Modified Distribution Entropy as a Complexity Measure of Heart Rate Variability (HRV) Signal

• Main Authors: Radhagayathri Udhayakumar, Chandan Karmakar, Peng Li, Xinpei Wang, Marimuthu Palaniswami
• Format: Article
• Language: English
• Published: MDPI AG 2020-09-01
• Series: Entropy
• Subjects: distribution entropy; complexity analysis; heart rate variability; Shannon entropy
• Online Access: https://www.mdpi.com/1099-4300/22/10/1077

The complexity of a heart rate variability (HRV) signal is considered an important nonlinear feature to detect cardiac abnormalities. This work aims at explaining the physiological meaning of a recently developed complexity measurement method, namely, distribution entropy (<inline-formula><math display="inline"><semantics><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mi>E</mi><mi>n</mi></mrow></semantics></math></inline-formula>), in the context of HRV signal analysis. We thereby propose modified distribution entropy (<inline-formula><math display="inline"><semantics><mrow><mi>m</mi><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mi>E</mi><mi>n</mi></mrow></semantics></math></inline-formula>) to remove the physiological discrepancy involved in the computation of <inline-formula><math display="inline"><semantics><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mi>E</mi><mi>n</mi></mrow></semantics></math></inline-formula>. The proposed method generates a distance matrix that is devoid of over-exerted multi-lag signal changes. Restricted element selection in the distance matrix makes “<inline-formula><math display="inline"><semantics><mrow><mi>m</mi><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mi>E</mi><mi>n</mi></mrow></semantics></math></inline-formula>” a computationally inexpensive and physiologically more relevant complexity measure in comparison to <inline-formula><math display="inline"><semantics><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mi>E</mi><mi>n</mi></mrow></semantics></math></inline-formula>.