A Deep-Learning Framework for Detecting and Predicting Clinical Events Using Continuous, Multimodal Physiological Signals

Abstract

There are an estimated 313 million surgeries performed worldwide each year. Even with significant clinical and technical advances in perioperative research, many patients experience a major complication during the first 30 days following surgery. In recent years, there has been significant advancement in wearable technology and digital health platforms to support remote patient monitoring. Research into machine learning models for predicting adverse clinical events have predominantly focused on utilizing static, derived vital metrics extracted from Electronic Health Record (EHR) systems. However, many limitations have been identified with developing machine learning models from EHR data. Deep learning offers a solution to these challenges by analyzing the physiological signals directly to organize and automatically extract progressive layers of features. Preliminary research in deep learning has just begun for biomedical signal analysis and has been predominately limited to processing individual biometric channels and signal modalities.

This dissertation presents a novel deep learning framework, called the BiometricNet, for processing continuous, multimodal physiological signals for the detection and prediction of adverse clinical events. In the initial signal pre-processing stage of the BiometricNet, an integrated Generative Multiscale Wavelet De-Noising Autoencoder (Ψ-GANDAE) is utilized to remove common noise patterns encountered during ambulatory signal collection. Signal segmentation and feature extraction is performed in the second processing phase of the BiometricNet, where ResNet and BiLSTM architectures leverage post-processing attention layers (RBLAN) to support multiple signal sensor formats (e.g., electrocardiograms, photoplethysmograms, respiration, temperature, and arterial blood pressure), and collation of multiple synchronized channels. In the final stage of the BiometricNet, detection and prediction of adverse health events is achieved by a Siamese Neural Network (SNN), which produces a risk score based on the comparison of a baseline signal with samples taken through the patient event timeline.

Important considerations addressed in the proposed framework include optimizing the use of available data, given the lack of large publicly available labelled datasets in healthcare, and addressing the imbalanced nature of clinical data, where there is a chronic scarcity representing target adverse event conditions. The BiometricNet is flexible and adaptive to support application to a wide array of adverse clinical events, and it is demonstrated on continuous non-invasive blood pressure estimations from synchronized ECG and PPG signals, and myocardial infarction predication from ECG Lead I, II, and III channels. Lastly, this research honours important considerations regarding AI ethics and network interpretability and transparency to facilitate regulatory approval processes and garner confidence from clinical practitioners.