Schuyler et al., 2007 - Google Patents
Epileptic seizure detectionSchuyler et al., 2007
View PDF- Document ID
- 1193911129723299277
- Author
- Schuyler R
- White A
- Staley K
- Cios K
- Publication year
- Publication venue
- IEEE Engineering in medicine and Biology Magazine
External Links
Snippet
IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE MARCH/APRIL 2007 75 data, and only data from the most relevant intracranial probe were used. Separate networks were trained with raw data, wavelet approximation coefficients, and detail coefficients. As …
- 206010015037 Epilepsy 0 title description 5
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0476—Electroencephalography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0402—Electrocardiography, i.e. ECG
- A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
- A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0488—Electromyography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/04001—Detecting, measuring or recording bioelectric signals of the body of parts thereof adapted to neuroelectric signals, e.g. nerve impulses
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B17/00—Surgical instruments, devices or methods, e.g. tourniquets
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Schuyler et al. | Epileptic seizure detection | |
| Vidyaratne et al. | Real-time epileptic seizure detection using EEG | |
| Tzallas et al. | Automatic seizure detection based on time‐frequency analysis and artificial neural networks | |
| Tawfik et al. | A hybrid automated detection of epileptic seizures in EEG records | |
| Zanos et al. | Relationships between spike-free local field potentials and spike timing in human temporal cortex | |
| Tzallas et al. | Automated epileptic seizure detection methods: a review study | |
| Andrzejak et al. | The epileptic process as nonlinear deterministic dynamics in a stochastic environment: an evaluation on mesial temporal lobe epilepsy | |
| Shoeb et al. | Patient-specific seizure onset detection | |
| US5743860A (en) | Apparatus and method for epileptic seizure detection using non-linear techniques | |
| Mahmud et al. | Automatic detection of opioid intake using wearable biosensor | |
| WO1997034524A9 (en) | Epileptic seizure detection by nonlinear methods | |
| Duma et al. | Altered spreading of neuronal avalanches in temporal lobe epilepsy relates to cognitive performance: A resting‐state hdEEG study | |
| Yadav et al. | Morphology-based automatic seizure detector for intracerebral EEG recordings | |
| Carey et al. | Epileptic spike detection with EEG using artificial neural networks | |
| Sriraam et al. | Multichannel EEG based inter-ictal seizures detection using Teager energy with backpropagation neural network classifier | |
| Pal et al. | A multi scale time–frequency analysis on electroencephalogram signals | |
| Kamath | Teager Energy Based Filter‐Bank Cepstra in EEG Classification for Seizure Detection Using Radial Basis Function Neural Network | |
| Ieracitano et al. | Wavelet coherence-based clustering of EEG signals to estimate the brain connectivity in absence epileptic patients | |
| Kamath | A new approach to detect epileptic seizures in electroencephalograms using teager energy | |
| Abibullaev et al. | Seizure detection in temporal lobe epileptic EEGs using the best basis wavelet functions | |
| Ibrahim et al. | EEG seizure detection by integrating slantlet transform with sparse coding | |
| Herrmann et al. | Adaptive frequency decomposition of EEG with subsequent expert system analysis | |
| Ba-Karait et al. | EEG signals classification using a hybrid method based on negative selection and particle swarm optimization | |
| Park et al. | Detection of epileptiform activity using wavelet and neural network | |
| Chavan et al. | Optimal mother wavelet for EEG signal processing |