Toward automated classification of pathological transcranial Doppler waveform morphology via spectral clustering.

Cerebral Blood Flow Velocity waveforms acquired via Transcranial Doppler (TCD) can provide evidence for cerebrovascular occlusion and stenosis. Thrombolysis in Brain Ischemia (TIBI) flow grades are widely used for this purpose, but require subjective assessment by expert evaluators to be reliable. In this work we seek to determine whether TCD morphology can be objectively assessed using an unsupervised machine learning approach to waveform categorization. TCD beat waveforms were recorded at multiple depths from the Middle Cerebral Arteries of 106 subjects; 33 with Large Vessel Occlusion (LVO). From each waveform, three morphological features were extracted, quantifying onset of maximal velocity, systolic canopy length, and the number/prominence of peaks/troughs. Spectral clustering identified groups implicit in the resultant three-dimensional feature space, with gap statistic criteria establishing the optimal cluster number. We found that gap statistic disparity was maximized at four clusters, referred to as flow types I, II, III, and IV. Types I and II were primarily composed of control subject waveforms, whereas types III and IV derived mainly from LVO patients. Cluster morphologies for types I and IV aligned clearly with Normal and Blunted TIBI flows, respectively. Types II and III represented commonly observed flow-types not delineated by TIBI, which nonetheless deviate from normal and blunted flows. We conclude that important morphological variability exists beyond that currently quantified by TIBI in populations experiencing or at-risk for acute ischemic stroke, and posit that the observed flow-types provide the foundation for objective methods of real-time automated flow type classification.

Objective Assessment of Beat Quality in Transcranial Doppler Measurement of Blood Flow Velocity in Cerebral Arteries.

OBJECTIVE: Transcranial Doppler (TCD) ultrasonography measures pulsatile cerebral blood flow velocity in the arteries and veins of the head and neck. Similar to other real-time measurement modalities, especially in healthcare, the identification of high-quality signals is essential for clinical interpretation. Our goal is to identify poor quality beats and remove them prior to further analysis of the TCD signal. METHODS: We selected objective features for this purpose including Euclidean distance between individual and average beat waveforms, cross-correlation between individual and average beat waveforms, ratio of the high-frequency power to the total beat power, beat length, and variance of the diastolic portion of the beat waveform. We developed an iterative outlier detection algorithm to identify and remove the beats that are different from others in a recording. Finally, we tested the algorithm on a dataset consisting of more than 15 h of TCD data recorded from 48 stroke and 34 in-hospital control subjects. RESULTS: We assessed the performance of the algorithm in the improvement of estimation of clinically important TCD parameters by comparing them to that of manual beat annotation. The results show that there is a strong correlation between the two, that demonstrates the algorithm has successfully recovered the clinically important features. We obtained significant improvement in estimating the TCD parameters using the algorithm accepted beats compared to using all beats. SIGNIFICANCE: Our algorithm provides a valuable tool to clinicians for automated detection of the reliable portion of the data. Moreover, it can be used as a pre-processing tool to improve the data quality for automated diagnosis of pathologic beat waveforms using machine learning.

Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals.

Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding. The test data set used in this study consists of 92,012 annotated CBFV pulses, whose quality is representative of real world data. On this test set, the algorithm achieves a true positive rate of 99.998% (2 false negatives), positive predictive value of 99.998% (2 false positives), and mean temporal offset error of 6.10 ± 4.75 ms. We do note that in this context, the way in which true positives, false positives, and false negatives are defined caries some nuance, so care should be taken when drawing comparisons to other algorithms. Additionally, we find that 97.8% and 99.5% of onsets are detected within 10 and 30 ms, respectively, of the true onsets. The algorithm's performance in spite of the large degree of variation in signal quality and waveform morphology present in the test data suggests that it may serve as a valuable tool for the accurate and reliable identification of CBFV pulse onsets in neurocritical care settings.

Sex-Based Differences in Transcranial Doppler Ultrasound and Self-Reported Symptoms After Mild Traumatic Brain Injury.

