An emerging diagnostic tool for detecting heart and physiological conditions is heart rate variability (HRV). Copious research continuously discovers relationships between heart rate variability metrics and physiological functions and cardiac health. The first step in calculating HRV metrics is calculating heart rate. Heart rate is typically calculated using the interval between R peaks in an EKG signal. Consequently, heart rate measurements rely on the presence of distinctive R peaks and the accurate detection of them. The study is motivated by the drawbacks associated with using R peaks to calculate instantaneous heart rate. In this study we present an alternative method (that does not rely on R peaks) based on the concept of instantaneous frequency to estimate heart rate from electrocardiogram (EKG) signals. The EKG signal is filtered to extract constituent frequency components that correlate with the instantaneous heart rate. The filtered signal is then fed into an algorithm that outputs a signal that shows the variation of the instantaneous heart rate with time. This output signal contains noise due to the behavior of the algorithm at zero crossings of the filtered EKG signal. Two methods for filtering the output signal are also presented in the study. The proposed method was able to successfully estimate the instantaneous heart rate and allowed the subsequent calculation of frequency domain HRV metrics. This method potentially provides more information for HRV analysis and addresses the drawbacks associated with methods based on R peak detection. / Master of Science / Heart disease is the leading cause of death in America accounting for about 20% of all deaths. Consequently, both the public and the medical community are engrossed in cardiovascular health, research that enables early detection of heart disease and novel treatments for cardiac conditions. An emerging diagnostic tool for detecting heart and physiological conditions is heart rate variability (HRV). Copious research continuously discovers relationships between heart rate variability metrics and physiological functions and cardiac health. The first step in calculating HRV metrics is calculating heart rate. With the rise in popularity and improvement of wearable technologies, it has become easier than ever to collect data and perform diagnostics, often in real time. As such the need for robust methods and algorithms to perform these calculations are ever more important. The study is motivated by drawbacks associated with the conventional method used to calculate heart rate from electrocardiogram signals. In this study we present a more robust method to calculate heart rate from EKG signals allowing more accurate HRV metrics to be calculated. In this study we present an alternative method based on the concept of instantaneous frequency to estimate heart rate from electrocardiogram (EKG) signals. We identify the shortcomings of the conventional method of estimating heart rate and discuss the strengths and weaknesses of the alternative method introduced. We then calculate and compare the HRV metrics calculated from the proposed method and the conventional method. The method presented also has the potential to be used on other signals that measure the heart's activity such as Photoplethysmography signals (PPG) allowing it to be used on wearable devices. We hope that the information provided, and the findings presented in this study will be utilized by the medical community and researchers for future research related to heart rate variability.