Josef Parvizi1, Kapil Gururangan2, Babak Razavi1, Chris Chafe3. 1. Department of Neurology and Neurological Sciences, Stanford University Medical Center, Stanford, CA, USA. 2. Stanford University School of Medicine, Stanford, CA, USA. 3. Center for Computer Research in Music and Acoustics, Stanford University, Stanford, CA, USA.
Abstract
OBJECTIVE: The traditional approach to interpreting electroencephalograms (EEGs) requires physicians with formal training to visually assess the waveforms. This approach can be less practical in critical settings where a trained EEG specialist is not readily available to review the EEG and diagnose ongoing subclinical seizures, such as nonconvulsive status epilepticus. METHODS: We have developed a novel method by which EEG data are converted to sound in real time by letting the underlying electrophysiological signal modulate a voice tone that is in the audible range. Here, we explored whether individuals without any prior EEG training could listen to 15-second sonified EEG and determine whether the EEG represents seizures or nonseizure conditions. We selected 84 EEG samples to represent seizures (n = 7), seizure-like activity (n = 25), or nonperiodic, nonrhythmic activity (normal or focal/generalized slowing, n = 52). EEGs from single channels in the left and right hemispheres were then converted to sound files. After a 4-minute training video, medical students (n = 34) and nurses (n = 30) were asked to designate each audio sample as "seizure" or "nonseizure." We then compared their performance with that of EEG-trained neurologists (n = 12) and medical students (n = 29) who also diagnosed the same EEGs on visual display. RESULTS: Nonexperts listening to single-channel sonified EEGs detected seizures with remarkable sensitivity (students, 98% ± 5%; nurses, 95% ± 14%) compared to experts or nonexperts reviewing the same EEGs on visual display (neurologists, 88% ± 11%; students, 76% ± 19%). If the EEGs contained seizures or seizure-like activity, nonexperts listening to sonified EEGs rated them as seizures with high specificity (students, 85% ± 9%; nurses, 82% ± 12%) compared to experts or nonexperts viewing the EEGs visually (neurologists, 90% ± 7%; students, 65% ± 20%). SIGNIFICANCE: Our study confirms that individuals without EEG training can detect ongoing seizures or seizure-like rhythmic periodic patterns by listening to sonified EEG. Although sonification of EEG cannot replace the traditional approaches to EEG interpretation, it provides a meaningful triage tool for fast assessment of patients with suspected subclinical seizures.
OBJECTIVE: The traditional approach to interpreting electroencephalograms (EEGs) requires physicians with formal training to visually assess the waveforms. This approach can be less practical in critical settings where a trained EEG specialist is not readily available to review the EEG and diagnose ongoing subclinical seizures, such as nonconvulsive status epilepticus. METHODS: We have developed a novel method by which EEG data are converted to sound in real time by letting the underlying electrophysiological signal modulate a voice tone that is in the audible range. Here, we explored whether individuals without any prior EEG training could listen to 15-second sonified EEG and determine whether the EEG represents seizures or nonseizure conditions. We selected 84 EEG samples to represent seizures (n = 7), seizure-like activity (n = 25), or nonperiodic, nonrhythmic activity (normal or focal/generalized slowing, n = 52). EEGs from single channels in the left and right hemispheres were then converted to sound files. After a 4-minute training video, medical students (n = 34) and nurses (n = 30) were asked to designate each audio sample as "seizure" or "nonseizure." We then compared their performance with that of EEG-trained neurologists (n = 12) and medical students (n = 29) who also diagnosed the same EEGs on visual display. RESULTS: Nonexperts listening to single-channel sonified EEGs detected seizures with remarkable sensitivity (students, 98% ± 5%; nurses, 95% ± 14%) compared to experts or nonexperts reviewing the same EEGs on visual display (neurologists, 88% ± 11%; students, 76% ± 19%). If the EEGs contained seizures or seizure-like activity, nonexperts listening to sonified EEGs rated them as seizures with high specificity (students, 85% ± 9%; nurses, 82% ± 12%) compared to experts or nonexperts viewing the EEGs visually (neurologists, 90% ± 7%; students, 65% ± 20%). SIGNIFICANCE: Our study confirms that individuals without EEG training can detect ongoing seizures or seizure-like rhythmic periodic patterns by listening to sonified EEG. Although sonification of EEG cannot replace the traditional approaches to EEG interpretation, it provides a meaningful triage tool for fast assessment of patients with suspected subclinical seizures.
Authors: Juan A Piantino; Amber Lin; Madison Luther; Luis D Centeno; Cydni N Williams; Craig D Newgard Journal: J Child Adolesc Trauma Date: 2020-06-10
Authors: Paul M Vespa; DaiWai M Olson; Sayona John; Kyle S Hobbs; Kapil Gururangan; Kun Nie; Masoom J Desai; Matthew Markert; Josef Parvizi; Thomas P Bleck; Lawrence J Hirsch; M Brandon Westover Journal: Crit Care Med Date: 2020-09 Impact factor: 9.296
Authors: Baharan Kamousi; Suganya Karunakaran; Kapil Gururangan; Matthew Markert; Barbara Decker; Pouya Khankhanian; Laura Mainardi; James Quinn; Raymond Woo; Josef Parvizi Journal: Neurocrit Care Date: 2020-10-06 Impact factor: 3.210