Clinical Evaluation of a Microwave-Based Device for Detection of Traumatic Intracranial Hemorrhage

Ljungqvist, Johan; Candefjord, Stefan; Persson, Mikael; Jönsson, Lars; Skoglund, Thomas; Elam, Mikael

doi:10.1089/neu.2016.4869

Cited by 49 publications

(33 citation statements)

References 28 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The device was used to differentiate patients with chronic subdural hematomas from healthy controls with a sensitivity of 100% and specificity of 75%. 41 42 Transcranial Doppler based technology is also under investigation. Results of a pilot study performed by Neural Analytics (Los Angeles, California, USA) were presented as an abstract at the 26th European Stroke Conference, Berlin, May 2017, demonstrating sensitivity of 100%, specificity of 86%, and AUC of 0.96, differentiating 17 patients with CT angiography confirmed ELVO from 28 healthy controls.…”

Section: Discussionmentioning

confidence: 99%

The VITAL study and overall pooled analysis with the VIPS non-invasive stroke detection device

Kellner

Sauvageau²,

Snyder

et al. 2018

J NeuroIntervent Surg

View full text Add to dashboard Cite

IntroductionEffective triage of patients with emergent large vessel occlusion (ELVO) to endovascular therapy capable centers may decrease time to treatment and improve outcome for these patients. Here we performed a derivation study to evaluate the accuracy of a portable, non-invasive, and easy to use severe stroke detector.MethodsThe volumetric impedance phase shift spectroscopy (VIPS) device was used to assign a bioimpedance asymmetry score to 248 subjects across three cohorts, including 41 subjects presenting as acute stroke codes at a major comprehensive stroke center (CSC), 79 healthy volunteers, and 128 patients presenting to CSCs with a wide variety of brain pathology including additional stroke codes. Diagnostic parameters were calculated for the ability of the device to discern (1) severe stroke from minor stroke and (2) severe stroke from all other subjects. Patients with intracranial hardware were excluded from the analysis.ResultsThe VIPS device was able to differentiate severe stroke from minor strokes with a sensitivity of 93% (95% CI 83 to 98), specificity of 92% (95% CI 75 to 99), and an area under the curve (AUC) of 0.93 (95% CI 0.85 to 0.97). The device was able to differentiate severe stroke from all other subjects with a sensitivity of 93% (95% CI 83 to 98), specificity of 87% (95% CI 81 to 92), and an AUC of 0.95 (95% CI 0.89 to 0.96).ConclusionThe VIPS device is a portable, non-invasive, and easy to use tool that may aid in the detection of severe stroke, including ELVO, with a sensitivity of 93% and specificity of 92% in this derivation study. This device has the potential to improve the triage of patients suffering severe stroke.

show abstract

Section: Discussionmentioning

confidence: 99%

The VITAL study and overall pooled analysis with the VIPS non-invasive stroke detection device

Kellner

Sauvageau²,

Snyder

et al. 2018

J NeuroIntervent Surg

View full text Add to dashboard Cite

show abstract

“…We apply Equation (9) to the N recorded waveforms and put the obtained clutter free signals in an array as below:…”

Section: Slow Time Spectral Analysismentioning

confidence: 99%

“…The feasibility of using microwaves for stroke and trauma detection has been proved by clinical trials [7][8][9]. The detections of vital signs using both continuous wave (CW) [10][11][12][13] and ultra wideband (UWB) microwave radars [14][15][16][17][18][19] have also been investigated and showed very promising results.…”

Section: Introductionmentioning

confidence: 99%

Investigation of an Ultra Wideband Noise Sensor for Health Monitoring

Zeng

Robakowski

Monteith

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Quick on-scene assessment and early intervention is the key to reduce the mortality of stroke and trauma patients, and it is highly desirable to develop ambulance-based diagnostic and monitoring devices in order to provide additional support to the medical personnel. We developed a compact and low cost ultra wideband noise sensor for medical diagnostics and vital sign monitoring in pre-hospital settings. In this work, we demonstrated the functionality of the sensor for respiration and heartbeat monitoring. In the test, metronome was used to manipulate the breathing pattern and the heartbeat rate reference was obtained with a commercial electrocardiogram (ECG) device. With seventeen tests performed for respiration rate detection, sixteen of them were successfully detected. The results also show that it is possible to detect the heartbeat rate accurately with the developed sensor.

show abstract

“…Therefore, there is a need for diagnostic methods that are suitable for prehospital use. At the forefront of microwave diagnostics research are groups in Sweden [2,3], Austria [4,5,6,7] and Australia [8,9,10], who have reached a stage of pre-clinical and clinical trials on stroke and trauma patients. Significant efforts, including experimental lab test on phantoms, were published in [11].…”

Section: Introductionmentioning

confidence: 99%

“…We have also shown, in three different clinical studies, that classification based on a machine learning method may be used for stroke and trauma diagnostics [2,3]. This method can generate real-time results even on devices with low computational power, such as tablets.…”

Section: Introductionmentioning

confidence: 99%

3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology

Fhager

Candefjord

Elam

2019

Sensors

Self Cite

View full text Add to dashboard Cite

Early, preferably prehospital, detection of intracranial bleeding after trauma or stroke would dramatically improve the acute care of these large patient groups. In this paper, we use simulated microwave transmission data to investigate the performance of a machine learning classification algorithm based on subspace distances for the detection of intracranial bleeding. A computational model, consisting of realistic human head models of patients with bleeding, as well as healthy subjects, was inserted in an antenna array model. The Finite-Difference Time-Domain (FDTD) method was then used to generate simulated transmission coefficients between all possible combinations of antenna pairs. These transmission data were used both to train and evaluate the performance of the classification algorithm and to investigate its ability to distinguish patients with versus without intracranial bleeding. We studied how classification results were affected by the number of healthy subjects and patients used to train the algorithm, and in particular, we were interested in investigating how many samples were needed in the training dataset to obtain classification results better than chance. Our results indicated that at least 200 subjects, i.e., 100 each of the healthy subjects and bleeding patients, were needed to obtain classification results consistently better than chance (p < 0.05 using Student’s t-test). The results also showed that classification results improved with the number of subjects in the training data. With a sample size that approached 1000 subjects, classifications results characterized as area under the receiver operating curve (AUC) approached 1.0, indicating very high sensitivity and specificity.

show abstract

Clinical Evaluation of a Microwave-Based Device for Detection of Traumatic Intracranial Hemorrhage

Cited by 49 publications

References 28 publications

The VITAL study and overall pooled analysis with the VIPS non-invasive stroke detection device

The VITAL study and overall pooled analysis with the VIPS non-invasive stroke detection device

Investigation of an Ultra Wideband Noise Sensor for Health Monitoring

3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology

Contact Info

Product

Resources

About