Mina Amiri1, Jean-Marc Lina2, Francesca Pizzo3, Jean Gotman4. 1. Montreal Neurological Institute, McGill University, Montréal, Québec, Canada. Electronic address: mina.amiri@mail.mcgill.ca. 2. École De Technologie Supérieure, Département de Génie Électrique, Montréal, Québec, Canada; Centre de Recherches Mathématiques, Montréal, Québec, Canada. 3. NEUROFARBA Department, University of Florence, Italy. 4. Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
Abstract
OBJECTIVE: To demonstrate and quantify the occurrence of false High Frequency Oscillations (HFOs) generated by the filtering of sharp events. To distinguish real HFOs from spurious ones using analysis of the raw signal. METHOD: We developed a new method to prevent false HFO detections due to the filtering effect by detecting oscillations in the raw signal at the time of sharp events. We specified temporal features to classify sharp events with and without HFOs using support vector machine in both ripple and fast ripple bands. The traditionally used time-frequency representation served as the gold standard to indicate real and false HFOs. RESULTS: 44% of ripples and 43% of FRs concurring with sharp events were found to be false HFOs. Sharp events with HFOs had significantly more oscillations in the raw signal than sharp events without. They could be distinguished from false HFOs with accuracy of 76.6% in the ripple band and 72.6% in the fast ripple band. CONCLUSION: It may be most appropriate to detect HFOs as oscillations not only on the filtered signal but also on the raw signal. The classical time-frequency display used for identifying HFOs should be used with great care due to the possible masking effect of broadband activities. SIGNIFICANCE: The separation of real HFOs from broadband activities will raise the validity of HFO detection methods and will therefore support future HFO investigations.
OBJECTIVE: To demonstrate and quantify the occurrence of false High Frequency Oscillations (HFOs) generated by the filtering of sharp events. To distinguish real HFOs from spurious ones using analysis of the raw signal. METHOD: We developed a new method to prevent false HFO detections due to the filtering effect by detecting oscillations in the raw signal at the time of sharp events. We specified temporal features to classify sharp events with and without HFOs using support vector machine in both ripple and fast ripple bands. The traditionally used time-frequency representation served as the gold standard to indicate real and false HFOs. RESULTS: 44% of ripples and 43% of FRs concurring with sharp events were found to be false HFOs. Sharp events with HFOs had significantly more oscillations in the raw signal than sharp events without. They could be distinguished from false HFOs with accuracy of 76.6% in the ripple band and 72.6% in the fast ripple band. CONCLUSION: It may be most appropriate to detect HFOs as oscillations not only on the filtered signal but also on the raw signal. The classical time-frequency display used for identifying HFOs should be used with great care due to the possible masking effect of broadband activities. SIGNIFICANCE: The separation of real HFOs from broadband activities will raise the validity of HFO detection methods and will therefore support future HFO investigations.
Authors: Nicolas Roehri; Jean-Marc Lina; John C Mosher; Fabrice Bartolomei; Christian-George Benar Journal: IEEE Trans Biomed Eng Date: 2016-12 Impact factor: 4.538
Authors: Cesar Santana-Gomez; Pedro Andrade; Matthew R Hudson; Tomi Paananen; Robert Ciszek; Gregory Smith; Idrish Ali; Brian K Rundle; Xavier Ekolle Ndode-Ekane; Pablo M Casillas-Espinosa; Riikka Immonen; Noora Puhakka; Nigel Jones; Rhys D Brady; Piero Perucca; Sandy R Shultz; Asla Pitkänen; Terence J O'Brien; Richard Staba Journal: Epilepsy Res Date: 2019-03-15 Impact factor: 3.045
Authors: Aljoscha Thomschewski; Nathalie Gerner; Patrick B Langthaler; Eugen Trinka; Arne C Bathke; Jürgen Fell; Yvonne Höller Journal: Front Neurol Date: 2020-10-19 Impact factor: 4.003
Authors: Christos Papadelis; Eleonora Tamilia; Steven Stufflebeam; Patricia E Grant; Joseph R Madsen; Phillip L Pearl; Naoaki Tanaka Journal: J Vis Exp Date: 2016-12-06 Impact factor: 1.355
Authors: Jessica K Nadalin; Uri T Eden; Xue Han; R Mark Richardson; Catherine J Chu; Mark A Kramer Journal: J Neurosci Methods Date: 2021-06-04 Impact factor: 2.987