Objective: To enable reliable cerebral embolic load monitoring from high-intensity transient signals (HITS) recorded with single-channel transcranial Doppler (TCD) ultrasound. Methods: We propose a HITS detection and characterization method using a weighted-frequency Fourier linear combiner that estimates baseline Doppler signal power. An adaptive threshold is determined by examining the Doppler signal power variance about the baseline estimate, and HITS are extracted if their Doppler power exceeds this threshold. As signatures from multiple emboli may be superimposed, we analyze the detected HITS in the time-frequency (TF) domain to segment the signals into individual emboli. A logistic regression classification approach is employed to classify HITS into emboli or artifacts. Data were collected using a commercial TCD device with emboli-detection capabilities from 12 children undergoing mechanical circulatory support or cardiac catheterization. A subset of 696 HITS were reviewed, annotated, and split into training and testing sets for developing and evaluating the HITS classification algorithm. Results: The classifier yielded 98% and 96% sensitivity for 100% specificity on training and testing data, respectively. The TF approach decomposed 38% of candidate embolic signals into two or more embolic events that ultimately account for 69% of the overall embolic counts. Our processing pipeline resulted in highly accurate emboli identification and produced emboli counts that were lower (by a median of 64%) compared to the commercial ultrasound system's estimates. Significance: Using only single-channel, single-frequency Doppler ultrasound, the proposed method enables sensitive detection and segmentation of embolic signatures. Our approach paves the way toward accurate real-time cerebral emboli monitoring.

中文翻译：

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脑栓塞负荷监测的时频方法

目的：通过单通道经颅多普勒 (TCD) 超声记录的高强度瞬态信号 (HITS) 实现可靠的脑栓塞负荷监测。方法：我们提出了一种使用加权频率傅立叶线性组合器来估计基线多普勒信号功率的 HITS 检测和表征方法。通过检查关于基线估计的多普勒信号功率方差来确定自适应阈值，如果它们的多普勒功率超过该阈值，则提取 HITS。由于来自多个栓子的特征可能会叠加，我们在时频 (TF) 域中分析检测到的 HITS 以将信号分割成单个栓子。采用逻辑回归分类方法将 HITS 分类为栓子或伪影。使用具有栓子检测功能的商用 TCD 设备收集了 12 名接受机械循环支持或心导管插入术的儿童的数据。审查、注释了 696 个 HITS 的子集，并将其拆分为训练和测试集，以开发和评估 HITS 分类算法。结果：分类器对训练和测试数据的 100% 特异性分别产生了 98% 和 96% 的灵敏度。TF 方法将 38% 的候选栓塞信号分解为两个或更多栓塞事件，最终占总栓塞计数的 69%。与商业超声系统的估计值相比，我们的处理管道实现了高度准确的栓塞识别，并产生了较低的栓塞计数（中位数为 64%）。意义：仅使用单通道，单频多普勒超声，所提出的方法能够灵敏地检测和分割栓塞特征。我们的方法为准确的实时脑栓塞监测铺平了道路。