Adaptive Filtering for Detecting Myocardial Infarction Using Noninvasive Conducting Polymer Composite Sensors

Continuous electrocardiographic (ECG) monitoring using conducting polymer composite sensors (CPS) presents a non-invasive way to detect cardiac irregularities such as myocardial infarction (MI). Electromyography (EMG), which measures muscle activity in the human body, has a frequency range that overlaps that of the ECG wave. As a result, both EMG and ECG data are present when CPSs collect ECG signals. When measuring ECG waves of an individual during motion, we account for EMG by removing the motion artifact from the ECG signal. With the use of a normalized least mean square (NLMS) algorithm and known signal characteristics, we show that EMG noise can be successfully filtered from an ECG signal that is collected using our CPSs in the standard 12 lead ECG placement. Our software produces a diagnostic-friendly ECG signal and then determines the patient’s heart rate. When applied to the arrhythmia database from the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH), our heartbeat detection logic has an accuracy of 99.6% with only 199 false beats and 240 missed beats out of 109,494 total heartbeats taken from 48 individual recordings