Department of Electronic and Information, Zhongyuan University of Technology, Zhengzhou, China.
Keck Data Science Institute, Pepperdine University, Malibu, USA.
Biomed Eng Online. 2024 Jun 24;23(1):61. doi: 10.1186/s12938-024-01245-9.
The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms.
Peak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise.
The quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data.
While machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting.
监测和分析心电图（ECG）、颅内压（ICP）和脑血流速度（CBFV）等准周期性生物信号对于早期发现患者不良事件和改善重症监护病房（ICU）的护理管理至关重要。本工作定量评估了现有的用于自动提取 ICP 波形内峰值的计算框架。
基于最先进的机器学习模型的峰值检测技术在抗噪能力方面进行了评估。评估是在从 64 名神经外科患者的 700 小时监测中组装的 ICP 信号数据集上进行的。在 13611 个脉冲的子集中手动建立了峰值位置的真值。使用具有受控时间动态和噪声的模拟 ICP 数据集进行了额外的评估。
应用于单个波形的峰值检测算法的定量分析表明，大多数技术在没有噪声的情况下提供可接受的准确度，平均绝对误差（MAE）为 ms。然而，在存在更高噪声水平的情况下，只有核谱回归和随机森林仍然低于该误差阈值，而其他技术的性能则恶化。我们的实验还表明，贝叶斯推理和长短期记忆（LSTM）等跟踪方法可以连续应用，并在单脉冲分析方法失败的情况下（例如数据丢失）提供额外的鲁棒性。
虽然基于机器学习的峰值检测方法需要手动标记数据进行训练，但这些模型优于基于手工规则的传统信号处理模型，应考虑在现代框架中用于峰值检测。特别是，在我们的实验中，在信号的连续周期之间纳入时间信息的峰值跟踪方法已被证明对噪声和临时伪影具有更高的鲁棒性，这些伪影通常作为临床监测设置的一部分出现。
