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.
Topics Covered
The many academic areas covered in this publication include, but are not limited to:
•Benchmark Functions
•Common Heart Disorders
•Gravitational Search Algorithm
•Heart Waveforms
•Median Filter
•Noise Removal
•Particle Swarm Optimization
•Unimodal High-Dimensional Functions
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.
Topics Covered
The many academic areas covered in this publication include, but are not limited to:
•Benchmark Functions
•Common Heart Disorders
•Gravitational Search Algorithm
•Heart Waveforms
•Median Filter
•Noise Removal
•Particle Swarm Optimization
•Unimodal High-Dimensional Functions