Epileptic Seizure Detection Using a Neuromorphic-Compatible Deep Spiking Neural Network

Zarrin, Pouya Soltani; Zimmer, Romain; Wenger, Christian; Masquelier, Timothée

doi:10.1007/978-3-030-45385-5_34

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…As a result, the trade-off between mentioned benefits and risks related to securing sensitive medical data is, still, an on-going challenge. To address this concern, lowpower neuromorphic platforms could be integrated into medical devices for locally processing of computations required for ML algorithms [32]. Neuromorphic chips have been successfully implemented in different studies for matrixmultiplications required for non-perceptron and perceptronbased ML methods [33], [34].…”

Section: Resultsmentioning

confidence: 99%

In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools

2020

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Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that reflect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fluids like saliva is a promising approach for staging disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. However, the concurrent consideration of patients' demographic and medical parameters is necessary for achieving accurate outcomes. Therefore, Machine Learning (ML) tools can play an important role for analyzing patient data and providing comprehensive results for the recognition of COPD in a PoC setting. As a result, the objective of this research work was to implement ML tools on data acquired from characterizing saliva samples of COPD patients and healthy controls as well as their demographic information for PoC recognition of the disease. For this purpose, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, ML tools were applied on the acquired data for classification. The XGBoost gradient boosting algorithm provided a high classification accuracy and sensitivity of 91.25% and 100%, respectively, making it a promising model for COPD evaluation. Integration of this model on a neuromorphic chip, in the future, will enable the real-time assessment of COPD in PoC, with low cost, low energy consumption, and high patient privacy. In addition, constant monitoring of COPD in a near-patient setup will enable the better management of the disease exacerbations.

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Section: Resultsmentioning

confidence: 99%

In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools

2020

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“…Moreover, implementation of novel meta learning algorithms, such as few-shot learning, on neuromorphic platforms will enable the rapid adaptation and real-time learning in these systems with a few data points and the least possible computation 39,49 . Example of such applications, where online learning and adaptation of a ML model is crucial, include autonomous driving, surgical robotics, personalized medicine, and precision diagnostic 39,[49][50][51][52] .…”

Section: Discussionmentioning

confidence: 99%

Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices

Zarrin

Zahari

Mahadevaiah

et al. 2020

Sci Rep

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Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD.

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“…In this paper, we propose a new deep SNN that can better capture long-term dependencies between the EEG features when compared with Convolutional SNNs [32]. The code for spiking convLSTM unit (SPCLU)-based networks is openly accessible in the snnTorch Python package.…”

Section: Novelty and Significancementioning

confidence: 99%

“…As an alternative, it is increasingly more common to train SNNs from scratch by using surrogate gradients to overcome the non-differentiability of spikes and has achieved promising results in a variety of signal processing tasks [31]. Inspired by this, the work in [32] developed a neuromorphic-compatible SNN network for seizure prediction task and achieved high performance using iEEG data. As this was tested using a small sample of data, this may result in reporting over-optimistic performance.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic deep spiking neural networks for seizure detection

Yang

Eshraghian

Truong

et al. 2023

Neuromorph. Comput. Eng.

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The vast majority of studies that process and analyze neural signals are conducted on cloud computing resources, which is often necessary for the demanding requirements of Deep Neural Network (DNN) workloads. However, applications such as epileptic seizure detection stand to benefit from edge devices that can securely analyze sensitive medical data in real-time and personalised manner. In this work, we propose a novel neuromorphic computing approach to seizure detection using a surrogate gradient-based deep Spiking Neural Network (SNN), which consists of a novel Spiking ConvLSTM unit (SPCLU). We have trained, validated, and rigorously tested the proposed SNN model across three publicly accessible datasets, including Boston Children’s Hospital–MIT (CHB-MIT) dataset from the U.S., and the Freiburg (FB) and EPILEPSIAE intracranial EEG (iEEG) datasets from Germany. The average leave-one-out cross-validation AUC score for FB, CHB-MIT, and EPILEPSIAE datasets can reach 92.7%, 89.0%, and 81.1%, respectively, while the computational overhead and energy consumption are significantly reduced when compared to alternative state-of-the-art models, showing the potential for building an accurate hardware-friendly, low-power neuromorphic system. This is the first feasibility study using a deep Spiking Neural Network for seizure detection on several reliable public datasets.

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Epileptic Seizure Detection Using a Neuromorphic-Compatible Deep Spiking Neural Network

Cited by 15 publications

References 10 publications

In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools

In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools

Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices

Neuromorphic deep spiking neural networks for seizure detection

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