Photoplethysmography based atrial fibrillation detection: a review.

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.

Intracranial Pressure Monitoring via External Ventricular Drain: Are We Waiting Long Enough Before Recording the Real Value?

BACKGROUND: External ventricular drain (EVD) is a standard approach for both monitoring intracranial pressure (ICP) and draining cerebrospinal fluid (CSF) for patients with subarachnoid hemorrhage. Documenting an accurate ICP value is important to assess the status of the brain, which would require the EVD system to be leveled properly and closed to CSF drainage for an adequate period. It is suggested that a minimum period of 5-minute EVD closure is needed before documenting a true ICP; however, there is no commonly agreed upon standard for documenting ICP. To obtain an insight into how well the intermittent EVD clamping procedure is performed for ICP documentation, we conducted a retrospective analysis of ICP recordings obtained through EVD from 107 patients with subarachnoid hemorrhage. METHODS: The EVD was kept open for continuous CSF drainage and then intermittently closed for ICP documentation. For each EVD closure, mean ICP, standard deviation of ICP, duration of EVD closure, and time interval between 2 adjacent EVD closures were studied. The total number of EVD closures was calculated for each patient. We developed an algorithm to evaluate whether ICP reached a new equilibrium before the EVD was reopened to drainage. The percentage of EVD closures that reach the equilibrium was calculated. RESULTS: The 107 patients had 32 755 EVD closures in total, among which 65.9% instances lasted less than 1 minute and only 16.3% of all the EVD closure episodes lasted longer than 5 minutes. The median duration of each EVD closure was 25 seconds (interquartile range, 10.2 seconds to 2.33 minutes). Only 22.9% of the EVD closures reached ICP equilibrium before EVD reopening. CONCLUSION: A standard guideline and proper training are needed for bedside nurses, and a potential tool that can render ICP trend at a proper scale at bedside would help clinicians correctly document ICP.

Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.

OBJECTIVE: Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracking. Its simplicity and cost-effectiveness has enabled a variety of biomedical applications, such as continuous long-term monitoring of heart arrhythmias, fitness, and sleep tracking, and hydration monitoring. One major issue that can hinder PPG-based applications is movement artifacts, which can lead to false interpretations. In many implementations, noisy PPG signals are discarded. Misinterpreted or discarded PPG signals pose a problem in applications where the goal is to increase the yield of detecting physiological events, such as in the case of paroxysmal atrial fibrillation (AF)-a common episodic heart arrhythmia and a leading risk factor for stroke. In this work, we compared a traditional machine learning and deep learning approaches for PPG quality assessment in the presence of AF, in order to find the most robust method for PPG quality assessment. APPROACH: The training data set was composed of 78 278 30 s long PPG recordings from 3764 patients using bedside patient monitors. Two different representations of PPG signals were employed-a time-series based (1D) one and an image-based (2D) one. Trained models were tested on an independent set of 2683 30 s PPG signals from 13 stroke patients. MAIN RESULTS: ResNet18 showed a higher performance (0.985 accuracy, 0.979 specificity, and 0.988 sensitivity) than SVM and other deep learning approaches. 2D-based models were generally more accurate than 1D-based models. SIGNIFICANCE: 2D representation of PPG signal enhances the accuracy of PPG signal quality assessment.

Continuous monitoring of cerebrovascular reactivity through pulse transit time and intracranial pressure.

OBJECTIVE: Cerebrovascular reactivity (CR) is a mechanism that maintains stable blood flow supply to the brain. Pressure reactivity index (PRx), the correlation coefficient between slow waves of invasive arterial blood pressure (ABP) and intracranial pressure (ICP) has been validated for CR assessment. However, in clinical ward, not every subarachnoid hemorrhage (SAH) patient has invasive ABP monitoring. Pulse transit time (PTT), the propagation time of a pulse wave travelling from the heart to peripheral arteries, has been suggested as a surrogate measure of ABP. In this study, we proposed to use PTT instead of invasive ABP to monitor CR. APPROACH: Forty-five SAH patients with simultaneous recordings of invasive ABP, ICP, oxygen saturation level (SpO2) and electrocardiograph (ECG) were included. PTT was calculated as the time from the ECG R-wave peak to the onset of SpO2. PTT based pressure reactivity index (tPRx) was calculated as the correlation coefficient between slow waves of PTT and ICP. Wavelet tPRx (wtRx) was calculated as the cosine of wavelet phase shift between PTT and ICP. Meanwhile, PRx and wPRx were also calculated using invasive ABP and ICP as input. MAIN RESULTS: The result showed a negative relationship between PTT and ABP (r = -0.58, p  < 0.001). tPRx negatively correlated with PRx (r = -0.51, p  = 0.003). Wavelet method correlated well with correlation method demonstrated through positive relationship between wPRx and PRx (r = 0.82, p  < 0.001) as well as wtPRx and tPRx (r = 0.84, p  < 0.001). SIGNIFICANCE: PTT demonstrates great potential as a useful tool for CR assessment when invasive ABP is unavailable. Key points • Pulse transit time (PTT), defined as the propagation time of a pulse wave travelling from the heart to the peripheral arteries, has been proposed as a surrogate measure of ABP. The relationship between PTT and ABP in SAH patients remains unknown. • Cerebrovascular reactivity (CR) assessment through PTT has advantages over invasive ABP, as it avoids bleeding and infection risk, and can be used outside of the ICU. • We introduced a new method to assess CR using PTT and ICP through correlation based method and wavelet based method. • We found that beat-to-beat PTT was negatively related with invasive ABP in SAH patients. A significant linear relationship exists between PTT-based CR parameter and a well validated method, PRx. PTT demonstrates great potential as a useful tool for CR assessment when invasive ABP is unavailable in SAH patients.