The possibility of sex-related differences in mild traumatic brain injury (mTBI) severity and recovery remains a controversial subject. With some studies showing that female subjects suffer a longer period of symptom recovery, while others have failed to demonstrate differences. In this study, we explored the sex-related effects of mTBI on self-reported symptoms and transcranial Doppler ultrasound (TCD) measured features in an adolescent population. Fifty-eight subjects were assessed-at different points post-injury-after suffering an mTBI. Subjects answered a series of symptom questions before the velocity from the middle cerebral artery was measured. Subjects participated in breath-holding challenges to evaluate cerebrovascular reactivity. The Pulsatility Index (PI), the ratio of the first peaks (P2R), and the Breath-Hold Index (BHI), were computed. Linear mixed effects models were developed to explore the interactions between measured features, sex, and time since injury while accounting for within subject variation. Over the first 10 days post-injury, the female group had significant interactions between sex and time since injury that was not present in the TCD features. This is the first study to compare sex-related differences in self-reported symptoms and TCD measurements in adolescents suffering an mTBI. It illustrates the pitfalls clinicians face when relying on subjective measures alone during diagnosis and tracking of mTBI patients. In addition, it highlights the need for more focused research on sex-related differences in concussion pathophysiology.

Velocity Curvature Index: a Novel Diagnostic Biomarker for Large Vessel Occlusion.

Despite being a conveniently portable technology for stroke assessment, Transcranial Doppler ultrasound (TCD) remains widely underutilized due to complex training requirements necessary to reliably obtain and interpret cerebral blood flow velocity (CBFV) waveforms. The validation of objective TCD metrics for large vessel occlusion (LVO) represents a first critical step toward enabling use by less formally trained personnel. In this work, we assess the diagnostic utility, relative to current standard CT angiography (CTA), of a novel TCD-derived biomarker for detecting LVO. Patients admitted to the hospital with stroke symptoms underwent TCD screening and were grouped into LVO and control groups based on the presence of CTA confirmed occlusion. Velocity curvature index (VCI) was computed from CBFV waveforms recorded at multiple depths from the middle cerebral arteries (MCA) of both cerebral hemispheres. VCI was assessed for 66 patients, 33 of which had occlusions of the MCA or internal carotid artery. Our results show that VCI was more informative when measured from the cerebral hemisphere ipsilateral to the site of occlusion relative to contralateral. Moreover, given any pair of bilateral recordings, VCI separated LVO patients from controls with average area under receiver operating characteristic curve of 92%, which improved to greater than 94% when pairs were selected by maximal velocity. We conclude that VCI is an analytically valid candidate biomarker for LVO diagnosis, possessing comparable accuracy, and several important advantages, relative to current TCD diagnostic methodologies.

Decision Criteria for Large Vessel Occlusion Using Transcranial Doppler Waveform Morphology.

Background: The current lack of effective tools for prehospital identification of Large Vessel Occlusion (LVO) represents a significant barrier to efficient triage of stroke patients and detriment to treatment efficacy. The validation of objective Transcranial Doppler (TCD) metrics for LVO detection could provide first responders with requisite tools for informing stroke transfer decisions, dramatically improving patient care. Objective: To compare the diagnostic efficacy of two such candidate metrics: Velocity Asymmetry Index (VAI), which quantifies disparity of blood flow velocity across the cerebral hemispheres, and Velocity Curvature Index (VCI), a recently proposed TCD morphological biomarker. Additionally, we investigate a simple decision tree combining both metrics. Methods: We retrospectively compare accuracy/sensitivity/specificity (ACC/SEN/SPE) of each method (relative to standard CT-Angiography) in detecting LVO in a population of 66 subjects presenting with stroke symptoms (33 with CTA-confirmed LVO), enrolled consecutively at Erlanger Southeast Regional Stroke Center in Chattanooga, TN. Results: Individual VCI and VAI metrics demonstrated robust performance, with area under receiver operating characteristic curve (ROC-AUC) of 94% and 88%, respectively. Additionally, leave-one-out cross-validation at optimal identified thresholds resulted in 88% ACC (88% SEN) for VCI, vs. 79% ACC (76% SEN) for VAI. When combined, the resultant decision tree achieved 91% ACC (94% SEN). Discussion: We conclude VCI to be superior to VAI for LVO detection, and provide evidence that simple decision criteria incorporating both metrics may further optimize. Performance: Our results suggest that machine-learning approaches to TCD morphological analysis may soon enable robust prehospital LVO identification. Registration: Was not required for this feasibility study.

A Cross-Sectional Study on Cerebral Hemodynamics After Mild Traumatic Brain Injury in a Pediatric Population.

