Use of wavelet energy for spinal cord vibration analysis during spinal surgery

Proceedings of the 33rd Chinese Control Conference

2014

Self Cite

In order to enhance operation safety, this study analyzes the sound pressure signal obtained from the bone drilling process for condition monitoring. The drilling sound is caused by the contact between the drill bit and the bone, and we only pay attention to the harmonic components of the sound pressure signal whose frequencies are integer multiples of spindle frequency. Lifting wavelet transform is performed to extract the crucial characteristic from the sound pressure signal to correlate drilling condition. The product of standard deviation of wavelet coefficients is calculated to determine whether cortical or cancellous bone is drilled. The proposed method is experimentally verified through the drilling operation in in vitro porcine spines, and the results confirm the feasibility and achievable performance of the method and then the safety of the drilling operation is improved.

Section: Sound Pressure Signal Processing Using Wavelet Transformmentioning

confidence: 99%

Condition monitoring based on sound feature extraction during bone drilling process

Dai

Xue

Proceedings of the 33rd Chinese Control Conference

2014

Self Cite

“…Laser displacement sensor Dai et al [9], [ [46] measurement-based condition monitoring technique has three clear advantages: the signal-to-noise ratio (SNR) is high; the areas affected by the cutting vibration are usually large (not only the bone being cut but also its surrounding tissues), as well as stable measurements are enabled for any color or surface condition through the sensor, so the tissues in a large scope can be identified by analyzing their vibration signal; the sensor does not in contact with the tissue surface, and a simple and low-cost ultraviolet disinfection method is suited for this application. But the raw vibration signal cannot be directly used to control the robot, and it is necessary to propose an appropriate signal processing method to extract the crucial characteristic from the signal to correlate cutting condition.…”

Section: A Related Workmentioning

confidence: 99%

“…Cutting vibration, which is caused by the dynamic components of the cutting force, is another parameter that can provide some useful information about bone state [7], [8]. Direct measurement of tissue vibration signal has been realized by a laser displacement sensor based on triangulation [9]. Compared with the other existing methods, the noncontact vibration [22], [23], Colla and Allotta [18], Kaburlasos et al [24], Ong and Bouazza-Marouf [15], Federspil et al [10], Lee et al [16], [25], Coulson et al [26], Sugita et al [12], Sugita et al [27], Wang et al [11], Boiadjiev et al [28], Du et al [29], Deng et al [19], Hu et al [17], Jin et al [30] No physical sensor Kasahara et al [31], Osa et al [20] Vibration…”

Section: A Related Workmentioning

confidence: 99%

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Vibration-Based Milling Condition Monitoring in Robot-Assisted Spine Surgery

Dai

Xue

IEEE/ASME Trans. Mechatron.

2015

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In order to enhance the safety of the milling operation, we develop a condition monitoring method by means of analyzing the bone vibration signal in robot-assisted spine surgery. Since the behavior of the bone being cut changes with the tissue removal, an analytical method is proposed for modeling of varying bone dynamics, and then, it is proved that the vibration amplitude of the bone indicates its status change. During the real milling process, the vibration signal of the bone is recorded by a noncontact laser displacement sensor, and we only pay attention to the harmonic component whose frequency is an integer times of the spindle frequency of the milling device. The wavelet packet transform and the adaptive linear element are performed to estimate the harmonic amplitude from the signal to correlate milling condition. Considering that the bone vibration also varies with the milling force, the relative magnitude of the force is measured from the wavelet coefficients in low-frequency subband, and subsequently, the estimated harmonic amplitude is compensated to reduce the influence of the force. The robot milling experiments in sheep spines are carried on to verify the effectiveness of the condition monitoring procedure. The experimental results are in substantial accordance with the conclusions from the dynamic model, and thus, the robot can improve the safety of the operation with the proposed method.

“…and industrial fields [1,6,22]. Next, we proposed to use the biogeography-based optimization (BBO) method to train the classifier, with the aim of improving its classification performance.…”

mentioning

confidence: 99%

Automated classification of brain images using wavelet-energy and biogeography-based optimization

Yang

Yang

et al. 2015

Multimed Tools Appl

155

It is very important to early detect abnormal brains, in order to save social and hospital resources. The wavelet-energy was a successful feature descriptor that achieved excellent performances in various applications; hence, we proposed a novel wavelet-energy based approach for automated classification of MR brain images as normal or abnormal. SVM was used as the classifier, and biogeography-based optimization (BBO) was introduced to optimize the weights of the SVM. The results based on a 5×5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO-KSVM in terms of sensitivity and accuracy. The study offered a new means to detect abnormal brains with excellent performance.