Classification of seismocardiographic cycles into lung volume phases

In this study, a machine learning algorithm was developed to classify seismocardiographic (SCG) signals occurring during low and high lung volumes. The results demonstrated that morphological differences can be observed in SCG waveforms during respiration. SCG events were classified using a Radial Basis Function (RBF) support vector machine (SVM) algorithm into the two classes of low and high lung volume. Classification accuracy was found to be about 75%.