Research Analysis on Current Advances in Parkinson’s Disease Detection Using Signal Processing and Machine Learning-Based Techniques
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3292-0_8
Reference24 articles.
1. Li J, Guo Y (2023) EEG detection and prediction of freezing of gait in Parkinson’s disease based on spatiotemporal coherent modes. https://doi.org/10.36227/TECHRXIV.21829530.V1
2. Shah D, Gopika GK, Sinha N (2022) Analysis of EEG for Parkinson’s disease detection. In: SPCOM 2022—IEEE international conference on signal processing and communications. https://doi.org/10.1109/SPCOM55316.2022.9840776
3. Oh SL et al (2020) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput Appl 32(15):10927–10933. https://doi.org/10.1007/S00521-018-3689-5
4. Li K, Ao B, Wu X, Wen Q, Ul Haq E, Yin J (2023) Parkinson’s disease detection and classification using EEG based on deep CNN-LSTM model. https://doi.org/10.1080/02648725.2023.2200333
5. Zhang W et al (2022) Multimodal data for the detection of freezing of gait in Parkinson’s disease. Sci Data 9(1):1–10. https://doi.org/10.1038/s41597-022-01713-8
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