Identification of mental stress granularity from long-term ECG recordings using novel complexity analysis.

Mental health conditions often manifest as changes in physiological signals and are characterized by specific features within these signals. However, there is limited knowledge about the relationship between long-term physiological time series including ECG and psychopathology. In a complex time series, the waiting time distribution of crucial events has an inverse power law probability density function with a temporal complexity index μ (1 < μ < 3).

Crucial events identify early stage of cardiac autonomic neuropathy progression from ECG signals.

Cardiac autonomic neuropathy (CAN) is a condition characterized by neuropathic damage resulting in aberrant regulation of heart rate, and often manifests as changes in the ECG signals characterized by specific features of complexity, such as crucial events. This research explored the relationship between CAN progression and complexity measures involving crucial events, which can be determined using the modified diffusion entropy analysis (MDEA). MDEA measures the scaling index (0.

Multiscale Diffusion Entropy Analysis for the Detection of Crucial Events in Cardiac Pathology.

The significance of crucial events in explaining the dynamics of a physiological system has only been recently emerging. Crucial events are yet to be fully understood and implemented in clinical applications of physiological signal processing. This paper proposes the application of modified diffusion entropy (MDEA) and novel multiscale diffusion entropy analyses (MSDEA) on measuring the temporal complexity of the ECG time series to improve crucial events detection performance.