The microvasculature is prominently affected by traumatic brain injury (TBI), including mild TBI (concussion). Assessment of cerebral hemodynamics shows promise as biomarkers of TBI, and may help inform development of therapies aimed at promoting neurologic recovery. The objective of this study was to assess the evolution in cerebral hemodynamics observable with transcranial Doppler (TCD) ultrasound in subjects suffering from a concussion at different intervals during recovery. Pediatric subjects between the ages of 14 and 19 years clinically diagnosed with a concussion were observed at different points post-injury. Blood flow velocity in the middle cerebral artery was measured with TCD. After a baseline period, subjects participated in four breath holding challenges. Pulsatility index (PI), resistivity index (RI), the ratio of the first two pulse peaks (P2R), and the mean velocity (MV) were computed from the baseline section. The breath hold index (BHI) was computed from the challenge sections. TCD detected two phases of hemodynamic changes after concussion. Within the first 48 h, PI, RI, and P2R show a significant difference from the controls (U = -3.10; P < 0.01, U = -2.86; P < 0.01, and U = 2.62; P < 0.01, respectively). In addition, PI and P2R were not correlated (rp = -0.36; P = 0.23). After 48 h, differences in pulsatile features were no longer observable. However, BHI was significantly increased when grouped as 2-3, 4-5, and 6-7 days post-injury (U = 2.72; P < 0.01, U = 2.46; P = 0.014, and U = 2.38; P = 0.018, respectively). To our knowledge, this is the first longitudinal study of concussions using TCD. In addition, these results are the first to suggest the multiple hemodynamic changes after a concussion are observable with TCD and could ultimately lead to a better understanding of the underlying pathophysiology. In addition, the different hemodynamic responses to a concussion as compared to severe traumatic brain injuries highlight the need for specific diagnostic and therapeutic treatments of mild head injuries in adolescents.

Normative Ranges of Transcranial Doppler Metrics.

OBJECTIVE: To determine normal ranges for traditional transcranial Doppler (TCD) measurements for two age groups (14-19 and 20-29 years) and compare to existing literature results. The development of a normal range for TCD measurements will be required for the development of diagnostic and prognostic tests in the future. MATERIALS AND METHODS: We performed TCD on the middle cerebral artery on 147 healthy subjects aged 18.9 years (SD = 2.1) and calculated mean cerebral blood flow velocity (mCBFV) and pulsatility index (PI). The study population was divided into two age populations (14-19 and 20-29 years). RESULTS: There was a significant decrease in PI (p = 0.015) for the older age group with no difference in mCBFV. CONCLUSION: Age-related, normal data are a prerequisite for TCD to continue to gain clinical acceptance. Our correlation of age-related TCD findings with previously published results as the generally accepted "gold standard" underlines the validity and sensitivity of this ultrasound method.

A Low-power CMOS BFSK Transceiver for Health Monitoring Systems.

A CMOS low-power transceiver for implantable and external health monitoring devices operating in the MICS band is presented. The LNA core has an integrated mixer in a folded configuration to reuse the bias current, allowing high linearity with a low power supply levels. The baseband strip consists of a pseudo differential MOS-C band-pass filter achieving demodulation of 150kHz-offset BFSK signals. An all digital frequency-locked loop is used for LO generation in the RX mode and for driving a class AB power amplifier in the TX mode. The MICS transceiver is designed and fabricated in a 0.18µm 1-poly, 6-metal CMOS process. The sensitivities of -70dBm and -98dBm were achieved with NF of 40dB and 11dB at the data rate of 100kb/s while consuming only 600µW and 1.5mW at 1.2V and 1.8V, respectively. The BERs are less than 10-3 at the input powers of -70dBm at 1.2V and -98dBm at 1.8V at the data rate of 100kb/s. Finally, the output power of the transmitter is 0dBm for a power consumption of 1.8mW.

Silicon on Insulator MESFETs for RF Amplifiers.

CMOS compatible, high voltage SOI MESFETs have been fabricated using a standard 3.3V CMOS process without any changes to the process flow. A 0.6µm gate length device operates with a cut-off frequency of 7.3GHz and a maximum oscillation frequency of 21GHz. There is no degradation in device performance up to its breakdown voltage, which greatly exceeds that of CMOS devices on the same process. Other figures of merit of relevance to RF front-end design are presented, including the maximum stable gain and noise figure. An accurate representation of the device in SPICE has been developed using the commercially available TOM3 model. Using the SOI MESFET model, a source degenerated low noise RF amplifier targeting operation near 1GHz has been designed. The amplifier was fabricated on a PCB board and operates at 940MHz with a minimum NF of 3.8dB and RF gain of 9.9dB while only consuming 5mW of DC power.