Segmentation and Optimal Region Selection of Physiological Signals using Deep Neural Networks and Combinatorial Optimization
This addresses the challenge of improving diagnostic accuracy in medical signal processing by focusing on high-quality segments, though it is incremental as it builds on existing methods.
The paper tackles the problem of noisy physiological signals by proposing an algorithm that automatically selects an optimal segment for analysis, inspired by physician strategies, and reports a 2.4% increase in sensitivity for heart sound segmentation compared to standard approaches.
Physiological signals, such as the electrocardiogram and the phonocardiogram are very often corrupted by noisy sources. Usually, artificial intelligent algorithms analyze the signal regardless of its quality. On the other hand, physicians use a completely orthogonal strategy. They do not assess the entire recording, instead they search for a segment where the fundamental and abnormal waves are easily detected, and only then a prognostic is attempted. Inspired by this fact, a new algorithm that automatically selects an optimal segment for a post-processing stage, according to a criteria defined by the user is proposed. In the process, a Neural Network is used to compute the output state probability distribution for each sample. Using the aforementioned quantities, a graph is designed, whereas state transition constraints are physically imposed into the graph and a set of constraints are used to retrieve a subset of the recording that maximizes the likelihood function, proposed by the user. The developed framework is tested and validated in two applications. In both cases, the system performance is boosted significantly, e.g in heart sound segmentation, sensitivity increases 2.4% when compared to the standard approaches in the literature.