Anticipatory detection of turning in humans for intuitive control of robotic mobility assistance

Many wearable lower-limb robots for walking assistance have been developed in recent years. However, it remains unclear how they can be commanded in an intuitive and efficient way by their user. In particular, providing robotic assistance to neurologically impaired individuals in turning remains a significant challenge. The control should be safe to the users and their environment, yet yield sufficient performance and enable natural human-machine interaction. Here, we propose using the head and trunk anticipatory behaviour in order to detect the intention to turn in a natural, non-intrusive way, and use it for triggering turning movement in a robot for walking assistance. We therefore study head and trunk orientation during locomotion of healthy adults, and investigate upper body anticipatory behaviour during turning. The collected walking and turning kinematics data are clustered using the k-means algorithm and cross-validation tests and k-nearest neighbours method are used to evaluate the performance of turning detection during locomotion. Tests with seven subjects exhibited accurate turning detection. Head anticipated turning by more than 400–500 ms in average across all subjects. Overall, the proposed method detected turning 300 ms after its initiation and 1230 ms before the turning movement was completed. Using head anticipatory behaviour enabled to detect turning faster by about 100 ms, compared to turning detection using only pelvis orientation measurements. Finally, it was demonstrated that the proposed turning detection can improve the quality of human–robot interaction by improving the control accuracy and transparency

Time-frequency strategies for increasing high frequency oscillation detectability in intracerebral EEG

International audienceBackground: High Frequency Oscillations (HFOs) are considered to be highly representative of brain tissues capable of producing epileptic seizures. The visual review of HFOs on intracerebral electroencephalography is time-consuming and tedious, and it can be improved by time-frequency (TF) analysis. The main issue is that the signal is dominated by lower frequencies that mask the HFOs. Our aim was to flatten (i.e. whiten) the frequency spectrum to enhance the fast oscillations while preserving an optimal Signal to Noise Ratio (SNR). Method: We investigated 8 methods of data whitening based on either prewhitening or TF normalization in order to improve the detectability of HFOs. We detected all local maxima of the TF image above a range of thresholds in the HFO band. Results: We obtained the Precision and Recall curves at different SNR and for different HFO types and illustrate the added value of whitening both in the time-frequency plane and in time domain. Conclusion: The normalization strategies based on a baseline and on our proposed method (the " H0 z-score ") are more precise than the others. Significance: The H0 z-score provides an optimal framework for representing and detecting HFOs, independent of a baseline and a priori frequency bands

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

High frequency oscillations (HFOs) are a promising biomarker of epileptic tissue, but detection of these electrographic events remains a challenge. Automatic detectors show encouraging results, but they typically require optimization of multiple parameters, which is a barrier to good performance and broad applicability. We therefore propose a new automatic HFO detection algorithm, focusing on simplicity and ease of implementation. It requires tuning of only an amplitude threshold, which can be determined by an iterative process or directly calculated from statistics of the rectified filtered data (i.e. mean plus standard deviation). The iterative approach uses an estimate of the amplitude probability distribution of the background activity to calculate the optimum threshold for identification of transient high amplitude events. We tested both the iterative and non-iterative approaches using a dataset of visually marked HFOs, and we compared the performance to a commonly used detector based on the root-mean-square. When the threshold was optimized for individual channels via ROC curve, all three methods were comparable. The iterative detector achieved a sensitivity of 99.6%, false positive rate (FPR) of 1.1%, and false detection rate (FDR) of 37.3%. However, in an eight-fold cross-validation test, the iterative method had better sensitivity than the other two methods (80.0% compared to 64.4 and 65.8%), with FPR and FDR of 1.3, and 49.4%, respectively. The simplicity of this algorithm, with only a single parameter, will enable consistent application of automatic detection across research centers and recording modalities, and it may therefore be a powerful tool for the assessment and localization of epileptic activity

HFOs in iEEG are not better predictors of epileptogenicity than spikes

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Caractérisation du rôle des oscillations à haute fréquence dans les réseaux épileptiques

