sensitivity (S) 95 , positive predictive value (P+) 98 ; for supraventricular beats, S 11 , P+ 39 ; and for ventricular beats S 81 , P+ 87 . In order to test the generalization capability, performance was also evaluated in the INCART, with results comparable to those obtained in the test set. This classifier model has fewer features and performs better than other state-of-the-art methods with results suggesting better generalization capability. In this paper, we propose a novel approach based on deep learning for active classification of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation from the raw ECG data in an unsupervised way using stacked denoising autoencoders (SDAEs) with sparsity constraint. After this feature learning phase, we add a softmax regression layer on the top of the resulting hidden representation layer yielding the so-called deep neural network (DNN). During the interaction phase, we allow the expert at each iteration to label the most relevant and uncertain ECG beats in the test record, which are then used for updating the DNN weights. As ranking criteria, the method relies on the DNN posterior probabilities to associate confidence measures such as entropy and Breaking-Ties (BT) to each test beat in the ECG record under analysis. In the experiments, we validate the method on the well-known MIT-BIH arrhythmia database as well as two other databases called INCART, and SVDB, respectively. Furthermore, we follow the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation. The results obtained show that the newly proposed approach provides significant accuracy improvements with less expert interaction and faster online retraining compared to state-of-the-art methods. In modern medicine, every cardiac assessment or respiratory check-up includes an audio auscultation during which one the medical specialist listens to sounds from the patient body with different tools (stethoscope, sonography). This shows how important sound analysis is for heart and lungs disease detection. During the IeBRI 2017 challenge, a database of 920 records acquired from 126 subject, was used to find a method that predicted if a respiratory cycle contains, or not, adventitious sounds like crackles, wheezes or both of them. The team which submits the best results reached around 50 of correct detection. Using a machine learning approach with a boosted decisional tree model and more audio features leads to the same results. A new approach consists in creating a new model at the patient level, which is able to decide if a patient sounds sick or not, by taking as input the predicted results of the first classification model. This new model permits to reach 85 of good predictions and could be used as a tool for helping doctors to make better diagnosis. The processing of ECG signal provides a wealth of information on cardiac function and overall cardiovascular health. While multi-lead ECG recordings are often necessary for a proper assessment of cardiac rhythms, they are not always available or practical, for example in fet al ECG applications. Moreover, a wide range of small non-obtrusive single-lead ECG ambulatory monitoring devices are now available, from which heart rate variability (HRV) and other health-related metrics are derived. Proper beat detection and classification of abnormal rhythms is important for reliable HRV assessment and can be challenging in single-lead ECG monitoring devices. In this manuscript, we modelled the heart rate signal as an adaptive non-harmonic model and used the newly developed synchrosqueezing transform (SST) to characterize ECG patterns. We show how the proposed model can be used to enhance heart beat detection and classification between normal and abnormal rhythms. In particular, using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and the Association for the Advancement of Medical Instrumentation (AAMI) beat classes, we trained and validated a support vector machine (SVM) classifier on a portion of the annotated beat database using the SST-derived instantaneous phase, the R-peak amplitudes and R-peak to R-peak interval durations, based on a single ECG lead. We obtained sentivities and positive predictive values comparable to other published algorithms using multiple leads and many more features.