Early diagnosis of Parkinson's disease via machine learning on speech data

Hananel Hazan; Dan Hilu; Larry Manevitz; Lorraine O. Ramig; Shimon Sapir

doi:10.1109/EEEI.2012.6377065

Early diagnosis of Parkinson's disease via machine learning on speech data

Hananel Hazan, Dan Hilu, Larry Manevitz, Lorraine O. Ramig, Shimon Sapir

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

54 Scopus citations

Abstract

Using two distinct data sets (from the USA and Germany) of healthy controls and patients with early or mild stages of Parkinson's disease, we show that machine learning tools can be used for the early diagnosis of Parkinson's disease from speech data. This could potentially be applicable before physical symptoms appear. In addition, we show that while the training phase of machine learning process from one country can be reused in the other; different features dominate in each country; presumably because of languages differences. Three results are presented: (i) separate training and testing by each country (close to 85% range); (ii) pooled training and testing (about 80% range) and (iii) cross-country (training in one and testing in the other) (about 75% ranges). We discovered that different feature sets were needed for each country (language).

Original language	English
Title of host publication	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012
DOIs	https://doi.org/10.1109/EEEI.2012.6377065
State	Published - 2012
Externally published	Yes
Event	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012 - Eilat, Israel Duration: 14 Nov 2012 → 17 Nov 2012

Publication series

Name	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012

Conference

Conference	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012
Country/Territory	Israel
City	Eilat
Period	14/11/12 → 17/11/12

Keywords

Classification
Early Diagnosis
Machine Learning
Parkinson Disease
Pattern Matching
SVM
Speech Data

Access to Document

10.1109/EEEI.2012.6377065

Cite this

Hazan, H., Hilu, D., Manevitz, L., Ramig, L. O., & Sapir, S. (2012). Early diagnosis of Parkinson's disease via machine learning on speech data. In 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012 Article 6377065 (2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012). https://doi.org/10.1109/EEEI.2012.6377065

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title = "Early diagnosis of Parkinson's disease via machine learning on speech data",

abstract = "Using two distinct data sets (from the USA and Germany) of healthy controls and patients with early or mild stages of Parkinson's disease, we show that machine learning tools can be used for the early diagnosis of Parkinson's disease from speech data. This could potentially be applicable before physical symptoms appear. In addition, we show that while the training phase of machine learning process from one country can be reused in the other; different features dominate in each country; presumably because of languages differences. Three results are presented: (i) separate training and testing by each country (close to 85% range); (ii) pooled training and testing (about 80% range) and (iii) cross-country (training in one and testing in the other) (about 75% ranges). We discovered that different feature sets were needed for each country (language).",

keywords = "Classification, Early Diagnosis, Machine Learning, Parkinson Disease, Pattern Matching, SVM, Speech Data",

author = "Hananel Hazan and Dan Hilu and Larry Manevitz and Ramig, {Lorraine O.} and Shimon Sapir",

year = "2012",

doi = "10.1109/EEEI.2012.6377065",

language = "English",

isbn = "9781467346801",

series = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012",

booktitle = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012",

note = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012 ; Conference date: 14-11-2012 Through 17-11-2012",

}

Hazan, H, Hilu, D, Manevitz, L, Ramig, LO & Sapir, S 2012, Early diagnosis of Parkinson's disease via machine learning on speech data. in 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012., 6377065, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012, Eilat, Israel, 14/11/12. https://doi.org/10.1109/EEEI.2012.6377065

Early diagnosis of Parkinson's disease via machine learning on speech data. / Hazan, Hananel; Hilu, Dan; Manevitz, Larry et al.
2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012. 2012. 6377065 (2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hazan, Hananel

AU - Hilu, Dan

AU - Manevitz, Larry

AU - Ramig, Lorraine O.

AU - Sapir, Shimon

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N2 - Using two distinct data sets (from the USA and Germany) of healthy controls and patients with early or mild stages of Parkinson's disease, we show that machine learning tools can be used for the early diagnosis of Parkinson's disease from speech data. This could potentially be applicable before physical symptoms appear. In addition, we show that while the training phase of machine learning process from one country can be reused in the other; different features dominate in each country; presumably because of languages differences. Three results are presented: (i) separate training and testing by each country (close to 85% range); (ii) pooled training and testing (about 80% range) and (iii) cross-country (training in one and testing in the other) (about 75% ranges). We discovered that different feature sets were needed for each country (language).

AB - Using two distinct data sets (from the USA and Germany) of healthy controls and patients with early or mild stages of Parkinson's disease, we show that machine learning tools can be used for the early diagnosis of Parkinson's disease from speech data. This could potentially be applicable before physical symptoms appear. In addition, we show that while the training phase of machine learning process from one country can be reused in the other; different features dominate in each country; presumably because of languages differences. Three results are presented: (i) separate training and testing by each country (close to 85% range); (ii) pooled training and testing (about 80% range) and (iii) cross-country (training in one and testing in the other) (about 75% ranges). We discovered that different feature sets were needed for each country (language).

KW - Classification

KW - Early Diagnosis

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KW - Pattern Matching

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ER -

Early diagnosis of Parkinson's disease via machine learning on speech data

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Keywords

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