A Novel Approach to Classify Significant ECG Data Based on Heart Instantaneous Frequency and ECG-derived Respiration using Conductive Textiles

Our study focuses on classifying as significant electrocardiogram (ECG) data from home healthcare system. Generally, spectral analysis of RR interval (RRI) time series is used to determine periodic component of heart rate variability (HRV). It is well known, moreover, that low frequency (LF) component is associated with heart rate variability regulation, and high frequency (HF) component is referred to respiration as respiration sinus arrhythmia (RSA) in the HRV power spectra. In many cases, however, LF and HF components may be entirely superimposed on each other and, therefore, the method by division of power spectra range can not be evaluated diagnostically. We propose another approach to interpret well better than before. The method which we suggest is that it finds high correlative data using frequency analysis comparison heart instantaneous frequency (HIF) based on extracting the instantaneous fundamental frequency with EDR. The reason which we use HIF is that it is simpler and more powerful against noise than HRV. First of all, we show the EDR extraction process, and then prove that HIF signal is useful or not through comparison with HRV. Finally, we classify significant signal data through comparison high frequency (HF) component obtained frequency analysis of HIF with that of EDR.