Research ArticleNo Access

Detection of Alzheimer’s Dementia by Using Signal Decomposition and Machine Learning Methods

Ozlem Karabiber Cura

Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli, 35620 Izmir, Turkey

E-mail Address: [email protected]

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Aydin Akan

Department of Electrical and Electronics Engineering, Izmir University of Economics, Balcova, 35330 Izmir, Turkey

E-mail Address: [email protected]

Corresponding author.

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Gulce Cosku Yilmaz

Department of Neurology, Faculty of Medicine, Izmir Katip Celebi University, Cigli, 35620 Izmir, Turkey

E-mail Address: [email protected]

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, and

Hatice Sabiha Ture

Department of Neurology, Faculty of Medicine, Izmir Katip Celebi University, Cigli, 35620 Izmir, Turkey

E-mail Address: [email protected]

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https://doi.org/10.1142/S0129065722500423Cited by:16 (Source: Crossref)

Abstract

Dementia is one of the most common neurological disorders causing defection of cognitive functions, and seriously affects the quality of life. In this study, various methods have been proposed for the detection and follow-up of Alzheimer’s dementia (AD) with advanced signal processing methods by using electroencephalography (EEG) signals. Signal decomposition-based approaches such as empirical mode decomposition (EMD), ensemble EMD (EEMD), and discrete wavelet transform (DWT) are presented to classify EEG segments of control subjects (CSs) and AD patients. Intrinsic mode functions (IMFs) are obtained from the signals using the EMD and EEMD methods, and the IMFs showing the most significant differences between the two groups are selected by applying previously suggested selection procedures. Five-time-domain and 5-spectral-domain features are calculated using selected IMFs, and five detail and approximation coefficients of DWT. Signal decomposition processes are conducted for both 1 min and 5 s EEG segment durations. For the 1 min segment duration, all the proposed approaches yield prominent classification performances. While the highest classification accuracies are obtained using EMD (91.8%) and EEMD (94.1%) approaches from the temporal/right brain cluster, the highest classification accuracy for the DWT (95.2%) approach is obtained from the temporal/left brain cluster for 1 min segment duration.

Keywords:

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