Automated Detection of Alzheimer’s Disease using raw EEG time series via. DWT-CNN model

Mesut Şeker; Mehmet Siraç Özerdem

Makale detayları

Automated Detection of Alzheimer’s Disease using raw EEG time series via. DWT-CNN model

Yazarlar:

Mesut Şeker | Mehmet Siraç Özerdem

Yayıncı:

Dicle University

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.13 Issue 4, pp.673-684

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001281812

pISSN:

1309-8640

Anahtar kelimeler:

Özet:

 Dementia is an age-related neurological disease and gives rise to profound cognitive decline in patients life. Alzheimers Disease AD is the progression of dementia and AD patients generally have memory loss and behavioral disorders. It is possible to determine the stage of dementia by developing automated systems via. signals obtained from patients. EEG is a popular brain monitoring system due to its cost effective, non-invasive implementation, and higher time resolution. In current study, we include participants of 24 HC 12 eyes open EO, 12 eyes closed EC, and 24 AD HC 12 eyes open EO, 12 eyes closed EC. The aim of current study is to design a practical AD detection tool for ADHC participants with a model called DWT-CNN. We performed Discrete Wavelet Transform DWT to extract EEG sub-bands. A Conv2D architecture is applied to raw samples of related EEG sub-bands. According to obtained performance metrics calculated from confusion matrices, all AD and HC time series are correctly classified for alpha band and full band range under both EO and EC. Classification rate of AD vs. HC increases under EO state in all cases even if EC is commonly preferred in other studies. We will add MCI patients with equal size and similar demographics and repeat the experimental steps to develop early alert system in future studies. Adding more participants will also increase generalization ability of method. It is also promising study to combine EEG with different modalities 2D TF image conversion, or MRI in a multimodal approach.

 Dementia is an age-related neurological disease and gives rise to profound cognitive decline in patients life. Alzheimers Disease AD is the progression of dementia and AD patients generally have memory loss and behavioral disorders. It is possible to determine the stage of dementia by developing automated systems via. signals obtained from patients. EEG is a popular brain monitoring system due to its cost effective, non-invasive implementation, and higher time resolution. In current study, we include participants of 24 HC 12 eyes open EO, 12 eyes closed EC, and 24 AD HC 12 eyes open EO, 12 eyes closed EC. The aim of current study is to design a practical AD detection tool for ADHC participants with a model called DWT-CNN. We performed Discrete Wavelet Transform DWT to extract EEG sub-bands. A Conv2D architecture is applied to raw samples of related EEG sub-bands. According to obtained performance metrics calculated from confusion matrices, all AD and HC time series are correctly classified for alpha band and full band range under both EO and EC. Classification rate of AD vs. HC increases under EO state in all cases even if EC is commonly preferred in other studies. We will add MCI patients with equal size and similar demographics and repeat the experimental steps to develop early alert system in future studies. Adding more participants will also increase generalization ability of method. It is also promising study to combine EEG with different modalities 2D TF image conversion, or MRI in a multimodal approach.

 Dementia is an age-related neurological disease and gives rise to profound cognitive decline in patients life. Alzheimers Disease AD is the progression of dementia and AD patients generally have memory loss and behavioral disorders. It is possible to determine the stage of dementia by developing automated systems via. signals obtained from patients. EEG is a popular brain monitoring system due to its cost effective, non-invasive implementation, and higher time resolution. In current study, we include participants of 24 HC 12 eyes open EO, 12 eyes closed EC, and 24 AD HC 12 eyes open EO, 12 eyes closed EC. The aim of current study is to design a practical AD detection tool for ADHC participants with a model called DWT-CNN. We performed Discrete Wavelet Transform DWT to extract EEG sub-bands. A Conv2D architecture is applied to raw samples of related EEG sub-bands. According to obtained performance metrics calculated from confusion matrices, all AD and HC time series are correctly classified for alpha band and full band range under both EO and EC. Classification rate of AD vs. HC increases under EO state in all cases even if EC is commonly preferred in other studies. We will add MCI patients with equal size and similar demographics and repeat the experimental steps to develop early alert system in future studies. Adding more participants will also increase generalization ability of method. It is also promising study to combine EEG with different modalities 2D TF image conversion, or MRI in a multimodal approach.

 Dementia is an age-related neurological disease and gives rise to profound cognitive decline in patients life. Alzheimers Disease AD is the progression of dementia and AD patients generally have memory loss and behavioral disorders. It is possible to determine the stage of dementia by developing automated systems via. signals obtained from patients. EEG is a popular brain monitoring system due to its cost effective, non-invasive implementation, and higher time resolution. In current study, we include participants of 24 HC 12 eyes open EO, 12 eyes closed EC, and 24 AD HC 12 eyes open EO, 12 eyes closed EC. The aim of current study is to design a practical AD detection tool for ADHC participants with a model called DWT-CNN. We performed Discrete Wavelet Transform DWT to extract EEG sub-bands. A Conv2D architecture is applied to raw samples of related EEG sub-bands. According to obtained performance metrics calculated from confusion matrices, all AD and HC time series are correctly classified for alpha band and full band range under both EO and EC. Classification rate of AD vs. HC increases under EO state in all cases even if EC is commonly preferred in other studies. We will add MCI patients with equal size and similar demographics and repeat the experimental steps to develop early alert system in future studies. Adding more participants will also increase generalization ability of method. It is also promising study to combine EEG with different modalities 2D TF image conversion, or MRI in a multimodal approach.

Makale detayları

Automated Detection of Alzheimer’s Disease using raw EEG time series via. DWT-CNN model

Yazarlar:

Mesut Şeker | Mehmet Siraç Özerdem

Yayıncı:

Dicle University

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.13 Issue 4, pp.673-684

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001281812

pISSN:

1309-8640

Deep Learning

Alzheimer's Disease

CNN

EEG

Disease Detection

Deep Learning

Alzheimer's Disease

CNN

EEG

Disease Detection

Sayı

Makale detayları

Yazarlar: Mesut Şeker | Mehmet Siraç Özerdem

Yayıncı: Dicle University

Dergi: Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.13 Issue 4, pp.673-684

Belge Tipi: Makale

Yayın Tarihi: 3/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001281812

pISSN: 1309-8640

Sayı

Yazarlar:

Mesut Şeker | Mehmet Siraç Özerdem

Yayıncı:

Dicle University

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.13 Issue 4, pp.673-684

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001281812

pISSN:

1309-8640