A Novel Hybrid Classical- Quantum Network to Detect Epileptic Seizures

Sameer, Mustafa; Gupta, Bharat

doi:10.1101/2022.05.18.22275295

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…The 1D CNN layers present in the proposed architecture has covered 4097, and 1024 length of EEG of 23.6, and 5.12 s respectively. The authors have achieved a comparable accuracy but achieved a low precision, recall, and F1 score in comparison to the present work Sameer and Gupta (2022b). proposed a 1D CNN architecture with a hybrid quantum classifier using the University of Bonn dataset.…”

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confidence: 53%

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One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

Handa,

Gupta,

Gupta

et al. 2023

Expert Systems

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Precise monitoring and diagnosis of epilepsy by manual analysis of EEG signals are challenging due to the low doctor‐to‐patient ratio, and shortage of medical resources. To automate this diagnosis in real‐time, EEG based Brain–Computer Interface (BCI) system with integration of artificial intelligence techniques will prove to be propitious. This work proposes an end‐to‐end, one‐dimensional atrous conv‐net‐based architecture for automatic epilepsy diagnosis using EEG signals with a conceptual framework of the EEG‐BCI system for routine monitoring and clinical use. The proposed architecture has a robust backbone of six blocks of atrous convolutional layers activated with exponential linear unit functions. The six blocks are followed by the addition of a long short‐term memory layer for automatic feature extraction and sequential EEG data analysis. The efficacy of the proposed architecture has been verified on three publicly available EEG datasets using various evaluation metrics, feature maps, test set evaluation, and ablation studies. An average training and validation accuracy of 96.16% and 90.80% has been achieved upon multiple runs for the three datasets. Ablation experiments indicate that the addition of each block contributed to increasing 17%–25% accuracy scores during the classification of epileptic and non‐epileptic EEG signals. The real‐time EEG‐BCI has been analyzed using weight optimization of the proposed architecture through the NVIDIA Tensor RT framework on a 40 GB DGX A100 NVIDIA workstation. The proposed architecture has generalized well in comparison with the existing techniques for the three EEG datasets and achieved a low training and validation loss with optimum evaluation metrics. This makes the proposed architecture suitable for future EEG‐BCI system deployment in the automatic diagnosis of epilepsy.

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confidence: 53%

“…Sameer and Gupta (2022b) proposed a 1D CNN architecture with a hybrid quantum classifier using the University of Bonn dataset. The architecture consists of the generic 1D convolutional layer with batch normalization, max pooling, and a fully connected layer.…”

Section: Resultsmentioning

confidence: 99%

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

Handa,

Gupta,

Gupta

et al. 2023

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Using LOSO and 10-fold CVs, the model obtained accuracy of 93.40% and 97.88%, with geometric means of 88.44% and 96.42% respectively. A new approach for feature extraction and classification that involves a 1D CNN model using the hybrid classical-quantum layers was proposed in [118]. In the Bonn EEG dataset for the binary classification task, the proposed model obtained maximum accuracy and specificity of 100% while reducing model complexity with the least learning parameters.…”

Section: Biomedical Signal Datasetsmentioning

confidence: 99%

Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications

Ullah,

Garcia-Zapirain

2024

IEEE Access

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Quantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique properties exhibited by subatomic particles, such as superposition, entanglement, and interference. These quantum phenomena enable quantum computers to operate on an entirely different level, exponentially surpassing the computational capabilities of classical computers. By manipulating qubits and capitalising on their quantum states, QC holds the promise of solving complex problems that are currently intractable in the case of traditional computers. The potential impact of QC extends beyond its computational power and reaches into various critical sectors, including healthcare. Scientists and engineers are working diligently to overcome various challenges and limitations associated with QC technology. These include issues related to qubit stability, error correction, scalability, and noise reduction. In such a scenario, our proposed work provides a concise summary of the most recent state of the art based on articles published between 2018 and 2023 in the healthcare domain. Additionally, the approach follows the necessary guidelines for conducting a systematic literature review. This includes utilising research questions and evaluating the quality of the articles using specific metrics. Initially, a total of 2,038 records were acquired from multiple databases, with 468 duplicate records and 1,053 records unrelated to healthcare subsequently excluded. A further 258, 68, and 39 records were eliminated based on title, abstract, and fulltext criteria, respectively. Ultimately, the remaining 49 articles were subject to evaluation, thus providing a brief overview of the recent literature and contributing to existing knowledge and comprehension of Quantum Machine Learning (QML) algorithms and their applications in the healthcare sector. This analysis establishes a foundational framework for forthcoming research and development at the intersection of QC and machine learning, ultimately paving the way for innovative approaches to addressing complex challenges within the healthcare domain.INDEX TERMS Quantum computing, quantum machine learning algorithms, a systematic review, healthcare.

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1D Quantum Convolutional Neural Network for Time Series Forecasting and Classification

Rivera-Ruiz,

Juárez-Osorio,

Mendez-Vazquez

et al. 2023

Lecture Notes in Computer Science

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A Novel Hybrid Classical- Quantum Network to Detect Epileptic Seizures

Cited by 6 publications

References 32 publications

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications

1D Quantum Convolutional Neural Network for Time Series Forecasting and Classification

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