Electrocardiogram Signal Classification and Machine Learning

Emerging Research and Opportunities

Nonfiction, Computers, Advanced Computing, Theory, Health & Well Being, Medical, Specialties, Internal Medicine, Cardiology
Cover of the book Electrocardiogram Signal Classification and Machine Learning by Sara Moein, IGI Global
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Author: Sara Moein ISBN: 9781522555827
Publisher: IGI Global Publication: May 25, 2018
Imprint: Medical Information Science Reference Language: English
Author: Sara Moein
ISBN: 9781522555827
Publisher: IGI Global
Publication: May 25, 2018
Imprint: Medical Information Science Reference
Language: English

Technological tools and computational techniques have enhanced the healthcare industry. These advancements have led to significant progress in the diagnosis of heart disorders. Electrocardiogram Signal Classification and Machine Learning: Emerging Research and Opportunities is a critical scholarly resource that examines the importance of automatic normalization and classification of electrocardiogram (ECG) signals of heart disorders. Featuring a wide range of topics such as common heart disorders, particle swarm optimization, and benchmarks functions, this publication is geared toward medical professionals, researchers, professionals, and students seeking current and relevant research on the categorization of ECG signals.

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Technological tools and computational techniques have enhanced the healthcare industry. These advancements have led to significant progress in the diagnosis of heart disorders. Electrocardiogram Signal Classification and Machine Learning: Emerging Research and Opportunities is a critical scholarly resource that examines the importance of automatic normalization and classification of electrocardiogram (ECG) signals of heart disorders. Featuring a wide range of topics such as common heart disorders, particle swarm optimization, and benchmarks functions, this publication is geared toward medical professionals, researchers, professionals, and students seeking current and relevant research on the categorization of ECG signals.

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