Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations—a technology known as photoplethysmography (PPG)—from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Interventions of intracranial pressure (ICP) elevation in neurocritical care is currently delivered only after healthcare professionals notice sustained and significant mean ICP elevation. This paper uses the morphological clustering and analysis of ICP (MOCAIP) algorithm to derive 24 metrics characterizing morphology of ICP pulses and test the hypothesis that preintracranial hypertension (Pre-IH) segments of ICP can be differentiated, using these morphological metrics, from control segments that were not associated with any ICP elevation or at least 1 h prior to ICP elevation. Furthermore, we investigate whether a global optimization algorithm could effectively find the optimal subset of these morphological metrics to achieve better classification performance as compared to using full set of MOCAIP metrics. The results showed that Pre-IH segments, using the optimal subset of metrics found by the differential evolution algorithm, can be differentiated from control segments at a specificity of 99% and sensitivity of 37% for these Pre-IH segments 5 min prior to the ICP elevation. While the sensitivity decreased to 21% for Pre-IH segments, 20 min prior to ICP elevation, the high specificity of 99% was retained. The performance using the full set of MOCAIP metrics was shown inferior to results achieved using the optimal subset of metrics. This paper demonstrated that advanced ICP pulse analysis combined with machine learning could potentially leads to the forecasting of ICP elevation so that a proactive ICP management could be realized based on these accurate forecasts.

Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to longterm AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust detection of AFib episodes. Traditionally, signal quality assessment is often based on the evaluation of similarity between pulses to derive signal quality indices. There are limitations to using this approach for accurate assessment of PPG quality in the presence of arrhythmia, as in the case of AFib, mainly due to substantial changes in pulse morphology. In this paper, we first tested the performance of algorithms selected from a body of studies on PPG quality assessment using a dataset of PPG recordings from patients with AFib. We then propose machine learning approaches for PPG quality assessment in 30-s segments of PPG recording from 13 stroke patients admitted to the University of California San Francisco (UCSF) neuro intensive care unit and another dataset of 3764 patients from one of the five UCSF general intensive care units. We used data acquired from two systems, fingertip PPG (fPPG) from a bedside monitor system, and radial PPG (rPPG) measured using a wearable commercial wristband. We compared various supervised machine learning techniques including knearest neighbors, decisions trees, and a two-class support vector machine (SVM). SVM provided the best performance. fPPG signals were used to build the model and achieved 0.9477 accuracy when tested on the data from the fPPG exclusive to the test set, and 0.9589 accuracy when tested on the rPPG data.

We investigated whether intracranial pressure (ICP) pulse morphological metrics could be used to realize continuous detection of low cerebral blood flow. Sixty-three acutely brain injured patients with ICP monitoring, daily (133)Xenon cerebral blood flow (CBF) and daily transcranial Doppler (TCD) assessments were studied. Their ICP recordings were time-aligned with the CBF and TCD measurements so that a 1 h ICP segment near the CBF and TCD measurements was obtained. Each of these recordings was processed by the Morphological Cluster and Analysis of Intracranial Pressure (MOCAIP) algorithm to extract pulse morphological metrics. Then the differential evolution algorithm was used to find the optimal combination of the metrics that provided, using the regularized linear discriminant analysis, the largest combined positive predictivity and sensitivity. At a CBF threshold of 20 ml/min/100 g, a sensitivity of 81.8 +/- 0.9% and a specificity of 50.1 +/- 0.2% were obtained using the optimal combination of conventional TCD and blood analysis metrics as input to a regularized linear classifier. However, using the optimal combination of the MOCAIP metrics alone we were able to achieve a sensitivity of 92.5 +/- 0.7% and a specificity of 84.8 +/- 0.8%. Searching the optimal combination of all available metrics, we achieved the best result that was marginally better than those from using MOCAIP alone. This study demonstrated that the potential role of ICP monitoring may be extended to provide an indicator of low global cerebral blood perfusion.

Objective . Wearable devices equipped with plethysmography (PPG) sensors provided a low-cost, long-term solution to early diagnosis and continuous screening of heart conditions. However PPG signals collected from such devices often suffer from corruption caused by artifacts. The objective of this study is to develop an effective supervised algorithm to locate the regions of artifacts within PPG signals. Approach . We treat artifact detection as a 1D segmentation problem. We solve it via a novel combination of an active-contour-based loss and an adapted U-Net architecture. The proposed algorithm was trained on the PPG DaLiA training set, and further evaluated on the PPG DaLiA testing set, WESAD dataset and TROIKA dataset. Main results . We evaluated with the DICE score, a well-established metric for segmentation accuracy evaluation in the field of computer vision. The proposed method outperforms baseline methods on all three datasets by a large margin (≈7 percentage points above the next best method). On the PPG DaLiA testing set, WESAD dataset and TROIKA dataset, the proposed method achieved 0.8734 ± 0.0018, 0.9114 ± 0.0033 and 0.8050 ± 0.0116 respectively. The next best method only achieved 0.8068 ± 0.0014, 0.8446 ± 0.0013 and 0.7247 ± 0.0050. Significance . The proposed method is able to pinpoint exact locations of artifacts with high precision; in the past, we had only a binary classification of whether a PPG signal has good or poor quality. This more nuanced information will be critical to further inform the design of algorithms to detect cardiac arrhythmia.