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A Non-invasive Approach for Early Alzheimer’s Detection Through Spontaneous Speech Analysis Using Deep Visibility Graphs

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Abstract

Identifying Alzheimer’s disease (AD) in its early stages is a challenging task for physicians and clinicians. This paper proposes a new algorithm for diagnosing AD, which is based on analyzing spontaneous speech signals. The proposed method uses two visibility graph methods, Natural Visibility Graph (NVG) and Horizontal Visibility Graph (HVG), to derive features from speech windows. These features are then given to a deep BiLSTM-based classifier to decide about segments of the signal. The proposed approach could obtain a sensitivity of 98.33%, specificity of 99.44%, and accuracy of 99.17%. The advantage of converting speech signals into graphs using NVG and HVG is that it allows for the extraction of complex structural features that are not easily captured by traditional methods. This method is highly beneficial due to its non-invasive nature, low cost, and lack of side effects. Patients can undergo the procedure without experiencing any discomfort, while also benefiting from its affordability and accessibility. The method’s safety and practicality make it an ideal choice for those seeking a reliable and effective solution. Moreover, the proposed algorithm has a high accuracy in detecting the early stage of AD, which makes it a promising tool to evaluate Alzheimer’s disease diagnosis in its pre-clinical stage.

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Data Availability

The data used in this study is explained in the Manuscript.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Shahrood University of Technology, Shahrood, Iran

    Zeynab Mohammadpoory

  2. Department of Electrical Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran

    Mahda Nasrolahzadeh

  3. Department of Computer Engineering, University of Mazandaran, Babolsar, Iran

    Sekineh Asadi Amiri

  4. Department of Biomedical Engineering, Hakim Sabzevari University, Sabzevar, Iran

    Javad Haddadnia

Authors
  1. Zeynab Mohammadpoory
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  2. Mahda Nasrolahzadeh
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  3. Sekineh Asadi Amiri
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  4. Javad Haddadnia
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Contributions

Zeynab Mohammadpoory, Mahda Nasrolahzadeh, Sekineh Asadi Amiri, and Javad Haddadnia conceived of the presented idea. Zeynab Mohammadpoory, Mahda Nasrolahzadeh, Sekineh Asadi Amiri, and Javad Haddadnia carried out the experiments and verified the analytical methods. Zeynab Mohammadpoory, Mahda Nasrolahzadeh, Sekineh Asadi Amiri, and Javad Haddadnia wrote the manuscript. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Zeynab Mohammadpoory.

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Mohammadpoory, Z., Nasrolahzadeh, M., Amiri, S.A. et al. A Non-invasive Approach for Early Alzheimer’s Detection Through Spontaneous Speech Analysis Using Deep Visibility Graphs. Cogn Comput 17, 42 (2025). https://doi.org/10.1007/s12559-024-10398-7

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