Memory-efficient neural networks and k-NN classifiers for PCG signals achieved up to 96.0% and 98.7% accuracy, respectively, for detecting cardiovascular diseases on wearable devices.
Can machine learning classifiers accurately detect heart failure and other cardiac conditions from PCG signals while remaining memory-efficient for edge computing?
Memory-efficient neural networks can accurately classify normal and pathological phonocardiogram signals, enabling potential deployment on resource-limited wearable devices for heart failure detection.
Conventional diagnostic tools for cardiovascular diseases usually employ expensive instrumentation and require specialized medical staff. An inexpensive and non-invasive alternative is the phonocardiogram (PCG). This paper presents the development of classifiers for binary (Normal/Pathological) and multiclass classifications of PCG signals. The latter discerns between a subset of heart diseases (mitral valve prolapse (MVP), coronary disease (CAD), and benign murmurs (Benign)). Two balanced datasets were created from the Physionet 2016/CinC database, consisting of 10104 and 13136 5-s frames. A custom preprocessing chain includes denoising, normalizing, and splitting the PCG signals, making them suitable to extract the scalar features set, constituting the training and test set. Several ML/DL models (e.g., SVMs (Support Vector Machines), k-NNs (k-Nearest Neighbors), and NNs (Neural Networks)) were trained and tested to classify the PCG signals. For binary classification, three different NNs have reached 96.0%, 95.9%, and 93.4% accuracy, and 95.9%, 96.0%, and 93.3% F1-scores, respectively. However, k-NN classifiers provide higher accuracy (up to 98.7%) than NNs but require much larger memory (up to 11 MB). As for the multiclass classification, three custom NNs have achieved 96.0%, 95.8%, and 94.7% accuracy with 735 kB max memory occupation. The developed classifiers provide a good balance between complexity and performance, with the latter not dependent on signal quality. In the feature engineering phase, the heart sound segmentation was not performed to make the classifiers suitable for resource-limited platforms.
Spongano et al. (Tue,) conducted a other in Cardiovascular diseases (heart failure, mitral valve prolapse, coronary artery disease). Machine learning classifiers (NNs, k-NNs, SVMs) for PCG signals was evaluated on Classification accuracy and F1-score. Memory-efficient neural networks and k-NN classifiers for PCG signals achieved up to 96.0% and 98.7% accuracy, respectively, for detecting cardiovascular diseases on wearable devices.