Abstract
A classification system to detect congestive heart failure (CHF) patients from normal (N) patients is described. The classification procedure uses the k-nearest neighbor algorithm and uses features from the second-order difference plot (SODP) obtained from Holter monitor cardiac RR intervals. The classification system which employs a statistical procedure to obtain the final result gave a success rate of 100% to distinguish CHF patients from normal patients. For this study the Holter monitor data of 36 normal and 36 CHF patients were used. The classification system using standard deviation of RR intervals also performed well, although it did not match the 100% success rate using the features from SODP. However, the success rate for classification using this procedure for SDRR was many fold higher compared to using a threshold. The classification system in this paper will be a valuable asset to the clinician, in the detection congestive heart failure.
Highlights
The need to reach remote, underserved communities with life saving health care is an important area that warrants attention
In the system that is being studied, these RR intervals are used in the construction of a second-order difference plot (SODP), whose features are used as input in the classification algorithm
In addition the paper looks at the results that are obtained using the standard deviation of RR intervals (SDRR) in the classification algorithm
Summary
The need to reach remote, underserved communities with life saving health care is an important area that warrants attention. Fast electronic communication and reliable automated classification systems will enhance this area of health care. In particular when cardiologists serving remote areas are few, reliable automated classification systems will free offsite cardiologists from routine visual analysis of electrocardiogram (ECG) data and provide valuable specialized treatment to patients in remote areas from more experienced cardiologists elsewhere via electronic communication. The analysis and classification of large amount Holter monitor data is an aspect which is amenable to reliable automation. The patient measurements that are used for this automated analysis are the Holter monitor RR interval data. In addition the paper looks at the results that are obtained using the standard deviation of RR intervals (SDRR) in the classification algorithm
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