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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., Competing Interests: At the time this research was conducted, authors ST, CT, KJ, AD, NC, SW, and RH were salaried employees of Neural Analytics, Inc., and TD was a paid consultant. FS is also a former paid consultant. All authors either hold stock or stock options in the company. The funding institution, Neural Analytics, Inc., holds numerous patents related to transcranial doppler technology from which the authors do not directly stand to benefit. Our commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials. This research was also supported in part by NINDS-1R43NS105340.

Objective: In this paper, we introduce a novel hybrid brain-computer interface (BCI) system that measures electrical brain activity as well as cerebral blood velocity using electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) respectively in response to flickering mental rotation (MR) and flickering word generation (WG) cognitive tasks as well as a fixation cross that represents the baseline. This work extends our previous approach, in which we showed that motor imagery induces simultaneous changes in EEG and fTCD to enable task discrimination; and hence, provides a design approach for a hybrid BCI. Here, we show that instead of using motor imagery, the proposed visual stimulation technique enables the design of an EEG-fTCD based BCI with higher accuracy., Approach: Features based on the power spectrum of EEG and fTCD signals were calculated. Mutual information and support vector machines were used for feature selection and classification purposes., Main Results: EEG-fTCD combination outperformed EEG by 4.05% accuracy for MR versus baseline problem and by 5.81% accuracy for WG versus baseline problem. An average accuracy of 92.38% was achieved for MR versus WG problem using the hybrid combination. Average transmission rates of 4.39, 3.92, and 5.60 bits min -1 were obtained for MR versus baseline, WG versus baseline, and MR versus WG problems respectively., Significance: In terms of accuracy, the current visual presentation outperforms the motor imagery visual presentation we designed before for the EEG-fTCD system by 10% accuracy for task versus task problem. Moreover, the proposed system outperforms the state of the art hybrid EEG-fNIRS BCIs in terms of accuracy and/or information transfer rate. Even though there are still limitations of the proposed system, such promising results show that the proposed hybrid system is a feasible candidate for real-time BCIs.

Background: International Consensus Criteria (ICC) consider right-to-left shunt (RLS) present when Transcranial Doppler (TCD) detects even one microbubble (microB). Spencer Logarithmic Scale (SLS) offers more grades of RLS with detection of >30 microB corresponding to a large shunt. We compared the yield of ICC and SLS in detection and quantification of a large RLS., Subjects and Methods: We prospectively evaluated paradoxical embolism in consecutive patients with ischemic strokes or transient ischemic attack (TIA) using injections of 9 cc saline agitated with 1 cc of air. Results were classified according to ICC [negative (no microB), grade I (1-20 microB), grade II (>20 microB or "shower" appearance of microB), and grade III ("curtain" appearance of microB)] and SLS criteria [negative (no microB), grade I (1-10 microB), grade II (11-30 microB), grade III (31100 microB), grade IV (101300 microB), grade V (>300 microB)]. The RLS size was defined as large (>4 mm) using diameter measurement of the septal defects on transesophageal echocardiography (TEE)., Results: TCD comparison to TEE showed 24 true positive, 48 true negative, 4 false positive, and 2 false negative cases (sensitivity 92.3%, specificity 92.3%, positive predictive value (PPV) 85.7%, negative predictive value (NPV) 96%, and accuracy 92.3%) for any RLS presence. Both ICC and SLS were 100% sensitive for detection of large RLS. ICC and SLS criteria yielded a false positive rate of 24.4% and 7.7%, respectively when compared to TEE., Conclusions: Although both grading scales provide agreement as to any shunt presence, using the Spencer Scale grade III or higher can decrease by one-half the number of false positive TCD diagnoses to predict large RLS on TEE.

In this study, chaos analysis was performed on the transcranial Doppler (TCD) signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. Two chaotic invariant measures, i.e. the maximum Lyapunov exponent and the correlation dimension, were calculated for the TCD signals after applying nonlinearity and stationarity tests to them. The sonograms obtained via Burg autoregressive (AR) method demonstrated that the chaotic invariant measures represented the unpredictability and complexity levels of the TCD signals. According to the multiple linear regression analysis, the chaotic invariant measures were found to be highly significant for the regression equation which fitted to the data. This result suggested that the chaotic invariant measures could be used for automatically differentiating various cerebrovascular conditions via an appropriate classifier. For comparison purposes, we investigated several different classification algorithms. The k-nearest neighbour algorithm outperformed all the other classifiers with a classification accuracy of 94.44% on the test data. We used the receiver operating characteristic (ROC) curves in order to assess the performance of the classifiers. The results suggested that the classification systems which use the chaotic invariant measures as input have potential in detecting the blood flow velocity changes due to various brain diseases.