Enhancing Detection Accuracy for Clinical Heart Failure Utilizing Pulse Transit Time Variability and Machine Learning

Physiological signal variability can offer important insight into cardiovascular activity and clinical cardiovascular diseases. Heart rate variability (HRV) and pulse transit time variability (PTTV) are two important time series variabilities. However, combining HRV and PTTV can enhance the classification accuracy for heart failure which is unknown. In this paper, a simultaneous analysis of HRV and PTTV performed on both normal subjects and heart failure patients, was carried out, aiming to investigate the improvement of HRV-based heart failure detection with the assistant of PTTV analysis. Forty normal subjects and forty heart failure patients were enrolled. Standard limb lead-II electrocardiogram and radial artery pressure waveforms were synchronously recorded. HRV and PTTV analysis were performed on the acquired RR and PTT time series using the standard time- (MEAN, SDNN, and RMSSD), frequency- (LF, HF, and LF/HF), and non-linear (SD1, SD2, sample entropy, and fuzzy measure entropy) domain indices. The results showed that all HRV indices except MEAN (P = 0.1) and LF/HF (P = 0.9) showed significant differences (all P <;0.01) between the two group, while only MEAN in PTTV significantly decreases in heart failure patients (P <; 0.01). Moreover, when combined the HRV, PTTV indices, and the predicted probabilities generated from the distance distribution matrix-based convolutional neural network models, the highest classification performances were achieved by a support vector machine classifier, outputting a sensitivity of 0.93, a specificity of 0.88, and an accuracy of 0.90. This paper demonstrated the potential of PTTV analysis for the detection of clinical heart failure.