Machine learning for supporting diagnosis of amyotrophic lateral sclerosis using surface electromyogram

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 22, Issue 1, 2014, Pages 96-103

  Zhang, Xu ^a,b   Barkhaus, Paul E ^c   Rymer, William Zev ^a,d   Zhou, Ping ^a,b,d  

^a REHABILITATION INSTITUTE OF CHICAGO   (United States)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^c CLEMENT J ZABLOCKI VETERANS AFFAIRS MEDICAL CENTER   (United States)

^d NORTHWESTERN UNIVERSITY   (United States)

Author keywords

Amyotrophic lateral sclerosis (ALS); clustering index; kurtosis; machine learning; surface electromyogram (EMG)

Indexed keywords

AMYOTROPHIC LATERAL SCLEROSIS; CLUSTERING INDEX; DIAGNOSTIC MARKERS; KURTOSIS; LINEAR DISCRIMINANT ANALYSIS; NEUROMUSCULAR DISEASE; SURFACE ELECTROMYOGRAM; VOLUNTARY MUSCLE CONTRACTION;

HIGHER ORDER STATISTICS; LEARNING SYSTEMS; NEEDLES; NEUROPHYSIOLOGY;

NEURODEGENERATIVE DISEASES;

AGED; ALGORITHM; AMYOTROPHIC LATERAL SCLEROSIS; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; ELECTROMYOGRAPHY; FEMALE; HUMAN; MACHINE LEARNING; MALE; MIDDLE AGED; MUSCLE CONTRACTION; PATHOPHYSIOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SKELETAL MUSCLE;

AGED; ALGORITHMS; AMYOTROPHIC LATERAL SCLEROSIS; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROMYOGRAPHY; FEMALE; HUMANS; MACHINE LEARNING; MALE; MIDDLE AGED; MUSCLE CONTRACTION; MUSCLE, SKELETAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84892404505     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2013.2274658     Document Type: Article

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