A New Neuromorphic Computing Approach for Epileptic Seizure Prediction

Tian, Fengshi; Yang, Jie; Zhao, Shiqi

doi:10.1109/iscas51556.2021.9401560

Cited by 16 publications

(5 citation statements)

References 28 publications

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“…However, the interictal intervals contained in CHB-MIT dataset are much more than preictal intervals causing the problem of sample imbalance in training, which may lead to poor performances ( Japkowicz and Stephen, 2002 ; Barandela et al, 2004 ). To overcome this barrier, samples of preictal intervals are extracted using 5 s overlapping ( Tian et al, 2021 ).…”

Section: Experiments and Evaluation Resultsmentioning

confidence: 99%

NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications

2023

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Highly accurate classification methods for multi-task biomedical signal processing are reported, including neural networks. However, reported works are computationally expensive and power-hungry. Such bottlenecks make it hard to deploy existing approaches on edge platforms such as mobile and wearable devices. Gaining motivation from the good performance and high energy-efficiency of spiking neural networks (SNNs), a generic neuromorphic framework for edge healthcare and biomedical applications are proposed and evaluated on various tasks, including electroencephalography (EEG) based epileptic seizure prediction, electrocardiography (ECG) based arrhythmia detection, and electromyography (EMG) based hand gesture recognition. This approach, NeuroCARE, uses a unique sparse spike encoder to generate spike sequences from raw biomedical signals and makes classifications using the spike-based computing engine that combines the advantages of both CNN and SNN. An adaptive weight mapping method specifically co-designed with the spike encoder can efficiently convert CNN to SNN without performance deterioration. The evaluation results show that the overall performance, including the classification accuracy, sensitivity and F1 score, achieve 92.7, 96.7, and 85.7% for seizure prediction, arrhythmia detection and hand gesture recognition, respectively. In comparison with CNN topologies, the computation complexity is reduced by over 80.7% while the energy consumption and area occupation are reduced by over 80% and over 64.8%, respectively, indicating that the proposed neuromorphic computing approach is energy and area efficient and of high precision, which paves the way for deployment at edge platforms.

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Section: Experiments and Evaluation Resultsmentioning

confidence: 99%

NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications

2023

Self Cite

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“…Researchers have used intelligent neuromorphic paradigms to develop real-time detection for epileptic seizures using neuromorphic technology. There is a growing literature that uses typical brainrelated biosignals to separate, identify, and even classify seizure-related markers [106][107][108][109]. We hereby include some representative examples to illustrate the capabilities and potential of these systems.…”

Section: Primary Cortexmentioning

confidence: 99%

Neuromorphic applications in medicine

et al. 2023

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In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.

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“…Subsequently, it exploits three-layered customized CNN to extract a comprehensive feature set and SVM, CNN, and LSTM in order to enable ensemble classifiers using Model-Agnostic Meta-Learning (MAML) to differentiate between preictal and interictal states. A new neuromorphic computing approach has been reported [ 30 ] in which the Gaussian random discrete encoder is employed to create spike sequences for the input EEG data. The combination of the energy-efficient SNN and CNN is able to perform seizure prediction by leveraging the potential advantages of each model.…”

Section: Literature Reviewmentioning

confidence: 99%

Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning

Saemaldahr

Ilyas

2023

Sensors

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Electroencephalography (EEG) signals are the primary source for discriminating the preictal from the interictal stage, enabling early warnings before the seizure onset. Epileptic siezure prediction systems face significant challenges due to data scarcity, diversity, and privacy. This paper proposes a three-tier architecture for epileptic seizure prediction associated with the Federated Learning (FL) model, which is able to achieve enhanced capability by utilizing a significant number of seizure patterns from globally distributed patients while maintaining data privacy. The determination of the preictal state is influenced by global and local model-assisted decision making by modeling the two-level edge layer. The Spiking Encoder (SE), integrated with the Graph Convolutional Neural Network (Spiking-GCNN), works as the local model trained using a bi-timescale approach. Each local model utilizes the aggregated seizure knowledge obtained from the different medical centers through FL and determines the preictal probability in the coarse-grained personalization. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized in fine-grained personalization to recognize epileptic seizure patients by examining the outcomes of the FL model, heart rate variability features, and patient-specific clinical features. Thus, the proposed approach achieved 96.33% sensitivity and 96.14% specificity when tested on the CHB-MIT EEG dataset when modeling was performed using the bi-timescale approach and Spiking-GCNN-based epileptic pattern learning. Moreover, the adoption of federated learning greatly assists the proposed system, yielding a 96.28% higher accuracy as a result of addressing data scarcity.

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A New Neuromorphic Computing Approach for Epileptic Seizure Prediction

Cited by 16 publications

References 28 publications

NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications

NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications

Neuromorphic applications in medicine

Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning

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