A Bidirectional Long Short-Term Memory Deep Learning Model for Classification of Pulse Waveform

The morphology of the arterial blood pressure (ABP) waveform has been demonstrated to serve as a significant indicator of the patient's condition and a marker of impending changes. The wave separation analysis (WSA) approach is based on invasive measures of concomitant arterial blood flow (ABF) and ABP. Other methods were developed as well, but the analyses were limited to waveforms with the physiological shape, namely the Type A. This study introduces a bidirectional long short-term memory (BiLSTM) deep learning model to classify ABP beats into Type A and Type B/C, this last group refers to a condition of altered vascular compliance and resistance. The models were developed by using central (aortic) and peripheral (femoral) waveforms. The best models have achieved an accuracy of 96% and 90% for aortic and femoral signals, respectively. The ultimate objective of this research is to enhance non-invasive cardiovascular monitoring and facilitate the early detection of arterial alterations.