Using Eulerian video magnification to enhance detection of fasciculations in people with amyotrophic lateral sclerosis

Detection of Carotid Artery Stenosis Based on Video Motion Analysis for Fast Screening

Background

Carotid artery stenosis (CAS) is a common cause of ischemic stroke, and the early detection of CAS may improve patient outcomes. Carotid Doppler ultrasound is commonly used to diagnose CAS. However, it is costly and may not be practical for regular screening practice. This article presents a novel noninvasive and noncontact detection technique using video‐based motion analysis (VMA) to extract useful information from subtle pulses on the skin surface to screen for CAS.

Methods and Results

We prospectively enrolled 202 patients with prior carotid Doppler ultrasound data. A short 30‐second video clip of the neck was taken using a commercial mobile device and analyzed by VMA with mathematical quantification of the amplitude of skin motion changes in a blinded manner. The first 40 subjects were used to set up the VMA protocol and define cutoff values, and the following 162 subjects were used for validation. Overall, 54% of the 202 subjects had ultrasound‐confirmed CAS. Using receiver operating characteristic curve analysis, the area under the curve of VMA‐derived discrepancy values to differentiate patients with and without CAS was excellent (area under the curve, 0.914 [95% CI, 0.874–0.954]; P<0.01). The best cutoff value of VMA‐derived discrepancy values to screen for CAS was 5.1, with a sensitivity of 87% and a specificity of 87%. The diagnostic accuracy was consistently high in different subject subgroups.

Conclusions

A simple and accurate screening technique to quickly screen for CAS using a VMA system is feasible, with acceptable sensitivity and specificity.

Seeing the unseen: Could Eulerian video magnification aid clinician detection of subclinical Parkinson's tremor?

INTRODUCTION

Eulerian magnification amplifies very small movements in video, revealing otherwise invisible motion. This raises the possibility that it could enable clinician visualisation of subclinical tremor using a standard camera. We tested whether Eulerian magnification of apparently atremulous hands reveals a Parkinsonian tremor more frequently in Parkinson's than in controls.

METHOD

We applied Eulerian magnification to smartphone video of 48 hands that appeared atremulous during recording (22 hands from 11 control participants, 26 hands from 17 idiopathic Parkinson's participants). Videos were rated for Parkinsonian tremor appearance (yes/no) before and after Eulerian magnification by three movement disorder specialist neurologists.

RESULTS

The proportion of hands correctly classified as Parkinsonian or not by clinicians was significantly higher after Eulerian magnification (OR = 2.67; CI = [1.39, 5.17]; p < 0.003). Parkinsonian-appearance tremors were seen after magnification in a number of control hands, but the proportion was greater in the Parkinson's hands.

CONCLUSION

Eulerian magnification slightly improves clinician ability to identify apparently atremulous hands as Parkinsonian. This suggests that some of the apparent tremor revealed may be subclinical Parkinson's (pathological) tremor, and Eulerian magnification may represent a first step towards contactless visualisation of such tremor. However, the technique also reveals apparent tremor in control hands. Therefore, our method needs additional elaboration and would not be of direct clinical use in its current iteration.

Extracting physiological information in experimental biology via Eulerian video magnification

BACKGROUND

Videographic material of animals can contain inapparent signals, such as color changes or motion that hold information about physiological functions, such as heart and respiration rate, pulse wave velocity, and vocalization. Eulerian video magnification allows the enhancement of such signals to enable their detection. The purpose of this study is to demonstrate how signals relevant to experimental physiology can be extracted from non-contact videographic material of animals.

RESULTS

We applied Eulerian video magnification to detect physiological signals in a range of experimental models and in captive and free ranging wildlife. Neotenic Mexican axolotls were studied to demonstrate the extraction of heart rate signal of non-embryonic animals from dedicated videographic material. Heart rate could be acquired both in single and multiple animal setups of leucistic and normally colored animals under different physiological conditions (resting, exercised, or anesthetized) using a wide range of video qualities. Pulse wave velocity could also be measured in the low blood pressure system of the axolotl as well as in the high-pressure system of the human being. Heart rate extraction was also possible from videos of conscious, unconstrained zebrafish and from non-dedicated videographic material of sand lizard and giraffe. This technique also allowed for heart rate detection in embryonic chickens in ovo through the eggshell and in embryonic mice in utero and could be used as a gating signal to acquire two-phase volumetric micro-CT data of the beating embryonic chicken heart. Additionally, Eulerian video magnification was used to demonstrate how vocalization-induced vibrations can be detected in infrasound-producing Asian elephants.

CONCLUSIONS

Eulerian video magnification provides a technique to extract inapparent temporal signals from videographic material of animals. This can be applied in experimental and comparative physiology where contact-based recordings (e.g., heart rate) cannot be acquired.

Eulerian videography technology improves classification of sleep architecture in primates

Sleep is a critically important dimension of primate behavior, ecology, and evolution, yet primate sleep is under-studied because current methods of analyzing sleep are expensive, invasive, and time-consuming. In contrast to electroencephalography (EEG) and actigraphy, videography is a cost-effective and non-invasive method to study sleep architecture in animals. With video data, however, it is challenging to score subtle changes that occur in different sleep states, and technology has lagged behind innovations in EEG and actigraphy. Here, we applied Eulerian videography to magnify pixels relevant to scoring sleep from video, and then compared these results to analyses based on actigraphy and standard infrared videography. We studied four species of lemurs (Eulemur coronatus, Lemur catta, Propithecus coquereli, Varecia rubra) for 12-h periods per night, resulting in 6480 1-min epochs for analysis. Cramer's V correlation between actigraphy-classified sleep and infrared videography-classified sleep revealed consistent results in eight of the nine 12-h videos scored. A sample of the infrared videography was then processed by Eulerian videography for movement magnification and re-coded. A second Cramer's V correlation analysis, between two independent scorers coding the same Eulerian-processed video, found that interobserver agreement among Eulerian videography increased sleep vs. awake, NREM, and REM classifications by 7.1%, 46.7%, and 34.3%, respectively. Furthermore, Eulerian videography was more strongly correlated with actigraphy data when compared to results from standard infrared videography. The increase in agreement between the two scorers indicates that Eulerian videography has the potential to improve the identification of sleep states in lemurs and other primates, and thus to expand our understanding of sleep architecture without the need for EEG.

Detection of Muscle Tension Dysphonia Using Eulerian Video Magnification: A Pilot Study

OBJECTIVE

To determine whether Eulerian Video Magnification software is useful in diagnosis of muscle tension dysphonia (MTD).

STUDY DESIGN

Prospective.

METHODS

Adult patients scheduled in a tertiary care laryngology practice for evaluation of dysphonia were recruited between November 2016 and March 2017. Demographic and clinical data were extracted from patient charts. Diagnosis of MTD was confirmed with videostroboscopic and physical exam and by a speech-language pathologist. Eighteen MTD patients were video recorded while at rest and with phonation. Five patients without MTD also were analyzed as controls. Videos were analyzed using Eulerian Video Magnification software (Massachusetts Institute of Technology) to assess change in blood flow at the forehead, infrahyoid muscles, and sternocleidomastoid muscles, while using the values of the background wall as a control value.

RESULTS

Patients with MTD demonstrated little change in perfusion to the infrahyoid muscles of the neck while phonating (+1% ± 55%). Control subjects demonstrated an increase in perfusion to the infrahyoid muscles while phonating (+102% ± 164%), with this change being significant when comparing the two groups (P = 0.04, t = 2.189, df = 21). A change in perfusion of 0% or less to infrahyoid muscles was 75% sensitive and 70% specific for diagnosis of MTD. No differences in perfusion were found between other regions assessed. Patient age and gender did not correlate with any change in perfusion between rest and phonation.

CONCLUSION

Our data suggest that Eulerian Video Magnification can be used in the diagnosis of MTD by focusing on the difference in perfusion to the infrahyoid muscles between rest and phonation.