Epilepsy is a major health problem as it affects 50 million people worldwide. One third of the patients are resistant to medication. Surgical removal of the brain areas generating the seizure – the epileptogenic zone – is considered as the standard option for these patients to be seizure free. The non-negligible rate of surgical failure has led to seek other electrophysiological criteria. One putative marker is the high-frequency oscillations (HFOs). An HFO is a brief oscillation between 80-500 Hz lasting at least 4 periods recorded in intracerebral EEG. Due to their short-lasting nature, visually marking of these small oscillations is tedious and time-consuming. Automatically detecting these oscillations seems an imperative stage to study HFOs on cohorts of patients. There is however no general agreement on existing detectors. In this thesis, we developed a new way of representing HFOs thanks to a novel normalisation of the wavelet transform and to use this representation as a base for detecting HFOs automatically. We secondly designed a strategy to properly characterise and validate automated detectors. Finally, we characterised, in a cohort of patients, the reliability of HFOs and epileptic spikes - the standard marker - as predictors of the epileptogenic zone using the validated detector. The conclusion of this thesis is that HFOs are not better than epileptic spikes in predicting the epileptogenic zone but combining the two leads to a more robust biomarker.Touchant plus de 50 millions de personnes dans le monde, l’épilepsie est un problème majeur de santé publique. Un tiers des patients souffrent d’épilepsie pharmaco-résistante. Une chirurgie visant à enlever la région cérébrale à l’origine des crises – la zone épileptogène – est considérée comme l’option de référence pour rendre libre de crises ces patients. Le taux d’échec chirurgical non négligeable a poussé la recherche d’autres marqueurs. Un marqueur potentiel est les oscillations à haute fréquence (HFOs). Une HFO est une brève oscillation entre 80-500 Hz qui dure au moins 4 périodes enregistrée en EEG intracérébrale. Par leur caractère très bref, le marquage visuel de ces petites oscillations est fastidieux et chronophage.. Il semble impératif de trouver un moyen de détecter automatiquement ces oscillations pour étudier les HFOs sur des cohortes de patients. Aucun détecteur automatique existant ne fait cependant l’unanimité. Durant cette thèse, nous avons développé un nouveau moyen de visualiser les HFOs grâce à une normalisation originale de la transformée en ondelettes pour ensuite mieux les détecter automatiquement. Puis, nous avons mise en place une stratégie pour caractériser et valider des détecteurs. Enfin, nous avons appliqué le nouveau détecteur à une cohorte de patients pour déterminer la fiabilité des HFOs et des pointes épileptiques - le marqueur standard - dans la prédiction de la zone épileptogène. La conclusion de cette thèse est que les HFOs ne sont pas meilleurs que les pointes épileptiques pour prédire la zone épileptogène mais que combiner ces deux marqueurs permettait d’obtenir un marqueur plus robuste

Characterization of the role of high-frequency oscillations in epileptic networks

Touchant plus de 50 millions de personnes dans le monde, l’épilepsie est un problème majeur de santé publique. Un tiers des patients souffrent d’épilepsie pharmaco-résistante. Une chirurgie visant à enlever la région cérébrale à l’origine des crises – la zone épileptogène – est considérée comme l’option de référence pour rendre libre de crises ces patients. Le taux d’échec chirurgical non négligeable a poussé la recherche d’autres marqueurs. Un marqueur potentiel est les oscillations à haute fréquence (HFOs). Une HFO est une brève oscillation entre 80-500 Hz qui dure au moins 4 périodes enregistrée en EEG intracérébrale. Par leur caractère très bref, le marquage visuel de ces petites oscillations est fastidieux et chronophage.. Il semble impératif de trouver un moyen de détecter automatiquement ces oscillations pour étudier les HFOs sur des cohortes de patients. Aucun détecteur automatique existant ne fait cependant l’unanimité. Durant cette thèse, nous avons développé un nouveau moyen de visualiser les HFOs grâce à une normalisation originale de la transformée en ondelettes pour ensuite mieux les détecter automatiquement. Puis, nous avons mise en place une stratégie pour caractériser et valider des détecteurs. Enfin, nous avons appliqué le nouveau détecteur à une cohorte de patients pour déterminer la fiabilité des HFOs et des pointes épileptiques - le marqueur standard - dans la prédiction de la zone épileptogène. La conclusion de cette thèse est que les HFOs ne sont pas meilleurs que les pointes épileptiques pour prédire la zone épileptogène mais que combiner ces deux marqueurs permettait d’obtenir un marqueur plus robuste.Epilepsy is a major health problem as it affects 50 million people worldwide. One third of the patients are resistant to medication. Surgical removal of the brain areas generating the seizure – the epileptogenic zone – is considered as the standard option for these patients to be seizure free. The non-negligible rate of surgical failure has led to seek other electrophysiological criteria. One putative marker is the high-frequency oscillations (HFOs).An HFO is a brief oscillation between 80-500 Hz lasting at least 4 periods recorded in intracerebral EEG. Due to their short-lasting nature, visually marking of these small oscillations is tedious and time-consuming. Automatically detecting these oscillations seems an imperative stage to study HFOs on cohorts of patients. There is however no general agreement on existing detectors.In this thesis, we developed a new way of representing HFOs thanks to a novel normalisation of the wavelet transform and to use this representation as a base for detecting HFOs automatically. We secondly designed a strategy to properly characterise and validate automated detectors. Finally, we characterised, in a cohort of patients, the reliability of HFOs and epileptic spikes - the standard marker - as predictors of the epileptogenic zone using the validated detector. The conclusion of this thesis is that HFOs are not better than epileptic spikes in predicting the epileptogenic zone but combining the two leads to a more robust biomarker

