Using Automated Detection and Classification of Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery

Farahmand, Sina; Sobayo, Tiwalade; Mogul, David J.

doi:10.1101/680280

Cited by 2 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Presurgical diagnostic workup at Schweizerische Epilepsie-Klinik included recording of iEEG from the medial temporal lobe. The iEEG data set has been analysed for HFO with state-of-the-art HFO detectors 23,24 .…”

Section: Patient Datamentioning

confidence: 99%

“…Selected patients with focal epilepsy can achieve seizure freedom if the epileptogenic zone (EZ), which is the brain area generating the seizures, is correctly identified and surgically removed in its entirety. Presurgical and intraoperative measurement of iEEG signals is often needed to identify the EZ precisely [20][21][22][23] . High Frequency Oscillations (HFOs) have been proposed as a new biomarker in iEEG to delineate the EZ 17,18,[24][25][26][27][28] While HFOs have been historically divided into "ripples" (80-250 Hz) and "fast ripples" (FR, 250-500 Hz), detection of their co-occurrence was shown to enable the optimal prediction of postsurgical seizure freedom 24 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

Sharifhazileh

Burelo

Sarnthein³

et al. 2020

Preprint

View full text Add to dashboard Cite

The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from compact and portable devices that can process these signals locally, in real-time, without the need for off-line processing. An example is the recording of intracranial EEG(iEEG) during epilepsy surgery with the detection of High Frequency Oscillations (HFOs, 80-500 Hz), which are a biomarker for the epileptogenic zone. Conventional approaches of HFO detection involve the offline analysis of prerecorded data, often on bulky computers. However, clinical applications during surgery or in long-term intracranial recordings demand a self-sufficient embedded device that is battery-powered to avoid interfering with other electronic equipment in the operation room. Mixed-signal and analog-digital neuromorphic circuits offer the possibility of building compact, embedded, and low-power neural network processing systems that can analyze data on-line and produce results with short latency in real-time. These characteristics are well suited for clinical applications that involve the processing of biomedical signals at (or very close to) the sensor level. In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network (SNN) architecture on the same die for recording, processing, and detecting clinically relevant HFOs in iEEG from epilepsy patients. The device was fabricated using a standard 0.18μm CMOS technology node and has a total area of 99 mm2. We demonstrate its application to HFO detection in the iEEG recorded from 9 patients with temporal lobe epilepsy who subsequently underwent epilepsy surgery. The total average power consumption of the chip during the detection task was 614.3 μW. We show how the neuromorphic system can reliably detect HFOs: the system predicts postsurgical seizure outcome with state-of-the-art accuracy, specificity, and sensitivity (78%, 100%, and 33% respectively). This is the first feasibility study towards identifying relevant features in intracranial human data in real-time, on-chip, using event-based processors and spiking neural networks. By providing “neuromorphic intelligence” to neural recording circuits the approach proposed will pave the way for the development of systems that can detect HFO areas directly in the operation room and improve the seizure outcome of epilepsy surgery.

show abstract

Section: Patient Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

Sharifhazileh

Burelo

Sarnthein³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Section: Patient Datamentioning

confidence: 99%

“…Selected patients with focal epilepsy can achieve seizure freedom if the epileptogenic zone (EZ), which is the brain area generating the seizures, is correctly identified and surgically removed in its entirety. Presurgical and intraoperative measurement of iEEG signals is often needed to identify the EZ precisely [18][19][20][21] . High Frequency Oscillations (HFOs) have been proposed as a new biomarker in iEEG to delineate the EZ 16,17,[22][23][24][25][26] While HFOs have been historically divided into "ripples" (80-250 Hz) and "fast ripples" (FR, 250-500 Hz), detection of their co-occurrence was shown to enable the optimal prediction of postsurgical seizure freedom 22 .…”

Section: Introductionmentioning

confidence: 99%

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

Sharifshazileh,

Burelo,

Sarnthein

et al. 2020

Preprint

View full text Add to dashboard Cite

The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from compact and portable devices that can process these signals locally, in real-time, without the need for off-line processing. An example is the recording of intracranial EEG (iEEG) during epilepsy surgery with the detection of High Frequency Oscillations (HFOs, 80-500 Hz), which are a biomarker for the epileptogenic zone. Conventional approaches of HFO detection involve the offline analysis of prerecorded data, often on bulky computers. However, clinical applications during surgery or in long-term intracranial recordings demand a self-sufficient embedded device that is battery-powered to avoid interfering with other electronic equipment in the operation room. Mixed-signal and analog-digital neuromorphic circuits offer the possibility of building compact, embedded and low-power neural network processing systems that can analyze data on-line and produce results with short latency in real-time. These characteristics are well suited for clinical applications that involve the processing of biomedical signals at (or very close to) the sensor level. In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network (SNN) architecture on the same die for recording, processing, and detecting clinically relevant HFOs in iEEG from epilepsy patients.The device was fabricated using a standard 0.18 µm CMOS technology node and has a total area of 99 mm 2 . We demonstrate its application to HFO detection in the iEEG recorded from 9 patients with temporal lobe epilepsy who subsequently underwent epilepsy surgery. The total average power consumption of the chip during the detection task was 614.3 µW. We show how the neuromorphic system can reliably detect HFOs: the system predicts postsurgical seizure outcome with state-of-the-art accuracy, specificity and sensitivity (78%, 100%, and 33% respectively). This is the first feasibility study towards identifying relevant features in intracranial human data in real-time, on-chip, using event-based processors and spiking neural networks. By providing "neuromorphic intelligence" to neural recording circuits the approach proposed will pave the way for the development of systems that can detect HFO areas directly in the operation room and improve the seizure outcome of epilepsy surgery.

show abstract

Using Automated Detection and Classification of Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery

Cited by 2 publications

References 30 publications

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

Contact Info

Product

Resources

About