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

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 High Frequency Oscillations (HFO), which are biomarkers for the epileptogenic zone. The device was fabricated using a standard 0.18$\mu$m CMOS technology node and has a total area of 99mm$^{2}$. 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$\mu$W. 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.Comment: 16 pages. A short video describing the rationale underlying the study can be viewed on https://youtu.be/NuAA91fdma

A Spiking Neural Network (SNN) for detecting High Frequency Oscillations (HFOs) in the intraoperative ECoG

To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative ECoG recordings, high frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. However, automatic detection of HFOs in real-time remains an open challenge. Here we present a spiking neural network (SNN) for automatic HFO detection that is optimally suited for neuromorphic hardware implementation. We trained the SNN to detect HFO signals measured from intraoperative ECoG on-line, using an independently labeled dataset. We targeted the detection of HFOs in the fast ripple frequency range (250-500 Hz) and compared the network results with the labeled HFO data. We endowed the SNN with a novel artifact rejection mechanism to suppress sharp transients and demonstrate its effectiveness on the ECoG dataset. The HFO rates (median 6.6 HFO/min in pre-resection recordings) detected by this SNN are comparable to those published in the dataset (58 min, 16 recordings). The postsurgical seizure outcome was "predicted" with 100% accuracy for all 8 patients. These results provide a further step towards the construction of a real-time portable battery-operated HFO detection system that can be used during epilepsy surgery to guide the resection of the epileptogenic zone.Comment: 11 pages, 3 figures, 2 tables. The results of this publication were obtained by simulating our hardware platform, built for online processing of biological signals. This hardware combines neural recording headstages with a multi-core neuromorphic processor arxiv.org/abs/2009.1124

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies

A Neuromorphic Device for Detecting High-Frequency Oscillations in Human iEEG

Among diagnostic biomarkers, high frequency oscillations in human iEEG are used to identify epileptogenic brain tissue during epilepsy surgery. However, current methods typically analyse the raw data offline using complex time-consuming algorithms. We developed a compact neuromorphic sensory-processing system-on-chip that can monitor the iEEG signals and detect high frequency oscillations in real-time using spiking neural networks. To this end, we present an integrated device with an analog front-end that can extract predefined spectral features and encode them as address-events, and a neuromorphic processor core that implements a network of integrate and fire neurons with dynamic synapses. The device was fabricated using a standard 0.18μm CMOS technology node. The estimated power consumption of the front-end is 6.2μW /channel and the area-on-chip for a single channel is 0.15 square millimetres. The SNN classifier provides 90.5% sensitivity and 67.7% specificity for detecting high frequency oscillations. This is the first feasibility study towards identifying relevant features in intracranial human data in real-time on-chip using event-base processors

Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks

Interictal high-frequency oscillations (HFO) detected in electroencephalography recordings have been proposed as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. Automatic HFO detectors typically analyze the data offline using complex time-consuming algorithms, which limits their clinical application. Neuromorphic circuits offer the possibility of building compact and low-power processing systems that can analyze data on-line and in real time. In this review, we describe a fully automated detection pipeline for HFO that uses, for the first time, spiking neural networks and neuromorphic technology. We demonstrated that our HFO detection pipeline can be applied to recordings from different modalities (intracranial electroencephalography, electrocorticography, and scalp electroencephalography) and validated its operation in a custom-designed neuromorphic processor. Our HFO detection approach resulted in high accuracy and specificity in the prediction of seizure outcome in patients implanted with intracranial electroencephalography and electrocorticography, and in the prediction of epilepsy severity in patients recorded with scalp electroencephalography. Our research provides a further step toward the real-time detection of HFO using compact and low-power neuromorphic devices. The real-time detection of HFO in the operation room may improve the seizure outcome of epilepsy surgery, while the use of our neuromorphic processor for non-invasive therapy monitoring might allow for more effective medication strategies to achieve seizure control. Therefore, this work has the potential to improve the quality of life in patients with epilepsy by improving epilepsy diagnostics and treatment

A spiking neural network (SNN) for detecting high frequency oscillations (HFOs) in the intraoperative ECoG

To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative electrocorticography (ECoG) recordings, high frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. However, automatic detection of HFOs in real-time remains an open challenge. Here we present a spiking neural network (SNN) for automatic HFO detection that is optimally suited for neuromorphic hardware implementation. We trained the SNN to detect HFO signals measured from intraoperative ECoG on-line, using an independently labeled dataset (58 min, 16 recordings). We targeted the detection of HFOs in the fast ripple frequency range (250-500 Hz) and compared the network results with the labeled HFO data. We endowed the SNN with a novel artifact rejection mechanism to suppress sharp transients and demonstrate its effectiveness on the ECoG dataset. The HFO rates (median 6.6 HFO/min in pre-resection recordings) detected by this SNN are comparable to those published in the dataset (Spearman’s = 0.81). The postsurgical seizure outcome was “predicted” with 100% (CI [63 100%]) accuracy for all 8 patients. These results provide a further step towards the construction of a real-time portable battery-operated HFO detection system that can be used during epilepsy surgery to guide the resection of the epileptogenic zone

Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks

Interictal high-frequency oscillations (HFO) detected in electroencephalography recordings have been proposed as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. Automatic HFO detectors typically analyze the data offline using complex time-consuming algorithms, which limits their clinical application. Neuromorphic circuits offer the possibility of building compact and low-power processing systems that can analyze data on-line and in real time. In this review, we describe a fully automated detection pipeline for HFO that uses, for the first time, spiking neural networks and neuromorphic technology. We demonstrated that our HFO detection pipeline can be applied to recordings from different modalities (intracranial electroencephalography, electrocorticography, and scalp electroencephalography) and validated its operation in a custom-designed neuromorphic processor. Our HFO detection approach resulted in high accuracy and specificity in the prediction of seizure outcome in patients implanted with intracranial electroencephalography and electrocorticography, and in the prediction of epilepsy severity in patients recorded with scalp electroencephalography. Our research provides a further step toward the real-time detection of HFO using compact and low-power neuromorphic devices. The real-time detection of HFO in the operation room may improve the seizure outcome of epilepsy surgery, while the use of our neuromorphic processor for non-invasive therapy monitoring might allow for more effective medication strategies to achieve seizure control. Therefore, this work has the potential to improve the quality of life in patients with epilepsy by improving epilepsy diagnostics and treatment.ISSN:1662-453XISSN:1662-454

Wireless, miniaturized, semi-implantable electrocorticography microsystem validated in vivo

This paper reports on the design, development, and test of a multi-channel wireless micro-electrocorticography (µECoG) system. The system consists of a semi-implantable, ultra-compact recording unit and an external unit, interfaced through a 2.4 GHz radio frequency data telemetry link with 2 Mbps (partially used) data transfer rate. Encased in a 3D-printed 2.9 cm × 2.9 cm × 2.5 cm cubic package, the semi-implantable recording unit consists of a microelectrode array, a vertically-stacked PCB platform containing off-the-shelf components, and commercially-available small-size 3.7-V, 50 mAh lithium-ion batteries. Two versions of microelectrode array were developed for the recording unit: a rigid 4 × 2 microelectrode array, and a flexible 12 × 6 microelectrode array, 36 of which routed to bonding pads for actual recording. The external unit comprises a transceiver board, a data acquisition board, and a host computer, on which reconstruction of the received signals is performed. After development, assembly, and integration, the system was tested and validated in vivo on anesthetized rats. The system successfully recorded both spontaneous and evoked activities from the brain of the subject.ISSN:2045-232