An Adaptive Feature Extraction Algorithm for Classification of Seismocardiographic Signals

TL;DR

This paper introduces an adaptive feature extraction method for seismocardiographic signals that improves classification accuracy of lung volume states, outperforming non-adaptive methods especially with fewer bins.

Contribution

The paper presents a novel adaptive binning algorithm for SCG feature extraction, enhancing classification performance over existing non-adaptive techniques.

Findings

Achieved ~90% classification accuracy.

Adaptive method outperforms non-adaptive, especially with fewer bins.

F1 score of 0.91 for adaptive vs. 0.63 for non-adaptive with 16 bins.

Abstract

This paper proposes a novel adaptive feature extraction algorithm for seismocardiographic (SCG) signals. The proposed algorithm divides the SCG signal into a number of bins, where the length of each bin is determined based on the signal change within that bin. For example, when the signal variation is steeper, the bins are shorter and vice versa. The proposed algorithm was used to extract features of the SCG signals recorded from 7 healthy individuals (Age: 29.4$\pm$4.5 years) during different lung volume phases. The output of the feature extraction algorithm was fed into a support vector machines classifier to classify SCG events into two classes of high and low lung volume (HLV and LLV). The classification results were compared with currently available non-adaptive feature extraction methods for different number of bins. Results showed that the proposed algorithm led to a classification accuracy of ~90%. The proposed algorithm outperformed the non-adaptive algorithm, especially as the number of bins was reduced. For example, for 16 bins, F1 score for the adaptive and non-adaptive methods were 0.91$\pm$0.05 and 0.63$\pm$0.08, respectively.