An automatic ST segment deviation detection technique using KLT and an ensemble neural classifier achieved a sensitivity of 90.75% and a positive predictive value of 89.2% for diagnosing ischemia.
Does an ensemble neural classifier using KLT accurately detect ST segment deviations in ambulatory ECG recordings?
A novel algorithm using KLT and an ensemble neural classifier demonstrates high sensitivity and positive predictive value for automated ST segment deviation detection in ambulatory ECGs.
Effect estimate: PPV 89.2%
In this paper, we describe a technique for automatic detection of ST segment deviations that can be used in the diagnosis of coronary heart disease (CHD) using ambulatory electrocardiogram (ECG) recordings. Preprocessing is carried out prior to the extraction of the ST segment which involves noise and artifact filtering using a digital bandpass filter, baseline removal and application of a discrete wavelet transform (DWT) based technique for detection and delineation of the QRS complex in ECG. Lead-dependent Karhunen-Loève transform (KLT) bases are used for dimensionality reduction of the ST segment data. ST deviation episodes are detected by a classifier ensemble comprising backpropagation neural networks. Results obtained through the use of our proposed method (sensitivity/positive predictive value = 90.75%/89.2%) compare well with those given in the existing research. Hence, the proposed method exhibits the potential to be adopted in the design of a practical ischemia detection system.
Afsar et al. (Wed,) conducted a other in Coronary heart disease (CHD). Automatic detection of ST segment deviations using KLT and ensemble neural classifier vs. Existing research methods was evaluated on Sensitivity and positive predictive value for ST deviation episodes (PPV 89.2%). An automatic ST segment deviation detection technique using KLT and an ensemble neural classifier achieved a sensitivity of 90.75% and a positive predictive value of 89.2% for diagnosing ischemia.