large vessel occlusion detection with novel eeg-based device - … · 2019. 11. 16. · 0 20 40 60...

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Large Vessel Occlusion Detection with Novel EEG-Based DeviceMatthew R. Kesinger1,2, Andrew J. Maza1, Liam T. Berti1, Madeleine Wilcox1

Figure 1. EEG Collection Device and Electrode

Cap.

IntroductionStroke is a leading cause of death and disability worldwide. Shorter stroke onset-to-treatment time (OTT) is associated with more positive patient outcomes. Forpatients with large vessel occlusion (LVO), a severe form of ischemic stroke, OTTcan be minimized by transporting patients directly to hospitals equipped to treatthem. This avoids inter-hospital transfer of LVO patients, which adds two hours toOTT (Malhotra et al., 2017). To allow LVO patients to be transported to theappropriate hospitals (Comprehensive Stroke Centers; CSCs) LVO must berecognized in the prehospital environment. The current prehospital LVO recognitionmethod is performance of a subjective clinical exam like the RACE (69% sensitivity,81% specificity; Smith et al., 2018) by emergency medical services (EMS) personnel.As an alternative to clinical exams in prehospital LVO diagnosis, we developed anelectroencephalogram (EEG) and machine learning-based device (AlphaStroke,Forest Devices, Pittsburgh, PA). We hypothesized that the experimental devicecould be used to detect LVO with sensitivity and specificity > 80%.

Methods

Results Figure 4. Experimental Device and Electrode Cap are Easy to Use

Figure 3. EEG-Based Algorithm Performs Better than RACE and NIHSS

Conclusions and Future Directions

We used an EEG collection device(“experimental device”) and electrode cap(Figure 1) to record EEG data from 137 patientssuspected of having stroke (NIHSS > 0) in theemergency departments of 8 stroke hospitals.We also collected data from 110 controls (NIHSS= 0). LVO and stroke status were determined bylocal neuroradiologists blinded to device output.LVO was defined as an acute occlusion of any ofthe following arteries: ICA/MCA-(M1 orM2)/vertebral/ basilar. An EEG-based LVOrecognition algorithm was generated accordingto the flow chart in Figure 2. Usability data wasobtained using the Likert Scale.

Figure 2. Creation of an EEG-Based LVO Recognition Algorithm

The EEG-based algorithm detects LVO with sensitivity and specificity > 80%, out-performing the RACE and NIHSS. Additionally, the EEG-based algorithm producesfewer false negative results and has higher overall accuracy than the RACE andNIHSS. Experimental device users in the emergency department determined thatthe device and electrode cap are easy to use on average. Patient enrollment isongoing and examination of the performance of the EEG-based algorithm on alarger sample size may yield improved results. Because the experimental device ismade for use in the prehospital setting, usability testing by EMS will be animportant future step.The EEG-based algorithm performs with higher sensitivity than the NIHSS and

RACE. The EEG-based algorithm performs with higher specificity than theNIHSS and specificity equivalent to the RACE (A). Given current stroke statistics(Benjamin et al., 2018) and assuming an LVO prevalence of 30%, the EEG-basedalgorithm produces far fewer false negatives than the RACE and NIHSS, fewerfalse positives than the NIHSS, and the same number of false positives as theRACE (B).

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ReferencesMalhotra, K., J. Gornbein, and J.L. Saver, Front Neurol, 2017. 8: p. 651.Smith, E.E., et al., Stroke, 2018. 49(3): p. e111-e122.Benjamin, E.J., et al., Circulation, 2018. 137(12): p. e67-e492.

128 experimental device and electrode cap users rated device and electrode capusability. Users found the software and hardware “very easy” to use, capplacement “easy”, and electrode connection to be “moderate” on average.Electrodes are connected using conductive gel loaded into a blunt-tipped syringe.Cap application consistently takes <3 minutes during in-house testing. The EEGtest lasts 60 s.

Table 1. Suspected Stroke Population Characteristics

A. B.

CollectEEG

Filter dataDetect bad channels

Create Features

Generate algorithm

Select top features

Export Algorithm

1. Forest Devices, Inc., Pittsburgh, PA, 2. University of Pittsburgh School of Medicine, Pittsburgh, PA

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Hardware DifficultySoftware DifficultyCap Placement DifficultyElectrode Connection Difficulty

Very Easy

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Very Difficult

Subject Characteristic NumberLVO 26 (19%)Stroke 98 (72%)No Stroke 39 (28%)

Age (years; median (IQR)) 66 (56 – 76)

Male 149 (63%)Last Known Well Time (minutes; median (IQR)) 329 (193 – 697)LVO Transferred 11 (42%)Suspected Stroke Transferred 25 (18%)White 86 (63%)African American 49 (36%)

RACE NIHSS ExperimentalDevice

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