||Via the delivery of blood, heart transfers oxygen and nutrients to various organs and is thus a highly influential for circulatory system. To adapt to the variation of physiological conditions, the intensity and frequency of heart beats change with time. Careful observation finds that the time intervals between heartbeats are often different even if the body is at rest. Such heart rate variability (HRV) has been used to estimate the activity of the autonomic nervous system which can be divided into sympathetic and parasympathetic subsystems both of which can significantly affect the physiology of the human body. As a result, HRV has been used as a physiological indicator to assist doctors in making diagnostic decisions.|
Many studies have used HRV to analyze the ECG signal via studying the QRS complex waveform to determine the time intervals between R-peaks and analyze the R-R intervals from time and frequency domains. Different from the conventional R-R Interval based approach, this work introduces new HRV feature variables by computing spectrogram of the ECG signal waveform. In particular, based on the harmonics of the spectrum, we introduce the concepts of modes. By find the relative amount of energy associated with each mode and degree-of-energy-concentration associated with each mode, this work introduces two sets of new HRV features. In addition, we also investigate how these variables change with time and the correlations between these features.
To demonstrate the potential of the proposed features, the differences of the values of the proposed features are compared for healthy individuals versus OSA patients, young versus old and male versus female. The experimental results show the differences between many of the tested features are statistically significant.