Sequential Emboli Detection From Ultrasound Outpatient Data

Wavelet Scattering Transform based Doppler signal classification

Normal blood supply to the human brain may be marred by the presence of a clot inside the blood vessels. This clot structure called emboli inhibits normal blood flow to the brain. It is considered as one of the main sources of stroke. Presence of emboli in human's can be determined by the analysis of transcranial Doppler signal. Different signal processing and machine learning algorithms have been used for classifying the detected signal as an emboli, Doppler speckle, and an artifact. In this paper, we sought to make use of the wavelet transform based algorithm called Wavelet Scattering Transform, which is translation invariant and stable to deformations for classifying different Doppler signals. With its architectural resemblance to Convolutional Neural Network, Wavelet Scattering Transform works well on small datasets and subsequently was trained on a dataset consisting of 300 Doppler signals. To check the effectiveness of extracted Scattering transform based features for Doppler signal classification, learning algorithms that included multi-class Support vector machine, k-nearest neighbor and Naive Bayes algorithms were trained. Comparative analysis was done with respect to the handcrafted Continuous wavelet transform features extracted from samples and Wavelet scattering with Support vector machine achieved an accuracy of 98.89%. Also, with set of extracted scattering coefficients, Gaussian process regression was performed and a regression model was trained on three different sets of scattering coefficients with zero order scattering coefficients providing least prediction loss of 34.95%.

Cerebral Emboli Monitoring Using Transcranial Doppler Ultrasonography in Adults and Children: A Review of the Current Technology and Clinical Applications in the Perioperative and Intensive Care Setting

Transcranial Doppler (TCD) ultrasonography is the only noninvasive bedside technology for the detection and monitoring of cerebral embolism. TCD may identify patients at risk of acute and chronic neurologic injury from gaseous or solid emboli. Importantly, a window of opportunity for intervention-to eliminate the source of the emboli and thereby prevent subsequent development of a clinical or subclinical stroke-may be identified using TCD. In this review, we discuss the application of TCD sonography in the perioperative and intensive care setting in adults and children known to be at increased risk of cerebral embolism. The major challenge for evaluation of emboli, especially in children, is the need to establish the ground truth and define true emboli identified by TCD. This requires the development and validation of a predictive TCD emboli monitoring technique so that appropriately designed clinical studies intended to identify specific modifiable factors and develop potential strategies to reduce pathologic cerebral embolic burden can be performed.