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.

Baevsky's Stress Index as a Sensitive Indicator for Biofeedback Efficacy in Medical Students: a Pilot Study.

Medical students often encounter stress, impacting their mental health and academic achievements. Biofeedback is an efficient stress management tool, but its efficacy in medical students is still poorly studied. A key biofeedback metric is heart rate variability (HRV), which detects stress through changes in autonomic nervous system activity.

EmoNet: Deep Learning-based Emotion Climate Recognition Using Peers' Conversational Speech, Affect Dynamics, and Physiological Data.

Understanding the emotional dynamics within social interactions is crucial for meaningful interpretation. Despite progress in emotion recognition systems, recognizing the collective emotional climate among peers has been understudied. Addressing this gap, we propose EmoNet, an AI model transcending traditional emotion identification.

Investigating the effects of beta-blockers on circadian heart rhythm using heart rate variability in ischemic heart disease with preserved ejection fraction.

Heart failure is characterized by sympathetic activation and parasympathetic withdrawal leading to an abnormal autonomic modulation. Beta-blockers (BB) inhibit overstimulation of the sympathetic system and are indicated in heart failure patients with reduced ejection fraction. However, the effect of beta-blocker therapy on heart failure with preserved ejection fraction (HFpEF) is unclear.

A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis.

Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG.