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<p>Parkinson’s Disease (PD) is a neurological disorder characterized by the gradual degradation of dopamine. It is most prevalent after Alzheimer’s disease particularly seen in old age people. Abnormalities in speech signals have been identified as an indicator of PD. This study introduces a unique method for detecting PD by analyzing speech data using the Ramanujan Fourier Transform (RFT). The projection of the acquired numerical series into a set of fundamental functions composed of Ramanujan sums (RS) is the foundation for the RFT. In this work, RFT-based features are proposed for the diagnosis of PD utilizing speech signals. The proposed features are evaluated using sustained vowels and isolated words from the PC-GITA database. The light gradient boosting machine (LGBM) achieves a maximum classification accuracy of 95% for /apto/. Additionally, the developed models are assessed using an independent dataset. The results obtained from this independent data set indicate that the accuracy of the isolated words is 71%. The results obtained from this research work demonstrate that the proposed features using RFTs are more appropriate and accurate for the evaluation of PD.</p>

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Parkinson’s disease identification from speech signal using Ramanujan Fourier transform

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Sudhansu Sekhar Nayak,
  • Anand D. Darji,
  • Prashant K. Shah,
  • Juan Rafael Orozco-Arroyave

摘要

Parkinson’s Disease (PD) is a neurological disorder characterized by the gradual degradation of dopamine. It is most prevalent after Alzheimer’s disease particularly seen in old age people. Abnormalities in speech signals have been identified as an indicator of PD. This study introduces a unique method for detecting PD by analyzing speech data using the Ramanujan Fourier Transform (RFT). The projection of the acquired numerical series into a set of fundamental functions composed of Ramanujan sums (RS) is the foundation for the RFT. In this work, RFT-based features are proposed for the diagnosis of PD utilizing speech signals. The proposed features are evaluated using sustained vowels and isolated words from the PC-GITA database. The light gradient boosting machine (LGBM) achieves a maximum classification accuracy of 95% for /apto/. Additionally, the developed models are assessed using an independent dataset. The results obtained from this independent data set indicate that the accuracy of the isolated words is 71%. The results obtained from this research work demonstrate that the proposed features using RFTs are more appropriate and accurate for the evaluation of PD.