Key result
An SCG-based stacked autoencoder classifies breathing states with ~91% accuracy.
Why the study?
The study aimed to assess the human respiratory system for m-health applications by identifying degrees of breathing using cardiac-induced chest-wall vibrations measured via seismocardiogram.
An SCG-based machine learning framework can accurately identify human breathing states, showing potential for m-health and remote monitoring applications.
Should not change clinical practice; leaves open the role of stacked autoencoders for seismocardiogram-based respiratory monitoring.
In this work, a seismocardiogram (SCG) based breathing-state measuring method is proposed for m-health applications. The aim of the proposed framework is to assess the human respiratory system by identifying degree-of-breathings, such as breathlessness, normal breathing, and long and labored breathing. For this, it is needed to measure cardiac-induced chest-wall vibrations, reflected in the SCG signal. Orthogonal subspace projection is employed to extract the SCG cycles with the help of a concurrent ECG signal. Subsequently, fifteen statistically significant morphological-features are extracted from each of the SCG cycles. These features can efficiently characterize physiological changes due to varying respiratory-rates. Stacked autoencoder (SAE) based architecture is employed for the identification of different respiratory-effort levels. The performance of the proposed method is evaluated and compared with other standard classifiers for 1147 analyzed SCG-beats. The proposed method gives an overall average accuracy of 91.45% in recognizing three different breathing states. The quantitative analysis of the performance results clearly shows the effectiveness of the proposed framework. It may be employed in various healthcare applications, such as pre-screening medical sensors and IoT based remote health-monitoring systems.
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Choudhary et al. (2020) studied Healthy (breathing state identification) (n=8). Stacked autoencoder (SAE) based breathing-state detector vs. Conventional classifiers (e.g., SVM, kNN, Naive Bayes) was evaluated on Overall average accuracy in recognizing three different breathing states. A stacked autoencoder-based breathing-state detector using seismocardiogram signals achieved an overall average accuracy of 91.45% in recognizing breathlessness, normal breathing, and labored breathing.
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