Zakeri et al., 2017 - Google Patents
Classifying hard and soft bone tissues using drilling soundsZakeri et al., 2017
View PDF- Document ID
- 16331962395074202556
- Author
- Zakeri V
- Hodgson A
- Publication year
- Publication venue
- 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
External Links
Snippet
The purpose of this study was to investigate if the sounds generated during bone drilling could be used to classify between hard (cortical) and soft (cancellous) tissues. Bone drilling is performed in many surgical procedures throughout the world. Inadvertent deviation from …
- 210000000988 Bone and Bones 0 title abstract description 49
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B17/00—Surgical instruments, devices or methods, e.g. tourniquets
- A61B2017/00017—Electrical control of surgical instruments
- A61B2017/00022—Sensing or detecting at the treatment site
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | A unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis | |
| Zakeri et al. | Automatic identification of hard and soft bone tissues by analyzing drilling sounds | |
| Seibold et al. | Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery | |
| Illanes et al. | Novel clinical device tracking and tissue event characterization using proximally placed audio signal acquisition and processing | |
| US9980738B2 (en) | Surgical tool monitoring system and methods of use | |
| Dai et al. | Milling state identification based on vibration sense of a robotic surgical system | |
| Grønnesby et al. | Feature extraction for machine learning based crackle detection in lung sounds from a health survey | |
| US12025785B2 (en) | Medical-optical observation apparatus with opto-acoustic sensor fusion | |
| Zakeri et al. | Classifying hard and soft bone tissues using drilling sounds | |
| Lopez-de-Ipiña et al. | Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳ s disease | |
| Torre-Cruz et al. | A novel wheezing detection approach based on constrained non-negative matrix factorization | |
| Krohne et al. | Detection of K-complexes based on the wavelet transform | |
| JP6562450B2 (en) | Swallowing detection device, swallowing detection method and program | |
| Massalimova et al. | Automatic breach detection during spine pedicle drilling based on vibroacoustic sensing | |
| EP3189776A1 (en) | An apparatus and method for generating fetal heart rate data | |
| Torun et al. | Parametric power spectral density estimation-based breakthrough detection for orthopedic bone drilling with acoustic emission signal analysis | |
| Islam et al. | Robust covid-19 detection from cough sounds using deep neural decision tree and forest: A comprehensive cross-datasets evaluation | |
| Zakeri et al. | A machine-learning approach to discriminate between soft and hard bone tissues using drilling sounds | |
| CA3053996A1 (en) | Methods and devices using swallowing accelerometry signals for swallowing impairment detection | |
| Sabieleish et al. | Study of needle punctures into soft tissue through audio and force sensing: can audio be a simple alternative for needle guidance? | |
| CN113925612B (en) | Instrument control method and system | |
| Rossini et al. | Localization of drilling tool position through bone tissue identification during surgical drilling | |
| Li et al. | State sensing of spinal surgical robot based on fusion of sound and force signals | |
| Zaylaa et al. | Cascade of nonlinear entropy and statistics to discriminate fetal heart rates | |
| Dai et al. | Condition monitoring based on sound feature extraction during bone drilling process |