1,721,095 research outputs found
Hyperdimensional Computing with Local Binary Patterns: One-shot Learning for Seizure Onset Detection and Identification of Ictogenic Brain Regions from Short-time iEEG Recordings.
Full text linkOBJECTIVE
We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG).
METHODS
Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them.
RESULTS
We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36 to 100 electrodes. For the majority of the patients (10 out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using k-fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% vs. 94.77%) and macroaveraging accuracy (95.42% vs. 94.96%), and 74x lower memory footprint, but slightly higher average latency in detection (15.9 s vs. 14.7 s). Moreover, the algorithm can reliably identify (with a p-value < 0.01) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes.
CONCLUSION AND SIGNIFICANCE
Our algorithm provides: (1) a unified method for both learning and classification tasks with end-to-end binary operations; (2) one-shot learning from seizure examples; (3) linear computational scalability for increasing number of electrodes; (4) generation of transparent codes that enables post-translational supports for clinical decision making
An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection with Automatic iEEG Electrode Selection.
Full text linkWe propose an intracranial electroencephalography (iEEG) based algorithm for detecting epileptic seizures with short latency, and with identifying the most relevant electrodes. Our algorithm first extracts three features, namely mean amplitude, line length, and local binary patterns that are fed to an ensemble of classifiers using hyperdimensional (HD) computing. These features are embedded into an HD space where well-defined vector-space operations are used to construct prototype vectors representing ictal (during seizures) and interictal (between seizures) brain states. Prototype vectors can be computed at different spatial scales ranging from a single electrode up to many electrodes covering different brain regions. This flexibility allows our algorithm to identify the iEEG electrodes that discriminate best between ictal and interictal brain states. We assess our algorithm on the SWEC-ETHZ iEEG dataset that includes 99 short-time iEEG seizures recorded with 36 to 100 electrodes from 16 drug-resistant epilepsy patients. Using k-fold cross-validation and all electrodes, our algorithm surpasses state-of-the-art algorithms yielding significantly shorter latency (8.81 s vs. 9.94 s) in seizure onset detection, and higher sensitivity (96.38 % vs. 92.72 %) and accuracy (96.85 % vs. 95.43 %). We can further reduce the latency of our algorithm to 3.74 s by allowing a slightly higher percentage of false alarms (2 % specificity loss). Using only the top 10 % of the electrodes ranked by our algorithm, we still maintain superior latency, sensitivity, and specificity compared to the other algorithms with all the electrodes. We finally demonstrate the suitability of our algorithm to deployment on low-cost embedded hardware platforms, thanks to its robustness to noise/artifacts affecting the signal, its low computational complexity, and the small memory-footprint on a RISC-V microcontroller
Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms
Full text linkWe propose Laelaps, an energy-efficient and fast learning algorithm with no false alarms for epileptic seizure detection from long-term intracranial electroencephalography (iEEG) signals. Laelaps uses end-to-end binary operations by exploiting symbolic dynamics and brain-inspired hyperdimensional computing. Laelaps's results surpass those yielded by state-of-the-art (SoA) methods [1], [2], [3], including deep learning, on a new very large dataset containing 116 seizures of 18 drug-resistant epilepsy patients in 2656 hours of recordings each patient implanted with 24 to 128 iEEG electrodes. Laelaps trains 18 patient-specific models by using only 24 seizures: 12 models are trained with one seizure per patient, the others with two seizures. The trained models detect 79 out of 92 unseen seizures without any false alarms across all the patients as a big step forward in practical seizure detection. Importantly, a simple implementation of Laelaps on the Nvidia Tegra X2 embedded device achieves 1.7x 3.9 x faster execution and 1.4 x 2.9x lower energy consumption compared to the best result from the SoA methods. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing
Full text linkThis paper presents an efficient binarized algorithm for both learning and classification of human epileptic seizures from intracranial electroencephalography (iEEG). The algorithm combines local binary patterns with brain-inspired hyperdimensional computing to enable end-to-end learning and inference with binary operations. The algorithm first transforms iEEG time series from each electrode into local binary pattern codes. Then atomic high-dimensional binary vectors are used to construct composite representations of seizures across all electrodes. For the majority of our patients (10 out of 16), the algorithm quickly learns from one or two seizures (i.e., one-/few-shot learning) and perfectly generalizes on 27 further seizures. For other patients, the algorithm requires three to six seizures for learning. Overall, our algorithm surpasses the state-of-the-art methods [1] for detecting 65 novel seizures with higher specificity and sensitivity, and lower memory footprint
Intrakranielle EEG Netzwerkanalysen bei fokalen Epilepsien
No full textZusammenfassungDie Theorie der Netzwerke hat sich zu einem bedeutsamen Werkzeug bei der Analyse komplexer Systeme entwickelt und ihre Methoden werden erfolgreich angewendet, um die Dynamik epileptischer Anfälle zu verstehen. In diesem Artikel wird eine nicht-technische Einführung in die Konzepte der Netzwerktheorie gegeben. Ausgehend von intrakraniellen EEG Signalen wird demonstriert wie daraus funktionale Netzwerke hergeleitet werden können und wie sich diese Netzwerke visualisieren und analysieren lassen. Wichtige Begriffe wie „Knoten“, „Verbindung“ und „Knoten-Zentralität“ werden erklärt und ein experimentell prüfbares Modell der Netzwerkdynamik fokaler Anfälle wird vorgestellt. Dieses Modell impliziert, dass die hierarchische und modulare Netzwerkstruktur unseres Gehirns dazu prädestiniert, dass lokale neurogliale Aktivität unkontrollierbar wird. Die „Neuro-Netzwerkwissenschaft“ dürfte in naher Zukunft zu besserer Diagnostik und Therapie für Epilepsiepatienten führen.</jats:p
VSA-based Positional Encoding Can Replace Recurrent Networks in Emergent Symbol Binding
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Pharmakotherapieresistene Epilepsien. Epilepsiechirurgie: eine wichtige Option für betroffene Patienten
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