  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.
