Transcranial Doppler (TCD) is useful for investigation of intracranial arterial blood flow and can be used to detect a real-time embolic signal. Unfortunately, artefacts can mimic the embolic signal, complicating interpretation and necessitating expert-level opinion to distinguish the two. Resolving this situation is critical to achieve improved accuracy and utility of TCD for patients with disrupted intracranial arterial blood flow, such as stroke victims. A common type of stroke encountered in the clinic is cryptogenic stroke (or stroke with undetermined etiology), and patent foramen ovale (PFO) has been associated with the condition. An early clinical trial of PFO closure effect on secondary stroke prevention failed to demonstrate any benefit for the therapy, and research into the PFO therapy generally diminished. However, the recent publication of large randomized control trials with demonstrated benefit of PFO closure for recurrent stroke prevention has rekindled the interest in PFO in patients with cryptogenic stroke. To confirm that emboli across the PFO can reach the brain, TCD should be applied to detect the air embolic signal after injection of agitated saline bubbles at the antecubital vein. In addition, the automated embolic signal detection method should further facilitate use of TCD for air embolic signal detection after the agitated saline bubbles injection in patients with cryptogenic stroke and PFO.

This paper addresses the detection of emboli from signals acquired with a new miniaturized and portable transcranial Doppler ultrasound device. The use of this device enables outpatient monitoring but increases the number of artifacts. These artifacts usually come from the patient voice and motion and can be superimposed to emboli. For this reason and because of the scarcity of emboli compared to artifacts, reliably detect emboli is a challenging task. As an example, the 11809 s of signal used in this study contained 0.06 % of embolic events and 10.14 % of artifacts. Herein, we propose an automatic and sequential approach. The method is based on sequential determination of high intensity transient signals. We also define efficient features to describe emboli in the time frequency representation. On our database, the number of artifacts detected as emboli is divided by more than 10 compared to the other algorithms reported in the literature.

This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.