Manual bone drilling in orthopedic surgical operations may cause injury to patient tissues if the drill bit continues to progress after exiting the bone. In this study, a new bone breakthrough detection algorithm based on acoustic emission (AE) signal analysis has been developed to minimize temporary and permanent injuries that can be caused by surgeon-controlled surgical drills. Three parametric estimation methods, Burg, Yule–Walker and Modified Covariance were used to estimate Power Spectral Density (PSD) of the AE signal during the drilling operation. Four frequency features, Mean Frequency, Median Frequency, Mean–Median and Power Bandwidth were calculated for each PSD estimate. An artificial neural network-based breakthrough detection classification was constructed from the extracted features. The highest breakthrough detection performance was obtained with the features extracted by the Burg method with an accuracy rate of 90.95 ± 0.97% in the training phase and 92.37 ± 1.09% in the test phase. In the detection of Not-Breakthrough situations, the highest accuracy was obtained with features extracted with the Covariance method as 99.04 ± 0.03% in the training phase and 99.05 ± 0.08% in the testing phase. This new approach which could be integrated into conventional drills with minimum configuration changes and without any major cost has the potential to increase the performance and safety of bone drilling procedures.

如果钻头在离开骨骼后继续前进，则在整形外科手术中进行手动骨骼钻孔可能会损坏患者组织。在这项研究中，已经开发了一种基于声发射（AE）信号分析的新的骨穿透检测算法，以最大程度地减少由外科医生控制的手术钻可能造成的暂时性和永久性伤害。三种参数估计方法，即Burg，Yule-Walker和Modified Covariance用于估计钻井作业过程中AE信号的功率谱密度（PSD）。为每个PSD估计值计算了四个频率特征，即平均频率，中频，中位数和功率带宽。从提取的特征构建了基于人工神经网络的突破检测分类。通过Burg方法提取的特征获得了最高的突破检测性能，在训练阶段的准确率为90.95±0.97％，在测试阶段的准确率为92.37±1.09％。在检测非突破情况时，使用协方差方法提取的特征在训练阶段的准确性为99.04±0.03％，而在测试阶段的准确性为99.05±0.08％。这种新方法可以以最小的配置变化集成到常规钻机中，而无需花费任何大笔成本，因此有可能提高骨骼钻削程序的性能和安全性。使用协方差方法提取的特征在训练阶段为99.04±0.03％，在测试阶段为99.05±0.08％，获得了最高的准确性。这种新方法可以以最小的配置变化集成到常规钻机中，而无需花费任何大笔成本，因此有可能提高骨骼钻削程序的性能和安全性。使用协方差方法提取的特征在训练阶段为99.04±0.03％，在测试阶段为99.05±0.08％，获得了最高的准确性。这种新的方法可以以最小的配置变化集成到常规钻机中，而无需花费任何大笔成本，因此有可能提高骨骼钻削程序的性能和安全性。