Caractérisation du rôle des oscillations à haute fréquence dans les réseaux épileptiques

Epilepsy is a major health problem as it affects 50 million people worldwide. One third of the patients are resistant to medication. Surgical removal of the brain areas generating the seizure – the epileptogenic zone – is considered as the standard option for these patients to be seizure free. The non-negligible rate of surgical failure has led to seek other electrophysiological criteria. One putative marker is the high-frequency oscillations (HFOs). An HFO is a brief oscillation between 80-500 Hz lasting at least 4 periods recorded in intracerebral EEG. Due to their short-lasting nature, visually marking of these small oscillations is tedious and time-consuming. Automatically detecting these oscillations seems an imperative stage to study HFOs on cohorts of patients. There is however no general agreement on existing detectors. In this thesis, we developed a new way of representing HFOs thanks to a novel normalisation of the wavelet transform and to use this representation as a base for detecting HFOs automatically. We secondly designed a strategy to properly characterise and validate automated detectors. Finally, we characterised, in a cohort of patients, the reliability of HFOs and epileptic spikes - the standard marker - as predictors of the epileptogenic zone using the validated detector. The conclusion of this thesis is that HFOs are not better than epileptic spikes in predicting the epileptogenic zone but combining the two leads to a more robust biomarker.Touchant plus de 50 millions de personnes dans le monde, l’épilepsie est un problème majeur de santé publique. Un tiers des patients souffrent d’épilepsie pharmaco-résistante. Une chirurgie visant à enlever la région cérébrale à l’origine des crises – la zone épileptogène – est considérée comme l’option de référence pour rendre libre de crises ces patients. Le taux d’échec chirurgical non négligeable a poussé la recherche d’autres marqueurs. Un marqueur potentiel est les oscillations à haute fréquence (HFOs). Une HFO est une brève oscillation entre 80-500 Hz qui dure au moins 4 périodes enregistrée en EEG intracérébrale. Par leur caractère très bref, le marquage visuel de ces petites oscillations est fastidieux et chronophage.. Il semble impératif de trouver un moyen de détecter automatiquement ces oscillations pour étudier les HFOs sur des cohortes de patients. Aucun détecteur automatique existant ne fait cependant l’unanimité. Durant cette thèse, nous avons développé un nouveau moyen de visualiser les HFOs grâce à une normalisation originale de la transformée en ondelettes pour ensuite mieux les détecter automatiquement. Puis, nous avons mise en place une stratégie pour caractériser et valider des détecteurs. Enfin, nous avons appliqué le nouveau détecteur à une cohorte de patients pour déterminer la fiabilité des HFOs et des pointes épileptiques - le marqueur standard - dans la prédiction de la zone épileptogène. La conclusion de cette thèse est que les HFOs ne sont pas meilleurs que les pointes épileptiques pour prédire la zone épileptogène mais que combiner ces deux marqueurs permettait d’obtenir un marqueur plus robuste

Are high-frequency oscillations better biomarkers of the epileptogenic zone than spikes?

International audiencePurpose of review Precise localization of the epileptogenic zone is imperative for the success of resective surgery of drug-resistant epileptic patients. To decrease the number of surgical failures, clinical research has been focusing on finding new biomarkers. For the past decades, high-frequency oscillations (HFOs, 80-500Hz) have ousted interictal spikes-the classical interictal marker-from the research spotlight. Many studies have claimed that HFOs were more linked to epileptogenicity than spikes. This present review aims at refining this statement in light of recent studies. Recent findings Analysis based on single-patient characteristics has not been able to determine which of HFOs or spikes were better marker of epileptogenic tissues. Physiological HFOs are one of the main obstacles to translate HFOs to clinical practice as separating them from pathological HFOs remains a challenge. Fast ripples (a subgroup of HFOs, 250-500Hz) which are mostly pathological are not found in all epileptogenic tissues. Summary Quantified measures of HFOs and spikes give complementary results, but many barriers still persist in applying them in clinical routine. The current way of testing HFO and spike detectors and their performance in delineating the epileptogenic zone is debatable and still lacks practicality. Solutions to handle physiological HFOs have been proposed but are still at a preliminary stage

Delphos: Detector of ElectroPHysiological Oscillations and Spikes

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