背景:睡眠呼吸暂停(SLA)是一种常见的睡眠障碍,其特征是睡眠时反复停止呼吸。在过去的几年里,研究人员专注于开发不太复杂、更具成本效益的诊断方法来识别SLA接受者,与繁琐的相反,复杂,和昂贵的常规方法。
方法:本研究提出了具有时间编码的尖峰神经网络(SNN)的生物学似然学习方法,以及使用单导联心电图(ECG)数据信息诊断SLA疾病的临时学习模型。所提出的框架利用时间编码和泄漏积分和火灾模型将ECG信号转换为尖峰,以捕获信号的动态模式性质并模拟输入响应行为。tempoton学习技术,基于尖峰的算法,训练SNN模型以从编码的输出尖峰序列中识别SLA事件模式。这项研究利用ECG数据从ECG记录的1分钟段数据中提取心率变异性(HRV)和ECG衍生呼吸(EDR)信号,以输入SNN模型。来自Physionet的呼吸暂停-ECG数据库的已发布和保留数据的35个记录已用于训练SNN模型并验证该模型在识别SLA发生方面的功效。
结果:与其他SLA检测技术相比,所提出的方法表现出重大改进,每段检测的准确率达到94.63%,连同特异性,灵敏度,F1评分和AUC值为96.21%,92.04%,分别为0.9285和0.9851。每次记录检测的准确性达到100%,相关系数值为0.986。此外,实验使用UCD数据作为验证方法,达到84.573%的精度。
结论:这些结果表明了所提出的准确识别SLA病例的方法的有效性和可及性。与基于特征工程和特征学习的各种技术相比,建议的模型增强了SLA检测的性能。
BACKGROUND: Sleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the past few years, researchers have focused on developing less complex and more cost-effective diagnostic approaches for identifying SLA recipients, in contrast to the cumbersome, complicated, and expensive conventional methods.
METHODS: This study presents a biologically plausible learning approach of spiking neural networks (SNN) with temporal coding and a tempotron learning model for diagnosing SLA disorder using single-lead electrocardiogram (ECG) data information. The proposed framework utilizes temporal encoding and the leaky integrate and fire model to transform the ECG signal into spikes for capturing the signal\'s dynamic pattern nature and to simulate input response behaviors. The tempoton learning technique, a spike-based algorithm, trains the SNN model to identify SLA event patterns from encoded output spike trains. This study utilized ECG data to extract heart rate variability (HRV) and ECG-derived respiration (EDR) signals from 1-min segment data of ECG records for input to SNN model. Thirty-five recordings of both released and withheld data from the Apnea-ECG databases from Physionet have been applied to train the SNN model and validate the model\'s efficacy in identifying SLA occurrences.
RESULTS: The proposed method demonstrated substantial improvements compared to other SLA detection techniques, achieving a significant accuracy of 94.63 % for per-segment detection, along with specificity, sensitivity, F1-score and AUC values of 96.21 %, 92.04 %, 0.9285, and 0.9851 respectively. The accuracy for per-recording detection achieved 100 %, with a correlation coefficient value of 0.986. Additionally, the experiment used UCD data for validation methods, achieving an accuracy of 84.573 %.
CONCLUSIONS: These results suggest the effectiveness and accessibility of the presented approach for accurately identifying SLA cases. The suggested model enhances the performance of SLA detection when contrasted with various techniques based on feature engineering and feature learning.