Anomaly Detection Technique Based on Sympathetic Nerve Activity for Detection of Cardiac Arrhythmia

摘要

In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) by using state-of-theart signal conditioning and machine learning methods is developed to perform robust detection of cardiac arrhythmia (CA). For this purpose, a signal conditioning technique which obtains SKNA and ECG from wideband recordings on IUC-SKNA database is developed. By using preprocessed data, a novel feature extraction technique which obtains SKNA features that are critical for reliable detection of CA is developed. By using extracted features, a supervised learning technique based on artificial neural network (ANN) and an unsupervised learning technique based on Gaussian mixture model (GMM) are developed to perform robust detection of SKNA anomalies. A Neyman-Pearson type of approach is developed to perform robust detection of outliers that correspond to CA. The performance results of proposed technique over IUC-SKNA database showed that technique provides highly reliable detection of CA by performing robust detection of SKNA anomalies. Therefore, in cases where diagnostic information of ECG is not sufficient for reliable diagnosis of CA, proposed technique can provide early and accurate diagnosis of the disease, which can lead to a significant reduction in mortality rates of cardiovascular diseases.